CIMFuels Editorials

CLIFFTENT Inc.: Process Control, Optimization, Scheduling, Performance
Dr. Pierre R. Latour, PE Consulting Chemical Engineer
CIMFuels Editorials
Pierre R Latour
FUEL Reformulation and
FUEL Technology & Management
July 1995 - January 1998
Hydrocarbon Processing
1997 - 1998
810 HERDSMAN DR, HOUSTON, TEXAS USA 77079-4203; Tel & Fax: 281-679-6709; clifftent@hotmail.com

Table of Contents
FUEL Reformulation
3 May 1995 Pierre Latour Added to Fuel Reformulation Advisory Board
4-5 July 1995 CIMFUELS: Computer-Integrated Manufacturing of Fuels
6-8 Sept 1995 Manufacturing Clean Fuels Doesn’t Have to be a Big Process Control
Problem
9-11 Nov 1995 Establish Performance Measures for Clean Fuels
FUEL Technology & Management (now World Refining)
12-15 Jan 1996 ECONOMICS – Local and Real Time
16-19 Mar 1996 INTEGRATION – Process, People, Computers, Information, Business
and R, D&A
20-23 May 1996 Scheduling = CLRTS
24-28 July 1996 Operations Optimization = CLRTO
29-33 Sept 1996 Advanced Dynamic Process Control = CMVPC
34-36 Nov 1996 Reconciliation, Learning, Improvement – RLI
37-40 Jan 1997 CIMFUELS: NPRA Computer Conference Continues to Grow
41-44 Mar 1997 CIMFUELS: Commercial Practice – Tools Vs. Solutions
45-47 May 1997 Time Cycle Management
48-50 July 1997 Intangible Benefits? Make Tangible!
51-52 Sept 1997 Data Management; Decision Support
53-56 Nov 1997 Benefit Potential > $1.00/bbl Crude
57-63 Jan 1998 Risk/Value: What’s Wrong With This Picture?
Hydrocarbon Processing
64 June 1994 Other ways to justify control and info. systems
65-68 July 1997 Does the HPI do its CIM business right?
69-70 Jan 1998 Decision-making and modeling in petroleum refining
71-74 June 1998 Optimize the $19-billion CIMFuels profit split
75 Author – Pierre R Latour
Copyright protected

Publisher for FUEL Reformulation, May/June 1995, p1 (3rd
column- Pierre Latour added)

CIMFUELS Editorial for FUEL Reformulation, July/August 1995, p17
CIMFUELS: Computer Integrated Manufacturing of Fuels
Dr. Pierre R. Latour
Co-founder, Vice-President (retired)
Setpoint, Inc.--Houston, Texas
While computers have played a basic but fragmented role in manufacturing fuels and
petrochemicals since the 1960's, the breadth, complexity, evolution, need and forecast of the role
for Computer Integrated Manufacturing of clean fuels in the 1990's and beyond 2000 is very
profound and challenging. In fact, it may be the biggest problem/opportunity which distinguishes
business performance and competitiveness worldwide.
Challenge and Opportunity
The profitable manufacture of clean gasolines and diesels, within the rules, is basically a
control problem, filled with economic modeling, optimization, scheduling, multivariable
predictive dynamic controls, integration, information management, accounting and decision
support activities. The scope is spreading from distillation columns to process units to refineries
to multi-site companies to third parties. Those who intend to compete and excel in manufacturing
fuels for profit must master these basic functions and activities with high skill and performance
for their processes, organizations and computer networks. The refinery margin benefit potential is
0.5 to 1.0 USD/BBL crude (depending on many things) with a sustained cost to capture of 10 to
50% (or more) of this gross benefit (depending on many other things). Some refiners have
captured much of this net potential, many have not.
Perspective
Fuels manufacturing management has four fundamental technology assets to lever influence
on business performance: processes, catalysts, humans (staff and organization) and CIM. Among
these, it now appears CIM is a strong differentiator among refineries because it is the most
difficult for managers to manage. The difficulties stem from hard to quantify intangible benefits,
high risk, unclear requirements and costs, rapid technology change and obsolescence,
controversial (political) functions and roles, short supply of expertise, lack of standards (make or
buy), fragmentation among inside staff and suppliers, unstable business practices (low cost
competitive bids versus strategic performance partnering) and at bottom disarray on
nomenclature, definitions, language and understanding for the new, always changing computer
field. These basic obstacles to success are falling as each company learns and matures from
CIMFUELS experiences.
CIMFUELS defines the intersection between the hydrocarbon processing industry and the
computerized information (Cyberspace, Internet?) industry, two of the biggest industries in the
world. There is universal consensus and obvious agreement in principle that some portions of
CIM ought to play a role in the manufacturing of the largest bulk chemical in the world: gasoline
and middle distillate transportation fuels. As quality/purity specifications (Octane, TOX, NOX,
SOX, RVP, CO, O2, etc.) explode in number, complexity and financial significance. The devil is
in the details; like what functions are performed, by whom (what system), on what basis, how
often, with what authority, for what purpose, with what benefit, at what cost, with what risk,
under what assumptions, with what options. What is the proper pace for broadening applications
and who is in charge of what?

While manufacturing cleaner fuels is technically feasible, the continuing problem is how to do
it well, efficiently, optimally, profitably and within the rules. CIM technology may have arrived
just in time to make clean fuels practically and profitably, but it is forcing business managers to
define and maintain clear descriptions for the objectives (performance measures) and the rules
(constraints, compliance measures, penalties). As modern computer networks (and staff and
processes) evolve in offices and plants, they should be viewed as tools and assets which
repeatedly inquire of business leaders: What is your/our objective and purpose for me, what can I
do to help? Use me better!
Modeling for Results
These ideas set the foundation for the modeling of everything: processes, control systems,
customers, environments, noncompliance, safety, reliability, flexibility, quality, speed, trade-offs
and decision-making. Sophisticated and effective CIMFUELS should not simply gather and
report mountains of flow, temperature, quality and financial data about the past. They should
provide natural functions, knowledge, memory, learning and discipline; complementary to people
and processes for planning, scheduling, optimization, control execution and auditing for
improvement of everything for future profit performance. These are the functions that make
money.
Where are we Going?
In future issues, this editorial column will report on CIMFUELS for business management.
We will build on history with definitions and descriptions, problems and solutions, performance
measures and tangible incentives, principles of planning, execution and maintenance, trends in
technology, debates and questions, explanations and achievements, and strategic visions. We will
point out management issues for consideration and action plus offer proven principles to guide
toward successful results.
It is my hope that the role and performance of CIMFUELS will be properly incorporated in
important planning and execution of business strategies for manufacturing future high quality
fuels. The companies with leadership and vision will prosper. The companies without leadership
and vision will not.
Look for a description of "the big control problem" in a future column. We intend to focus
later columns on performance measures, real time-local operator economics, integration,
scheduling, operations optimization, advanced dynamic process control, learning/improvement,
time cycle management, modeling, data management, decision support, tangible benefits
determination, how to identify/capture/sustain $1.0/bbl crude, blending and oil movements,
FCC/Alky/Ether/blending, CR/ISOM/H2/blending, managing carbon/hydrogen/sulfur,
transportation and logistics, government compliance, CIMGASOLINE, CIMDIESEL, CIMMID-
DIST, CIMPETROCHEM, CIMBOTTOMS, communications, data bases/MMI/client/server,
suppliers of technology/equipment/services/solutions, America/Europe/Asia activity, property
quality control and other CIMFUEL topics of interest to FUEL readers.
Stay tuned...

CIMFUELS Editorial for FUEL Reformulation, September/October 1995, p18, 20
Manufacturing Clean Fuels Doesn’t Have to be a Big Process Control
Problem
Vice President, Business Development
Dynamic Matrix Control Corporation
Houston, Texas
Manufacturing clean fuels commercially is, at bottom, a big process control problem.
Although new process, catalysts, and equipment have been developed to ensure the technical
feasibility of making reformulated gasoline and low sulfur diesel components, comprehensive
study of the US - CAAA90 illustrates a big control problem to operate plant, blender, delivery,
and compliance mechanisms efficiently for competitive profitability. How to make each batch of
RFG and LSD with all qualities (nine or so) exactly on specs, optimally, competitively, every
time?
It is wise to define the control problem carefully and completely, before devising and
installing methods and mechanisms to solve and operate it.
CAAA90 Control System
The Clean Air Act Amendments of 1990 formulated national goals and a control system (of
sorts) to 1) improve human health, by 2) cleaning air pollution, by 3) improving exhaust
emissions, by 4) improved fuel compositions, by 5) improving fuel components from 6) oil,
gas, and bio feedstocks... among other things. The Congress and President actually set some of
the controller setpoints (gasoline 02 > 2w%, RVP < 9PSI) as well as structures and mechanisms
for developing and operating this national distributed control system.
The system now has localized air quality attainment specs, evolving vehicle composition
specs, “corresponding” fuel composition specs like CARB2 in Jan ‘96, “corresponding” blend
component requirements, and consequences for crude oil and other feedstock quality/demand.
Process vs. Process Control
Chemical engineering tradition since 1965 has taught the distinction between process design
and process operation/control. The technology of advanced process control automation of major
plants focuses on adjusting operating conditions to optimize profits (yields, operating costs,
capacity) while safely meeting product quality targets with feeds and economics which differ
from design premises. These systems swap variability of qualities and constraints for valves, in
face of disturbances, by adjusting manipulated variables (MV) to hold dependent response
controlled variables (CV) at optimum setpoint targets (SP), in face of unmeasured disturbances
(DV) at the best combination of constraints (on CV and MV) to optimize a profit function.
Constraints are properly set at the intersection of credits against violation penalties.
Manufacturing vs. CIM
The information systems engineering tradition since 1980 has taught the distinction between
manufacturing equipment and the science/art of performance measures, data modeling,
scheduling, large scale optimization, and integration (of organizations, systems and businesses)
for computer integrated manufacturing. The 1980’s CIM is undergoing its first fundamental re-

engineering in the 1990’s. The power of CIM comes from using computers to perform useful,
meaningful, important, profitable functions like those to solve the big control problem, or major
segments of it.
Economics of Quality
Most now see the competing trade-offs between benefit (to people) and cost (to people
through manufacturers) for each quality. These must be modeled physically (chemically) and
financially (human values), in order to properly make critical decisions about the numerical
setting of targets, specs, limits and constraints. Further, many leaders now recognize the
importance of quantifying the net credit slope to approach each limit (quality give-away), as well
as the necessity to quantify the net penalty slope for violating each limit (cost of non compliance).
The latter is now known to be the quantitative source of major “hidden, intangible” benefits from
improved quality variance (reliability).
As future quality requirements tighten, these influences become increasingly nonlinear and
critical. That is, the marginal cost to achieve successive quality improvement steps naturally
increases, so quality giveaway costs become increasingly significant. Consequently, the value of
precise modeling and tight control becomes increasingly important.
The first step is to determine a quality setpoint assuming perfect control (zero variance) to
properly tradeoff violation penalties (from customer values) against manufacturing costs. The
second step is to properly determine the offset tolerance to account for real statistical variance
performance from uncertain analysis, inaccurate models, uncertain components, inaccurate
operations, and imperfect CIM. (Herein lays the incentive for powerful CIM.)
Blending Serves Marketing
Component blending for clean fuels has become a very complex business in the 1990’s, within
the refinery and downstream. Blending now ranks with fractionation and cracking as a major
refining process step. Customized RFG for regions and seasons, and the variety of diesels and
middle distillates have transformed in-line fuel blending into batch steps, oil movement
transactions, and refinery unit operating modes. In-line blends sometimes go directly to
transportation (pipeline, ship, rail, truck) and sometimes come directly from process units. This
requires sophisticated use of storage for components and finished fuel products.
New financial modeling of all these blending, oil movement, and inventory operations are
underway for control optimization and scheduling, i.e. CIM. This is one part of the “Big Control
Problem”.
Processes Serve Blending
Refinery and petrochemical processes make a host (8-12) of components for each
RFG/conventional gasoline blend. What are the proper (optimum) flow, quality and value of each
component needed for each blend? Obviously this depends on other components to be blended
(their flow, quality, value) and the finished fuel to be sold (flow, quality, value).
Traditionally, LP planning tools lump average (over a week or so) product qualities with
lumped crude types to determine aggregated/lumped/average flows and qualities for components
from processes to “pools”. This is now changing to customized manufacturing of many
components for each unique fuel blend batch, in concert and harmony with future plans (crudes,
shutdowns, products, price forecasts). This is another part of the “Big Control Problem”.

Economics of Cuts
Process control accepts the problem objectives in terms of proper (optimal) setting of each cut
along the processing chain to finished fuels. Some examples are splitting virgin naphtha between
isomerization and reforming, splitting FCC olefins among alkylation/ ethers/FCC gasoline
components, splitting iC4 between alky and gasoline components, stabilizing to split C4/C5
between component RVP and C5 in LPG. Each split decision, in real operating time, should
consider local operating costs and global economics and constraints.
Economics of Feeds
The value and quality of crudes, gases, and intermediate supplies are becoming more closely
coordinated along the scheduling chain of processes modes and blends to finished fuels, in order
to follow the principle: “what it is worth depends (critically) upon what you will do with it, what
you make of it, what you sell it for, when”.
Conclusion
Manufacturing clean fuels in the 1990’s is basically a “Big Control Problem”. There are many
operations, components, players, and interactions along the value added chain of steps from oil
and gas to the vehicle customer for combustion to the atmosphere.
The control problem to meet all fuel quality specs, for each batch blend, simultaneously,
optimally, on schedule, competitively, must be clearly understood before it can be solved and
mastered. The incentives to get it right with CIM can approach 1 USD/BBL crude (or the penalty
of regularly getting it wrong might approach 1 USD/BBL crude, as you wish).
Whether American (and foreign) political leaders designed a feasible or optimal control
system remains open. But CIM applications abound.
Look for ideas on Performance Measures in the next issue of Fuel Reformulation.

CIMFUELS Editorial for FUEL Reformulation, November/December 1995, p19 - 20
Establish Performance Measures for Clean Fuels
Vice President, Business Development
Houston, Texas
Improving and sustaining profitability is fundamental to a business’ success. There is a host of
secondary, yet useful and complementary performance measures (PMs), which indicate important
factors contributing to long term profitability with reduced risk.
Baseball is replete with statistical performance measures deemed to indicate success: winning
the World Series, winning pennants, winning games, hitting above 300, pitching below 2.00
ERA, committing no errors, stealing many bases, pitching many strike-outs, and hitting many
homeruns. These statistics relate to profits for the club owner in complex ways.
The strategic PM for the manufacturing of fuels is the present value of reliably forecasted
future profits taken over appropriate future periods and discounted for the time value of money.
One of the five primary functions of Computer Integrated Manufacturing of fuels, CIMFUELS, is
to model, measure, and report useful performance measures for keeping score, accounting,
learning, evaluating, and improving.
PM Examples
PMs for products include quality (giveaway/violations), delivery (early/late to inventory),
amount (over/under) and value. It is clear that product quality giveaway and violations for RFG,
LSD, and most future fuels is prohibitively expensive.
PMs for refineries include capacity (vs. yield), sulfur handling, carbon rejection, hydrogen
addition, crack spreads, inventory management, losses, energy consumption, emissions,
flexibility, reliability, responsiveness, cost of manufacture, safety, permit compliance, economic
margin and competitiveness benchmark.
PMs for process equipment includes the approach to limits for capacity (vs. yield) like
compressor speed, distillation flooding, furnace firing, tube rupture temperature, pressure relief to
flare or atmosphere, separator velocity, tank spills, reactor run-aways/reversals, heating limits,
cooling limits, valve positions, coking, corrosion, fouling, plugging.
PM Ingredients
Most PMs have six ingredients or components. These are mean (average), variance, limit
value, credit (yield, capacity, operating cost) for approaching the limit, penalty debit (safety,
customer, legal, emergency) for exceeding the limit and optimum target for the mean. The latter
can be determined from the preceding four ingredients. When the penalty debit exceeds the
approach credit, the best target is within the limit value. The Greeks were right when they advised
around 450 B.C. “it is better to play it on the safe side”. The amount of cushion depends on these
ingredients. In fact, it can be determined analytically. Also, when the approach credit exceeds the
penalty debit, the best target is beyond the limit value.

These six ingredients provide the basic means for quantifying improved performance of
CIMFUELS, which is manifested in:
1. proper determination of limit value, credit and debit for deviation,
2. assessment of mean and improvement of variance,
3. proper setting of target and mean at target,
4. increasing credit for approaching limit,
5. reducing penalty for exceeding limit.
Objective Functions
Each of the five CIMFUELS functions has an associated objective function for performance
measurement. Proper profit or cost function expressions are required for each major process step,
such as distilling, cracking, reforming, alkylation, etherification and blending. These functions
must be properly linked with the refinery profit or cost functions. Since no one is interested in
improving, let alone optimizing, the wrong objective function with incorrect economic incentives,
accurate objective functions are obvious PMs. They can be quite complex.
The second of five primary CIMFUELS functions, advanced multivariable predictive dynamic
control, compels careful attention to the financial purpose and performance of a process unit. The
third CIMFUELS function, closed-loop optimization of groups of processes and whole refineries,
compels careful attention to the financial purpose and performance of these larger groups and
plants. In fact, optimization starts with the expression of the profit function, or performance
measure to be optimized, subject to process relationships, rules, limits, and constraints. Modern
scheduling packages for the fourth primary CIMFUELS function highlight the requirement for a
scalar financial value measure of approach to JIT (Just In Time), which discriminates between
good (if only suboptimal) and poor schedules, all of which must be mechanically feasible. The
fifth primary CIMFUELS function, integration of information flow, CIM functions, people, and
process operations to the business should have a purpose and associated set of PMs.
CIMFUELS software platforms now make it easy to insert models of plants, plans and rules of
operation. Often a major human task for successful CIMFUELS is to sort out and quantify what
we want the plants and CIMFUELS systems to accomplish. Focus on PM is very useful to start
building CIMFUELS right.
CIMFUELS Performance
Performance measures can also be devised specifically for CIMFUELS systems. The long
sought methods for justifying information systems and quantifying the value of information are
now at hand, because these ill-posed questions are now related to business functions with PMs.
They turn out to be comparison appraisals (base case/delta case, without/with, off/on) of
improved business performance (yield, operating cost, capacity, PV profit), just like any other
business component such as process equipment, staff group, support contract. CIMFUELS
benefits start with analysis of the five primary functions which CIMFUELS can improve: PMs,
planning and scheduling, optimization, control and integration. If improvements in these
CIMFUELS functions can enhance fuel manufacturing performance, this performance benefit can
be weighed against the cost to identify, capture and sustain benefits.
If the business operation is simple, static, low risk and well known, the value added by PMs
and CIMFUELS is naturally rather low. If the business is complex, dynamic, controversial and
risky, PMs are critical and CIMFUELS value added is substantial. For complex fuel and
petrochemical plants, the net return from comprehensive CIMFUELS systems is 0.5 to 1.0
USD/barrel of crude. Invariably, most capacity constraints can be safely pushed 2 - 4%.

It pays to know what you are doing and why. PMs tell that story. CIMFUELS provide PMs for
fuel manufacturing, and PMs provide the benefits for CIMFUELS. The James Dunlap (Texaco)
article “Meeting the challenges of Global Competition” in the July issue of FUEL illustrates how
he sees integration of the information revolution principles and technology for CIMFUELS. This
is another form of “REFORMULATION” for fuels.

CIMFUELS Editorial for FUEL Technology & Management, January/February 1996, p16, 18
Economics - Local and Real Time
CIMFUELS is Beginning to Revamp the Methods for Determining Manufacturing
Economics for Fuels and Petrochemicals
Vice President
Dynamic Matrix Control Corp.
Houston, Texas
……………………………………………………………………………………………………….
Closed-loop real-time optimization incorporates business objective functions, rigorous
process models, flexible open-equation software formats, large scale non-linear
successive quadratic programming optimization techniques and reconciled data fitted
with plant parameters.
……………………………………………………………………………………………………….
Every restaurant menu shows the customer the benefit and cost for meal selections. Most track
volume, costs, value added and profitability of each item and each restaurant. Every clothing and
grocery store shows the customer the benefit and cost for product selections. Each tracks volume,
costs, value added and profitability of individual items, departments and stores along the supply
chain. Does every debutanizer (or ACU, FCC, alky, blender) operator track the volume, quality,
costs, value added and profitability of his products (overhead C5, bottom C4) to his customers
(blenders), based on feed price and reboiler/condenser costs, hourly?
Situation – Credits and Penalties
Operating economics for processing hydrocarbons to manufacture fuels and petrochemicals
are complex, highly interactive, volatile, nonlinear, uncertain and hence controversial. There are
sell prices, production values and buy costs. Each may be average or marginal for a particular
stream. Each may be contracted, spot, future, offered, or forecasted. There are production amount
values/barrel and quality values/property. There may be credits for improved quality within
specifications and penalties for quality specification violations. There may be incentives for
product uniformity and timely delivery, and penalties for variability and unreliability.
Accounting policies may impose ad hoc rules for allocating fixed cost overhead among
processing steps and profit centers such as head count, capital deployed, throughput or "activity
based". These methods are used rather than allocating overhead proportional to value added/profit
generated because determining the profit independently of overhead allocation has been
computationally unwieldy.
Intermediate stream transfer prices for flows and qualities, particularly across manufacturing
business profit center interfaces, for major recycles (e.g., H2) and environmental emissions
remains notoriously difficult and controversial.
Tracking Plant Profit Margins
Tracking value added and sources of plant profit margin contributions remains difficult.
Assuming a refinery margin is 3 USD/BBL crude for average crude and product prices and
variable operating costs, what is the contribution among the major products: gasolines (each

grade), middle distillates, black products, aromatics, oxygenates and sulfur? What is the
contribution from each process: ACU, FCC, CR, alky, blender, utilities, sulfur? What is the
profit contribution from each debutanizer, boiler, storage tank and sour water stripper? How are
such things determined? If refinery margin is negative, how is the loss distributed among
products and processes?
Buy/sell decisions for intermediate streams (e.g., virgin naphtha, FCC feed, isobutylene,
MTBE, RBOB, raffinate) need clear assessment of existing transfer prices (average and
marginal), variance and causes/consequences of these values.
Economic Information Handicaps
Every process (e.g., distillation) has an optimum feed rate at the trade-off point where the
incremental value of products (marginal yield times marginal prices) equals incremental cost of
feed (marginal feed flow/quality times marginal cost). Yet there are very few shift operators of
crude units, FCCU’s, blenders with such current, accurate information. Marketing information
about price forecasts is often poorly synchronized with current (let alone future) crude processing
and unit modes. Customer (dis)satisfaction and preferences are rarely quantified or
accommodated analytically. Economic planners and schedulers often issue production and quality
targets to operations, without in-depth business information about financial objectives, economic
sensitivities, assumed constraints, prices or penalties.
As a result, the economic consequences of adjusting cut points, refluxes, recycles, pressures,
temperatures and flows are often not available to the operator or his advanced control system.
Operations planning continues to be impeded by LP tools which employ averaging (over 30 days
or so), preset multiple periods, lumping, pools, linear model segments, inaccurate shadow prices
and artificial constraints on dependent variables. Scheduling blend batches, oil movements,
inventory, receipts/shipments and unit modes/maintenance continues to be limited to the short
term because CIMFUELS scheduling technology for mechanical feasibility, financial appraisal
and improvement algorithms remains in its infancy.
While it is remarkable how well plants are operated under these economic information
handicaps, most observers agree the lack of accurate, localized, real-time economic information
for hourly operating decisions is a fundamental weakness of fuel and petrochemical
manufacturing practice worldwide. It is a basic barrier to improved performance.
The Solution - CLRTO
At bottom, CIMFUELS forces people to think more analytically and quantify objectives,
models, communications and decisions.
CLRTO in particular is currently progressing rapidly to provide closed-loop real-time
optimization of major processes (ACU, FCC, olefins, blending) and entire refineries and
manufacturing complexes.
CLRTO incorporates business objective functions, rigorous process models, flexible open-
equation software formats, large scale nonlinear SQP optimization techniques and reconciled data
fitted with plant parameters (i.e., efficiency, fouling, hydraulics). Solution periods are shrinking
to less than hourly, while the number of multiple periods is increasing.
One useful result of the plant-wide global optimum solution allows arbitrary interface
envelopes defining sub plants of interest to obtain intermediate stream transfer prices, which have
already been optimally negotiated among the units. If the CLRTO results are routinely and

reliably implemented to the plant, integrally involved in affecting its performance (usually
through comprehensive dynamic multivariable predictive controllers), these local, real-time
economics finally become accurate, meaningful, reliable and useful for other business purposes.
CLRTO of a refining complex provides the basic link of CIMFUELS between marketing and
operations. It includes allocation of fixed overhead costs based on value added among suitably
defined operating profit centers.
The Expectation - 0.3 $/B
Trend plots of local, real-time economics start with forecasts for product and feed prices. Next,
process shutdown/maintenance/catalyst performance trends are developed.
CLRTO uses price forecasts to optimize operating mode conditions. As a consequence, it
provides reconciled local, real-time cash flow economics of all streams and process steps of
interest, with trends plotted at least hourly. Value added tracking and profit margin allocation
among interacting process steps is simultaneously determined. Primary active constraints are
identified down to heat exchangers, compressors and valves. Performance of process profit
centers and product profit centers is measured. Buy/sell decisions can be made for any stream.
All stream values and process profits are determined for each batch of gasoline/distillate, for each
property of each product batch, for each crude, product slate and processing mode/configuration.
People need to focus on modeling, constraint trade-offs, objectives and performance
monitoring, along with how to get computers to act on these human creations.
Local, real-time economics, deployed with CLRTO and business decisions, can generate net
benefits of 0.3 to 0.5 USD/BBL of crude for most refineries. For 200 MBPD, the present value
profit over 20 years at 10%/y is 173 - 288 MMUSD.
Landmark Paper - HCU
Sunoco, at Sarnia, Canada recently reported the first commercial CLRTO with MVC on a
HCU - SMR plant (1). The work began in 1985. By 1994, CLRTO and MVC were generating
0.17 plus 0.23 = 0.4 USD/BBL feed times 20 MBPD for NPVP (20y, 10%) of 23 MMUSD. This
success was recognized by a Smithsonian Institute award.
Stepping from a HCU to an entire refinery is now at hand, one of the major current activities
of CIMFUELS.
Table 1. CLRTO Captures Economic Opportunities Otherwise Lost
Tactical Opportunities
One of the most lucrative opportunities for real-time optimization is in coordinating setpoints to
take advantage of trade-offs that are outside the scope of the individual controllers.
Strategic Opportunities
Market opportunities identified by planning and scheduling can be captured as soon as updated
economics are available.
Refinery-Wide Optimization
Provides the necessary platform through which refinery-wide optimization can be implemented.

Reference
1. Pederson, C.C., et al, "Closed Loop Real Time Optimization of a Hydrocracker Complex," paper CC-95-121, NPRA
Computer Conference, Nashville, TN, Nov 6, 1995.

CIMFUELS Editorial for FUEL Technology & Management, March/April 1996, p14, 16
INTEGRATION - Process, People, Computers, Information, Business
and R, D&A.
Vice President
Houston, Texas
……………………………………………………………………………………………………….
CIM technology highlights the arbitrary definition of jobs and groups and the
interfaces among them for business purposes. Since the amount, quality and value of
any person/group work depends on how it is used, modeling of human organization
work flow network integration is vital to manufacturing decision success.
……………………………………………………………………………………………………….
One of the five principal active money making functions of Computer Integrated
Manufacturing of fuels and petrochemicals is INTEGRATION. Integration of what? Processes,
people, computers, information, business(es) and research/development/application. Why? To
increase profitability (read profit growth). This is the most complex and critical of the five CIM
functions.
Process Integration
Processes for manufacturing operations are traditionally defined as steps like reaction,
conversion, separation, recovery, blending and auxiliary utilities. Processes must be segregated
and integrated simultaneously for analysis, operation, control, optimization, scheduling and
accounting.
Current CIM technology shows how to manage series/parallel networks of processes with
recycles for multifeed/multiproduct plants. Most processes are connected to most other processes.
The proper operation of any process is intimately related to the operation of processes to which it
is connected. Each process is a client/customer of its supplier processes, and is in turn
simultaneously a server/supplier to its product processes. Integration is
upstream/downstream/sidestream/recycle stream.
CIM technology highlights the arbitrary definition of envelopes for processes and their
interfaces to other processes, for business purposes. Since the amount, quality and value (average
and marginal) of any stream depends on where it goes and what happens to it, modeling
knowledge of process integration and interfaces is vital to manufacturing decision success. CIM
is the enabling technology for actively operating integrated processes in integrated ways for
improved global performance.
People Integration
People and organizations for manufacturing operations are traditionally defined in terms of
functions, teams and skills for performing required work. People must be segregated and
integrated simultaneously for analysis, operation, control, optimization, scheduling and
accounting work.

Current CIM technology shows how to manage networks of people and organizations for
multifunction/multidiscipline/multiobjective plants. All people/groups are connected to most
other people/groups. (It seems everything is connected to everything else!) The proper role,
responsibility and authority (RRA) of any person or group is intimately related to the RRA of
people or groups to which they are connected. Each person/group is a client/customer of its
supplier persons/groups and is in turn simultaneously a server/supplier to its customer
people/groups. Integration is up/down/across/recycled through the organization.
CIM technology highlights the arbitrary definition of jobs and groups and the interfaces
among them for business purposes. Since the amount, quality and value of any person/group work
depend on how it is used, modeling of the human organization work flow network integration is
vital to manufacturing decision success.
CIM is the enabling technology for actively managing integrated people/groups in integrated
ways for improved global performance.
Computer Integration
Networks of digital equipment for manufacturing operations are traditionally defined as
mainframes, work stations, personal computers, distributed control systems, LANS and WANS of
client/servers. System components must be segregated and integrated simultaneously for analysis,
operation, control, optimization, scheduling and accounting.
Current CIM technology shows how to build computer networks for real-time continuous and
batch operation. Most computers are connected to most other computers. Proper operation of any
component is intimately related to the components to which it is connected. Each component is a
client/customer of its supplier components, and is in turn simultaneously a server/supplier to its
product components. Integration is up/down/side/ring wise.
CIM technology highlights the arbitrary definition of network components and interfaces for
business purposes. The size, speed and value of any component depend on its role in the network;
modeling knowledge of network integration is vital to manufacturing computer performance.
CIM is the enabling technology for actively designing and operating integrated computer
networks in integrated ways for improved global performance.
……………………………………………………………………………………………………….
CIM technology highlights the definition of information functions and interfaces.
Since the amount, quality and value of any information and software depends on how
it is used, modeling knowledge of information integration and software performance is
vital to manufacturing success.
……………………………………………………………………………………………………….
Information Integration
Manufacturing businesses make money by supplying products and information competitively.
Information generation, communication and management are the glue at the interface between
physical and financial events. Information must be segregated and integrated simultaneously in
efficient, meaningful and timely ways for analysis, operation, control, optimization, scheduling
and accounting of processes by people and computerized functions.

Current CIM technology shows how to manage series/parallel information networks for multi-
process/multigroup plants. It provides the software and communications to achieve valuable
results. The proper management of any information network is intimately related to the processes
and organizations to which it is connected. Each information processing step (software package)
is a client/customer of its suppliers and is in turn simultaneously a server/supplier to its
customers. Information integration is up/down/sideways/recycled.
CIM technology highlights the definition of information functions and interfaces. Since the
amount, quality and value of any information and software depends on what is done with it (how
it is used), modeling knowledge of information integration and software performance is vital to
manufacturing success.
CIM is the enabling technology for actively building and managing integrated information
flows in integrated ways for improved global performance.
Business Integration
Businesses and profit centers for manufacturing are traditionally defined as value added
activities manifested in arbitrary ways like processes, products, plants, sectors, regions, markets,
customers. Businesses must be clearly defined as to what is offered for sale, to what market, with
what assets, for what purpose, within what rules, with what authority. Businesses must be
segregated and integrated simultaneously at different levels for analysis, operation, control,
optimization, scheduling and accounting.
Current CIM technology shows how to integrate series/parallel businesses with interactions.
Most profit centers are connected to most other profit centers. The proper management of any
profit center is intimately related to the management of profit centers to which it is connected.
Each profit center is a client/customer of its supplier profit centers, and is in turn simultaneously a
server/supplier to its customer profit centers. Integration is up/down/sideways/recycled.
CIM technology highlights the arbitrary definition of envelopes for profit centers/businesses
and their interfaces to other profit centers/businesses for business/financial purposes. Since the
amount, quality and value of any business depend on what it does, for whom to create value,
modeling knowledge of business value added integration is vital to manufacturing business
success.
CIM is the enabling technology for actively managing integrated fuel manufacturing profit
centers for improved global performance.
R&D&A Integration
Integration of CIM systems research, development and application is fostered by university,
industry and vendor interaction. Integration of chemical engineering research and education is
fostered by process engineering, process dynamics and control engineering, process optimization
and scheduling engineering interaction. Integration of chemical engineering, chemistry, computer
science, systems engineering and mathematics fosters CIMFUELS technology. Many conferences
and consortia are promoting this integration around the world. The Chemical Process Control
Conference V at Tahoe City, NV on January 7-12, 1996, sponsored by CACHE Division of
AIChE every five years brought together 120 experts from academia and industry for the fifth
time since 1976 for assessment and new directions in constrained multivariable predictive
dynamic control (CMVPC), closed-loop real-time optimization (CLRTO), plant-wide modeling
and scheduling for chemical engineering systems. Here is the foundation for CIMFUELS like
RFG and LSD. Commercialization is now expanding due to improved R&D&A integration.

CIM Integration
Notice any parallelism, repetition and commonality among these six sectors? Beyond
integration within each sector: processes, people, computers, information, businesses and
R&D&A is the integration of all six sectors together.
Sectors must be segregated (as above) and integrated simultaneously for analysis, operation,
control, optimization, scheduling and accounting. Each sector must be defined, justified, planned,
designed, built, used, managed, maintained, improved, audited and integrated. Current CIM
technology is beginning to show how this can be done profitably for manufacturing fuel and
petrochemicals. Incentives approach 1.0 USD/BBL crude. The real issue is how to identify,
capture and sustain such performance, net after costs. 1996 looks like an interesting year for
CIMFUELS - INTEGRATION.

CIMFUELS Editorial for FUEL Technology & Management, May/June 1996, p21 - 23
Scheduling = CLRTS
Vice President
Aspen Technology, Inc.
Houston, Texas
……………………………………………………………………………………………………….
The purpose of scheduling is to ensure mechanical feasibility of move sequences,
within the rules, for good (prefer best) profitability.
……………………………………………………………………………………………………….
One of the five principal active money making functions of Computer Integrated
Manufacturing of fuels and petrochemicals is SCHEDULING. Scheduling of what? Processes,
shipments, receipts, movements, purchases, sales, deliveries, people, computers, information,
business(es) and research/development/application. Everything. Why? To increase profitability
(read profit growth) of course. Scheduling is fundamental to all our activities and interactions.
This editorial is confined to Closed-Loop Real-Time Scheduling (CLRTS) of manufacturing
operations, one of the five active CIM functions: performance measures, multivariable control,
rigorous optimization and integration. CLRTS resides at the center of CIMFUELS.
Setting the Scene
Discrete product manufacturing, batch chemical processing and batch fuel blending operations
have been scheduled by people for a long time. The first refinery I worked for in 1966 had an
Economics & Scheduling Department (E&S) reporting directly to the Refinery Manager. It had
been in existence since around 1946; it ran the place. Every refinery has a refinery scheduler.
Refinery operations scheduling is the (human) activity of accepting and adjusting crude
arrivals, process operating modes, blend batches, product shipments, planned unit shutdowns and
other important refinery operating events prior to their occurrence. The purpose of scheduling is
to insure mechanical feasibility of move sequences, within the rules, for good (prefer best)
profitability.
Scheduling provides the primary interface link between refinery internal operations and
external suppliers and customers. Furthermore, scheduling provides the primary link between
longer term (monthly) aggregated operating Linear Program Plans (LPP), which are not
guaranteed to be mechanically feasible over time, and daily process control and unit optimization
(CMVPC & CLRTO), which may not be well represented by the scheduler’s models. The
scheduler is in the middle of these horizontal and vertical entities (some schedulers call them
crosshairs). If there is a conflict between LPP and CLRTS, the latter usually rules. If there is a
conflict between CLRTS and CMVPC, the latter usually rules. If there is a conflict between
CLRTO and CLRTS, the latter usually wins the negotiation. If there is a conflict between CLRTS
and crude supplier or product purchaser the negotiation is about who pays how much when.
Inputs to scheduling are the desired long term commitments and specific requests or demands
arising at any time from many places. Outputs from scheduling are commitments to requestors
and timing/sequencing orders to operations (CLRTO & CMVPC) to accommodate the
commitments.

Naturally the better the scheduler can evaluate the consequences (feasibility and profitability)
of each commitment the easier it is to make and honor good, fast commitments. The schedule
shows receipt, production, shipment and inventory profiles into the future. Rescheduling occurs
whenever newly revised request/demand information is received/accepted. The schedulers’ orders
specify crude receipts, product shipments, oil movements between tanks and units, process
modes, product types, flow routings from sources to destinations, blend recipes and sequences (all
with exquisite timing).
Problems/Opportunities
Recent changes to a myriad of mogas products (RFG, CARB2, RBOB, REG, PREM, OXY
etc), diesel products, jet fuels, mid distillates, fuel oils, petrochemicals, and lubes have
transformed continuous refineries into “continuous batch” manufacturing plants. Merchant profit
center refineries competing in the spot markets with a worldwide ocean based crude diet, diverse
fragmented markets (geographical, seasonal, commercial, commodity, niche, political) have
significantly more scheduling activity (headache or opportunity?) than a land locked refinery near
a secure billion barrel reservoir servicing a captive stagnant fixed price market.
Savvy scheduling of a modern, complex, dynamic competitive and exciting refining and
trading business for sophisticated clean fuels, petrochemicals and lubes is not so easy. There are
situations today where potential profit from improved scheduling exceeds 0.3 USD/bbl crude
purchased (not necessarily refined).
Status
CIMFUELS is helping in a big way. Modeling for a purpose is the name of the game.
Modeling mechanical steps to simulate an entire refinery for scheduling (tank transfers, receipts,
shipments) is very easy. Assembling inputs (requests, demands, prices, and plans) is also easy.
Object oriented expert system software platforms with relational data bases, powerful graphical
user interfaces, and extensive system interface connects is widespread and inexpensive. Data
management, hardware and communications are powerful and no issues.
However, CLRTS, the Closed-Loop Real-Time Scheduler for the entire refining chain to
deliver (by manufacture or purchase) fuels and petrochemicals to customers is rare, even
controversial.
……………………………………………………………………………………………………….
Faithful implementation of precise schedule output orders remains spotty, a common
weak link with human interpretation and execution heavily involved.
……………………………………………………………………………………………………….
Barriers
What’s holding things up? As usual for CIM, it is hard to confidently predict the financial
performance of CLRTS in order to justify it. Computers and software can do the job all right,
provided we (folks) can tell them WHAT we want to schedule, with what rules (limits), WHY
(objectives) and what happens when the rules are broken.
1. PM. The first barrier is inattention to the financial performance measure, PM, to allow us
(and CLRTS) to distinguish between good/great/profitable/desirable schedules and poor/bad/
loser/difficult schedules. Business models of logistics, customer penalties for off spec or late
products, price forecasting, time value of money for optimum (not minimum) inventory
management which accounts for customer late penalties to optimize JIT policies, demurrage and
operating penalties for rapid sequence switching with low fidelity process control systems are all

fundamental ingredients to appraisal of candidate schedules. Optimization should always be out
of the question if the proper objective function cannot be formulated and computerized. The
objective function should be the expected value of NPV(X mos, Y%). What is the “best” time
horizon for the scheduling period X? Best time resolution for each order, minutes? Uncertainties
and reliability of the inputs to the future schedule should be classified, modeled and accounted
for. Some classifications of types of future information are: contracted, forecasted, proposed,
futures market, tentative, offered and rejected.
2. Methodology. Many seem to insist on optimizing the schedule or nothing in spite of the fact
there is abundant old mathematical proof that no general purpose rigorous algorithm can ever be
devised for combinatorial explosive, nonlinear, mixed integer, generalized scheduling problems.
Since time marches on, if the whole rescheduling problem ever were solved it would be too late.
So the methodology of scheduling will remain partly art as long as humans determine the value
and beauty of things. A better approach is to deploy methods and tools for improving scheduling
within the time, information and capability available. Scheduling research offers many algorithms
(like time based SQP) that can be customized to the particular nature of problems like refinery
scheduling which help find better answers. Expert systems have proven to offer a fruitful tool.
Several heuristics have proven useful:
1) insure mechanical feasibility first.
2) consider financial consequences of every move, decision or change: it costs lots of
money to clean a tank overfill spill or put black oil in a white oil tank.
3) revise schedule as soon as new information or opportunities appear, and decisions are
made.
4) feedback comparison of past orders with results, then continually attempt to reconcile by
improving models, definitions, methodology, communications, analyses, instructions,
safety margins, (everything).
3. Segmentation & Interfaces. Theory and practice have not yet clarified how CLRTS
activities should be segmented to be manageable and solvable: crude receipts, product shipments,
blend sequences, unit modes, whole refinery onsite and offsite together, several connected
refineries and chem plants together? How does the refinery CLRTS negotiate with crude supply
CLRTS and with product distribution logistics CLRTS? Across horizontal profit centers? Who
decides who takes a loss to reconcile discrepancies? Perhaps the greatest potential for CLRTS is
forcing people to resolve these long standing issues.
4. Output. Faithful implementation of precise schedule output orders remains spotty, a
common weak link with human interpretation and execution heavily involved. The connecting
link for closing the scheduler to actuators, control systems and optimizers is gaining attention.
Many offsite tank farms have invested heavily in motorizing remote valves to bring CLRTS to
reality.
5. Gap Closure. Scheduling resides in a technology gap between operations planning
(monthly, quarterly by LP), and operation execution, which includes control (CMVPC) and
rigorous unit optimization (CLRTO). Technical developments are underway to reconcile and
harmonize these rather distinct problems and technologies, in order to close the major
CIMFUELS gap. New CLRTS techniques allow us to devise and implement comprehensive
revised schedules which reconcile the planning - execution gap to meet new situations quickly,
accurately, reliably, and profitably because they act in real-time, closed-loop. That is the key to
gathering rather important money in the gap. Some have found 0.3 USD/bbl crude x 200 MBPD
= 21 kk$/y = NPV (20y, 10%) = 179 kk$/ refinery.

Outlook
Refinery scheduling is a growing proportion of the CIMFUELS business because the
incentives are high for good rescheduling. Clear integrated connections (2 way) downward to
advanced control promises high returns. Clear integrated connections upward to planning (2 way)
provide high returns as well.
The interesting developments are extending CLRTS activity horizontally across traditional
organizational and profit/cost center lines from crude and intermediates supply to
products/components manufacturing and/or trading.
The sector of CIMFUELS called CLRTS can do the job when people can communicate to
CLRTS the job to be done: WHAT to schedule, with what rules, WHY, HOW. Also define the
results to be communicated properly to its CIMFUELS system partners for control, optimization,
planning in an integrated way to enhance appropriate performance measures.
Remember what Woody Allen said: “time is just nature’s way of keeping everything from
happening all at once”. Are you good at scheduling? Does it matter? Do you know for sure?
Worth your while to improve? Know how much your customers value your excellence at
scheduling? Your shareholders? Do you need to check out CLRTS? Sure you know the
benefit/cost/risk? Could it be critical for profitability? Survival? Ask any airline. Follow trade
journals and NPRA. Stay tuned, the refining business is getting trickier every year.

CIMFUELS Editorial for FUEL Technology & Management, July/August 1996, p16, 18, 19
Operations Optimization = CLRTO
Vice President
Houston, Texas
One of the five principal active money-making functions of Computer Integrated
Manufacturing of Fuels and petrochemicals is CLOSED-LOOP, REAL-TIME OPTIMIZATION
of process operations. Optimization of what? The steady-state operating conditions for each mode
that determine production rates, yields, qualities and utilities. Why? To maximize current profits
in harmony with future plans. This editorial is directed to nonlinear steady-state optimization of
rigorous process models for current (minute-to-minute, hour-by-hour) operating modes (1). We
do not include simplified, lumped, pooled, off-line, LP-based monthly planning optimization.
Description
On-line, closed-loop, real-time optimization of process operations first requires formulation of
the profit function representing the financial purpose of the process to be maximized. Next, the
process model of its chemical and physical behavior must be formulated. The specific
independent variables to be manipulated or adjusted (MV) are identified. These are
predominately flow controller setpoints which adjust valves, or feedback control system setpoints
like temperatures, pressures and qualities which reset flows. Independent disturbance variables
(DV) that are not manipulated because they are set by other means (such as ambient conditions or
some feedstock compositions) are identified. Then, dependent variables (DV) that have limits or
specifications and economic importance and which can be measured or inferred are identified.
The number of degrees of freedom that can be optimized equals the number of available MV’s.
Formulating comprehensive profit objective function expressions which properly represent the
purpose and financial performance of the process operation, all significant trade-offs and real-
time price/cost economics remains a challenging yet essential step for CLRTO (not to mention
management of fuels and petrochemicals manufacturing in general). We must tell CLRTO why
we want to run the process.
Rigorous process models include material, energy and momentum balances of chemical
engineering. Component mass, molar and volume (mass/density) material balances include
kinetics and equilibria. Energy balances for heat transfer, reactions and separations are
fundamental. Momentum balances for hydraulics and pressure profiles are basic. Many of these
rigorous steady-state models incorporate differential equations which are integrated through
distance (length and even radially) inside equipment. Accurate prediction of tower flooding,
compressor surge, pump cavitation, separator entrainment carryover, fouling, plugging, corrosion,
metal fatigue, coke deposits and catalyst deactivation are also essential. Neural networks of
combined rigorous and empirical models of hitherto intractable phenomena (e.g. visbreaker
residue stability, combustion NOX emissions, diesel cetane, asphalt penetration) have recently
been successfully applied commercially. We must tell CLRTO how the plant works.
Constraint bounds are placed on the range of feasible values for the independent variables.
Specification and limit values are placed on the range of acceptable values for the dependent

response variables. These usually represent basic trade-offs between process yield credits and
risks of damage, reprocessing, customer dissatisfaction or noncompliance. (In fact, setting these
limits properly is a commonly unmodeled optimization opportunity not covered here.) We must
tell CLRTO the rules (and penalty consequences for violating the rules). Penalty modeling should
receive vigorous attention in CLRTO commercial practice to connect maintenance, safety and
environmental permit compliance to production, yield and quality results.
Statistically-based calculated risks abound. A major US refiner reported (2) that it experienced
“Unscheduled shutdowns and other refinery operating problems increased operating expenses .....
That is why incident-free operations are now the number one priority”. An ASM (Abnormal
Situation Management) Consortium of Amoco, BP, Chevron, Mobil, Shell, Texaco, Novacor and
several suppliers believe the impact on the US economy exceeds $30 billion and $20 billion cash
be eliminated (3). A US refiner experienced $60 million/year expenses from unforeseen
occurrences and abnormal incidents. The adjective “unforeseen” causes greater concern than the
noun “$60 mil”. Well calculated risks are far superior to uncalculated ones.
Some think of MEMM modeling for CLRTO: Mass, Energy, Momentum and Money
balances.
Optimization solver algorithms like sequential LP and Reduced Gradient searchers are being
replaced by large-scale, open equation, sparse matrix and quadratic programming methods
solving more than 300,000 equations simultaneously, as frequently as hourly.
Span of Processes
CLRTO has been successfully applied to run distillation trains, crude units, hydrocrackers,
fluid catalytic crackers, catalytic reformers, RFG blenders, and whole steam cracker olefin plants.
Programs are underway to handle whole refineries and petrochemical complexes with CLRTO.
The number of MV (designated DOF, degrees of freedom) for common processes are: SGP (10-
15), ACU/VAC (25-30), HCU (20-30), FCC (30-40), CCR (10-15), RFG Blend (10-16), eight
furnace olefin plant (50-60) and fuels refinery (150-300).
Functions
The four main functions of rigorous CLRTO systems are parameter fitting reconciliation,
future operation and equipment revamps simulation, intermediate stream transfer pricing (ISTP)
and optimization of operating conditions. Reconciliation of vast amounts of plant measurement
data with fundamental model relationships is the standard method for fitting empirical efficiency
factors, which may change in unpredictable ways, to provide the most accurate process model of
commercial plants. Optimized simulations of plant performance with hypothetical future feed
types, rates, product qualities, economics or equipment modifications provide the best method for
related decision making.
Routine CLRTO of the operating DOF makes money directly by securing the best process
operation. Further, it verifies the accuracy and fidelity of the models, empirical factors and
associated economics. When these all work together routinely for the whole processing train
connected to suppliers, customers and the environment, we gain assurance that each component is
valid.
The real optimum solution may be with all DOF at some combination of DV limits and MV
constraints. For such a fully constrained solution at a corner in DOF-space, LP solver techniques
are usually suitable. LP solutions are necessarily at a constraint corner where the sum of the
number of limiting DV’s plus the number of constrained MV’s equals DOF. The real optimum

solution may find some DOF at unconstrained interior smooth hilltops and others at limit corners.
Mixed solutions can only be found by nonlinear solvers such as QP. In practice, while the
preponderance of process operation DOF optima are found at limit/constraint corners, some
smooth interior point optima may occur for recycles, refluxes, reactor severities, yield/capacity
trade-offs, parallel flow splits, recoveries and intermediate qualities.
With 90% of DOF optima at limits and constraints, profit improvements from CLRTO are
principally determined by proper setting of DV limit values. If they are set too tightly, they
restrict solution movements and little gain is realized. If they are set too loosely, solutions can
move outside the domain of model validity and experience where nonlinear penalties arise and
risk of damage increases. The current art of successful CLRTO takes great care to set DV limits
properly for maximum safe performance.
Plausible and useful transfer prices for intermediate stream flows and qualities (ISTP) are
notoriously difficult to obtain from simplified, linearized, lumped, averaged LP modeling. Large-
scale, global, plant-wide CLRTO solutions and local process unit CLRTO solutions suitably
connected to other processes for global results provide the proper method for determining ISTP.
These prices are marginal, average and even functions (principally of production rates). ISTP are
used with CLRTO profit functions for value added tracking (VAT) through the plant-wide
processing train to reveal sources of profit generation and loss. Value added determined by
CLRTO provides the rigorous method for allocation of fixed accounting costs rather than
common ad hoc methods such as head count, capital employed or “activity-based” guidelines.
This allows easier definition of profit centers by process units or product lines. In addition, ISTP
provide the proper basic information for buy/sell decisions on intermediate streams and blend
components. Plant-wide CLRTO strengthens profit estimating for crude oil purchase selection
and cost decisions as well as for product slate menus and pricing.
Augment Scheduler
Beyond CLRTO of current operating condition DOF, rigorous optimization of postulated
future feeds, products, modes and economics along a planned schedule sequence can trim and
enhance the estimates from simplified scheduler process models for improved accuracy and
profitability. Important work is now underway to provide people with easy linkage between
CLRTS (scheduling) and CLRTO; further, these two basic functions promise to soon become
computationally combined.
Augment CMVPC
The conventional, primary control systems of most processes lack sufficient dynamic
performance to directly accept targets from steady-state CLRTO. Current technology for
Constrained MultiVariable Predictive dynamic Controllers (CMVPC) provides this necessary
capability for closing the complete optimization loop. Some have steady-state LP or QP
optimizers built in for every move step, minute-to-minute, using necessarily simplified steady-
state models in conjunction with comprehensive dynamic process models for all interactions.
These controllers find and hold a constrained, steady-state optimum solution (just like CLRTO
finds) at a combination of limited DV plus constrained MV summing to DOF. When the CLRTO
solution does not have many interior points, these powerful CMVPC’s capture the main process
performance improvement by reducing variance and holding the process in the neighborhood of
the critical constraint set, which maximizes profit. They provide the fundamental protection to
keep the process transients within the safe operating region at all times. In these common
situations, rigorous CLRTO takes a secondary role as a limiting DOF point selector which
verifies and corrects the limiting points determined by the simpler CMVPC.

When the CLRTO solution is mixed, with significant DOF at numerous interior smooth hilltop
points, rigorous CLRTO finds them more effectively than CMVPC. Flat, hilltop interior profit
optima prove that incentives for tight control of dynamic variance are diminished and the CLRTO
value added dominates that of CMVPC.
In any case, CLRTO is no longer seen as simply “setting optimum steady-state setpoints” for
the basic process control systems (for flow, pressure, temperature, level and quality). Their
automatic, closed-loop connections through CMVPC are more sophisticated as they work
together in closely coordinated partnership. CLRTO may even provide profit functions of DV
values as input to proper statistical selection of DV limits and CMVPC targets to optimize
calculated risk trade-offs fundamental to profitability of any plant.
Barriers
Computational speed limits for plant-wide CLRTO are being addressed by segmenting and
distributing subproblems among parallel workstation/personal computers with proper executive
linking for the global solution. Formulating accurate profit objectives with correct economics
remains a challenge. Modeling the consequences and financial penalties for violating limits and
specifications needs much greater attention. Quantifying the plant characteristics and
environments that are conducive for CLRTO and the performance contribution that it can deliver
will foster its proper commercialization in CIMFUELS. Predicting financial benefits from
CLRTO requires special expertise.
Performance
CLRTO has been generating benefits for manufacturing fuels and petrochemicals since the
1970’s (1). Benefits of 0.002 to 0.003 $/LB of C2= have been generated from a number of olefin
plants since the late 1980’s. They optimize severity against coking. They optimize C3 cocracking
against crack spreads for C2= and C3=. They maximize use of process gas and refrigeration
compressors. They optimize recycles, operating conditions and profits from cracking C2 through
gas oil.
CLRTO can optimize FCC conversion and selectivity for olefins, mogas and distillate against
fresh feed rate (when feed price is well known and rate can be adjusted). They optimize heat
balance, pressure profile and recoveries. They generate 0.05 to 0.1 $/bbl feed. Similar results are
obtained from CLRTO on HCU. Reoptimizing CR severity to customize octane for each blend,
BTX production and H2 yield can generate 0.05 to 0.1 $/bbl naphtha feed in complex refineries.
Crude distillation units are candidates for CLRTO when yield of low value AR increases with
crude rate and marginal economics of a trade-off are clear, strong and variable. While most
refineries do not experience this situation, optimization of pressure and fractionation against heat
recovery merits optimization on large units with strong and volatile product price differentials.
Refinery-wide or plant-wide CLRTO promises to generate 0.1 to 0.2 $/bbl crude in dynamic
competitive economic environments, and substantially more when ISTP and VAT are highly
significant.
Outlook
Standardized, rigorous modeling and CLRTO of operating plants for clearly defined profit
purposes and widespread use within operating companies for all relevant decisions will accelerate
through the remaining 1990’s. As one of the five pillars of CIMFUELS, it will enhance the
competitiveness of fuels and petrochemical manufacturing at sites around the world.

References
1. Latour, P.R., “Online computer optimization 1: What it is and where to do it”, and “2: benefits and implementation”,
Hydrocarbon Processing, Jun & Jul 79.
2. Refiner Profile, Chevron, Octane Week, vX, n43, 6 Nov 95.
3. Companies Team Up To Tackle Control and Software, Chemical Engineering Progress, May 96, p 10.

CIMFUELS Editorial for FUEL Technology & Management, September/October 1996, p17 - 20
Advanced Dynamic Process Control = CMVPC
Vice President
Houston, Texas
We identified the five basic active CIMFUELS functions that make money: Performance
Measures (PM), Information Integration (IT), Scheduling (CLRTS), Operations Optimization
(CLRTO) and Advanced Dynamic Process Control (ADPC or recently Constrained MultiVariable
Predictive Control - CMVPC). The first four were described in recent issues.
This editorial will cover Constrained MultiVariable Predictive Control. Since the late 1980’s
CMVPC has become almost synonymous with Advanced Dynamic Process Control (ADPC).
This is the automatic execution function of CIMFUELS, which ensures that CIMFUELS
technology truly affects change. It moves the plant directly and automatically, while people
watch, check, approve, audit, learn and maintain. ADPC enables CIMFUELS to take charge, take
control, manage - really implement decisions to actively integrate computers with manufacturing.
Some would add ADPC protects the process from awkward CIM, infeasible CIM, incorrect CIM,
and dangerous CIM. Further, good ADPC provides feedback to other CIM functions on
inaccuracies, errors and infeasibilities.
What Does it Do?
The job of ADPC is to adjust or manipulate the primary operating condition settings (every 30
to 60 seconds) for flow, pressure, temperature, level and quality on single-loop controllers, which
in turn adjust primary actuators (every 0.1 second) such as valves and motors. This activity has
traditionally been done by board operators in control rooms. ADPC must safely adjust and protect
the process to achieve some purpose (e.g., production rate, quality and efficiency), while adhering
to the rules (operating limits and procedures). Inputs include operating condition limits (max and
min) on all controlled variables of interest, quality specifications, equipment limitations and
adjustment range bounds. It must employ some economic objective function to guide its trade-off
actions and performance. It should incorporate information about the consequences and penalties
for violating specifications, exceeding limits, breaking the rules.
The function of ADPC is to accept input commands and desires from people and other CIM
functions and execute them as well as possible, i.e., accurately, promptly, safely and optimally.
What is the Problem?
Fuel and petrochemical processes are inherently constrained and limited. Independent
manipulated variables are constrained and dependent controlled response variables are limited.
Fuel and petrochemical processes are inherently multivariable and interacting: each flow
affects other flows, each heat affects other heats, flows affect heats, heats affect flows, heats and
flows affect compositions and qualities, heats and flows affect pressure, most adjustments affect a
variety of limits and economic performance in different ways. Multivariable interactions must be
accounted for to operate processes and make products.

Unmeasured disturbances abound. Feed composition, ambient conditions, catalyst activity and
equipment malfunctions are notable. One might also include economic incentives.
Fuels and petrochemical processes are inherently dynamic. Transient lags and dead times can
exceed several hours. Recycle changes around alkylation plants, olefin plants and hydrocrackers
have long settling times to reach the new steady state. Initial responses are often opposite the
ultimate direction to final steady state. Many responses are oscillatory, with multiple frequencies.
Some are highly nonlinear and not fully reproducible. Response speed increases when production
rates are low. The nature of dynamic responses can differ significantly when portions of the basic
control system are disabled or restructured.
An interesting FCC had it’s preheat furnace and feed temperature control off for a valid
economic reason. Main fractionator pumparound heat to feed affected the riser, which affected
the regenerator and main fractionator, which subsequently affected the riser and main fractionator
again. Uncontrolled transients were detected from regenerator stack CO through the C3 recovery
absorber with several oscillations after 90 minutes, inhibiting the ability to approach limits.
The financial objectives of processes can change modes significantly. FCC can switch from
mogas liquid to olefins to serve conventional and RFG summer blends, then to middle distillates
for winter heating oil or kerosene. Olefin plants change severity to follow ethane-propane co-
cracking spreads. HCU changes from mogas to jet modes are significant. Catalytic reformer
economic objectives can swing from octane to BTX to H2.
How does CMVPC Work?
These controllers are built on a dynamic model of the response of each dependent controlled
variable (CV) to all independent manipulated variables (MV). Most include a steady-state model,
profit objective and optimizer to determine the best feasible final steady-state targets within the
constraint or limit region. They also retain a prediction of how the process is destined to respond
in the near term (its dynamic horizon) based on known prior manipulated inputs and disturbances
currently propagating through the process.
At each control interval (usually every 30 to 60 seconds), the CMVPC devises a sequence of
feasible future moves that will drive CV’s to the desired limiting steady-state targets with
minimum variance along the way for maximum dynamic performance within all imposed limits
and constraints. The first move of this sequence is implemented because it is deemed to be
optimal based on all of the best information available (in a future dynamic as well as a steady-
state sense, i.e., an optimal path to an optimal destination). Then, one time interval later, with new
process feedback measurements available (which invariably differ from predictions) and perhaps
new objectives, the entire prediction, steady-state optimization and minimum variance sequence
is recalculated to determine the new best move sequence and next move.
This creates a robust, high fidelity, high performance, dynamic control system for operating
big fuel manufacturing plants at their proper economic limits, provided the dynamic model fairly
represents the true process dynamics. These models may be rigorous first principles differential
equations for simpler processes, but are more commonly developed empirically from carefully
executed process testing and comprehensive data collection and analysis for complex commercial
processes.

CMVPC technology, developed separately by Shell Oil in the US and Adersa Gerbios in
France during the late 1970’s, is very basic and profound systems theory. The scenario
techniques, rapid inversion of large nonsquare matrices, and identification of process dynamics
by experimental testing of plants with 20 to 30 MV’s and 30 to 50 CV’s on small
microprocessors is remarkable.
What are CV’s?
Not all imaginable dependent response variables relating to a process are candidate controlled
variables. The weight fraction of C41 normal paraffin in crude distiller tray 49 downcomer is not
a CV because it is of no interest or consequence. CV’s are variables we select to control. CV’s
represent phenomena and characteristics we care about. They are important, they may have
imposed limits, they can have financial consequences, and we can assign an economic value to
them that depends upon their magnitude. CV’s must be measurable - directly or indirectly. They
must also be controllable - sufficiently influenced by one or more independent manipulated
variables.

Span of Processes
CMVPC has been successfully applied to run distillation trains, crude units, hydrocrackers,
fluid catalytic crackers, catalytic reformers, RFG blenders and whole steam cracker olefin plants.
Programs are underway to handle larger process combinations in refineries and petrochemical
complexes with CMVPC. The number of MV’s for common processes are: SGP (10-15),
ACU/VAC (15-25), HCU (15-20), FCC (20-40), CR (10-15), RFG Blend (10-16), eight furnace
olefin plant (50-60) and fuels refinery (150-300).
How Well does it Perform?
Dynamic variance is routinely reduced 50 to 90% over basic operator based control. The
financial benefit is critically dependent upon the importance of reduced variance and proper
setting of CV limits. If limits are set too narrowly, the controller (or human operator for that
matter) cannot move the plant much or improve profits. If limits are set too widely, the controller
(or human operator) may move the plant beyond the validity of the model outside the domain of
its expertise into uncharted, dangerous regions, placing profit at risk and perhaps even inducing
its decline. Setting CV limits properly is the key to successful CMVPC application. In practice
these settings are integrally linked to dynamic variance performance, profit trade-off profiles and
statistically based calculated risk taking (which is known to be superior to uncalculated risk
taking) to lessen unforeseen loss incidents and increase overall long term profits.
Benefits in $/bbl throughput of major feed or product that can be identified, captured and
sustained for common fuel and petrochemical processes are typically: ACU/VAC (0.10), FCC
(0.25), HCU (0.25), CR (0.20), DCU (0.30), ALKY (0.15), ether (0.15), mogas blend (0.10), RFG
blend (0.20), middist blend (0.06), aromatics recovery (0.20), lubes (0.5) and entire refinery (0.5).
Olefin plants provide about 0.002 $/lb C2=. Costs to obtain these benefits in the worldwide HPI
were reported in 1995 (2) to be less than 50% of these figures. Many cases have been reported
where costs are less than 10% of benefits.
What is Required?
Clear economic objectives, knowledge of the process, commercially proven software tools,
knowledgeable and experienced appliers, satisfied operator users and financially driven
instrument - computer - software sustaining support are all essential for success. Commercial
software tools, applications know-how technology and capable attention to sustained performance
are available within some large operating companies, from some control system vendors and a
few specialist suppliers. Profit oriented outsourcing business arrangements are growing in
significance.
How does it Fit CIM?
As a basic CIM function, ADPC makes money by itself. It makes even more money when it is
harmoniously connected to the other CIM functions (PM, IT, CLRTS, CLRTO) and used for their
execution to achieve their unified objectives. It can make even more money if realistic results
from CMVPC are regularly fed back to the other four CIM functions so they can modify their
models and behavior to more closely match the true plant (and its associated control systems,
including ADPC) characteristics.
Watching CMVPC in action on processes such as FCC, HCU, DCU, ACU, OLEF and RFG
blenders in the control room with operators and supervisors, in conjunction with scheduling and
optimization, is a 30 year dream of many practitioners now coming true. One must turn it off to
see how much money is lost and to appreciate its true value, because people only learn to value
things properly after they are deprived of them.

What is the Current Status?
By late 1995, there were over 2233 commercial CMVPC installations (1) from the five main
suppliers, with 1500 of these in oil refining and another 483 in petrochemicals and chemicals.
Virtually every type of process unit has been controlled by CMVPC. Universities teach this
technology, professors continue active research, books have been written, papers frequently
report performance of commercial successes, conferences are held regularly worldwide, short
courses are offered widely, most DCS vendors offer some tools and algorithms and several
technology suppliers have growing businesses licensing products and working commercial
application solutions.
Trends
Applications are trending to larger single controllers (30 MV’s x 60 CV’s) on multiple
connected processes, coordination among several subcontrollers (for a whole olefin plant), new
methods for quantifying financial value, tighter integration with operations optimization
(CLRTO) and outsourcing of implementation and long term on-site maintenance support with
financially sound partnerships between operating companies and selected suppliers. Sound
commercial shared risk - shared reward (SR)2 arrangements are useful (probably essential) to
sustain profit performance from CMVPC over the life of the process operation.
Applications in the US, Europe and Japan are often revamps of older classical ADPC with
CMVPC. Applications are spreading to existing process units throughout the world. Grassroots
plants normally provide for CMVPC shortly after startup. Environmentally driven quality
specifications for fuels are compelling applications of CMVPC and broader CIMFUELS
technology.
Once dynamic performance claims for capacity, yield and operating costs (by closer approach
to limits) are widely accepted, CMVPC will influence process design tolerances and sizing of
new plants. The broad Chemical Engineering connection between process design and process
control will strengthen.
The next NPRA Computer Conference, Nov 11-13, 1996 in Atlanta, will feature a half day
session on Process Control Megatrends, with speakers from four oil companies. The 650 expected
attendees will find out more about achievements, problems and trends from this vital technology.
References
1. Qin, S. Joe, Badgewell, Thomas A., “An Overview of Industrial Model Predictive Control Technology”, AIChE
Chemical Process Control - V Conference, Tahoe City, CA, 11 Jan 96.
2. HPI Market Data, Hydrocarbon Processing, Gulf Publishing Co., 1994 & 95.

CIMFUELS Editorial for FUEL T & M, November/December 1996, p12, 14
Reconciliation, Learning, Improvement - RLI
Vice President
Houston, Texas
Since inauguration of CIMFUELS editorials in the Jul - Aug 95 issue of FUEL, we have
attempted to describe its role and contribution to competitive manufacturing of fuels and
petrochemicals, particularly clean fuels like RFG, CARB2 and LSD. Recent editorials described
the five basic money making functions of CIMFUELS: Performance Measures (PM), Integration
(IT), Advanced Dynamic Process Control (ADPC), Operations Optimization (OOPT), and
Scheduling (SCH).
Now we turn from technical areas to some deeper principles of good management practice that
are employed with successful CIMFUELS. This issue will focus on RLI - Reconciliation,
Learning and Improvement.
Reconciliation
As reconciliation is basic to human relationships, the scientific method and checkbook
balancing, it is also a basic ingredient for useful CIMFUELS.
Plant data (the facts) is full of discrepancies, errors, inconsistencies, redundancies,
inaccuracies, conflicts, transients, misunderstandings and lies. Things are suspect, they don’t add
up, check out, make sense, jive, seem right, match experience, correlate well, go in the right
direction, or fit models. Plant people spend a significant portion of their time verifying and
reconciling facts and data into something believable, accurate, reliable, meaningful, truthful and
useful, which then becomes what we call information.
The basic reconciliation idea is to devise methods and policies to deploy mathematical
techniques of CIMFUELS as a tool to do data reconciliation work easily to create information.
Large scale open equation SQP solvers for profit optimization by operating condition adjustment
are equally useful for data reconciliation by parameter adjustment. However, we should
remember the basics of the scientific method (the Greeks, 4th century BC) at work here:
hypothesis of theory - model, experimental tests to verify or refute the theory - model and
analysis to accept, reject or improve the theory - model. Do analysis properly before synthesis.
The scientific method has not yet been fully computerized (even with AI - neural nets). Human
thought (art and/or science?) will remain a critical ingredient of data reconciliation as long as
people set the objectives and values of the inquiry endeavor.
There are well established methods for adjusting massive amounts of raw, inexact
measurements to satisfy complex relationships humans choose to impose for some reason or
belief, such as mass balance closure for weight flows, volume balance closure and density
properties for volume flows, kinetics and equilibria for component balances, energy balances for
temperatures, momentum balances for pressures and optimization money balances for
intermediate transfer prices and value added. In each case, definition and adjustment of empirical
factors (rate constants, mass/heat transfer coefficients, efficiencies, resistances, polynomial

regression coefficients, neural net weights, functional forms and limit values) requires some
human involvement (art and/or science?).
Automatic adaptation practice for reconciliation remains rather ad hoc. People must select the
relationships we wish to impose upon the data in order to convert it into something we are willing
to value and use as information. Reconciliation for its own sake has little or no value.
Reconciliation to create information should strengthen understanding for sound decisions (by
people and CIMFUELS functions), and clearly relate to (nay impact) consensus among people for
business (= financial) success.
The mathematical power to reconcile data according to any rules and relationships we wish to
impose is now at hand, but people have difficulty knowing what to reconcile, why they should
reconcile, what relationships to honor and how to determine the value in order to justify
reconciling in the first place. One reason for this situation is that people do not connect
reconciliation to higher purposes well; they do not learn from reconciliation.
Learning
CIMFUELS learning is manifested in its 1) models and 2) model improvements.
We must model 1) how the process works, 2) what the financial purpose of the process is, 3)
what the rules and limits are and 4) what the consequences and penalties will be for breaking the
rules or violating the limits. Plant people have always modeled plants, improved these models and
learned from them. They have also specified the purpose of the plant and set rules and limits upon
it. Some have experienced the penalties for violating the rules. However, people forget, change
and depart.
CIMFUELS provides the means for the permanent plant to model 1) how the process behaves
(not necessarily how or why it works), 2) what the financial objective is, 3) what the rules and
limits are and 4) the penalties for limit violation. As plant people learn more about the details,
accuracy and significance of these model components, they should encode them in CIMFUELS
document storage and retrieval, and better yet, into the active CIMFUELS functions (PM, IT,
ADPC, OOPT, SCH). This allows the permanent plant CIM to become the repository of know-
how and experience, which becomes smarter over time and is regularly used.
Remember, memory is part of learning. CIM should remember plant performance with past
feeds, catalysts, modes, economic situations, discoveries and mishaps. Since it is possible to learn
much from mistakes (probably the only merit of a mistake is what we learn from it and the only
way to really learn is from mistakes), there is value in tight linkage between errors and mistakes
and CIMFUEL learning.
Further, reconciliation by people and CIMFUELS provides a powerful means for rational
model improvement. This is how the permanent plant becomes a learning system, with a
permanent built in capability to learn. This capability requires active human leadership and
involvement using reconciliation technology.
People still have difficulty knowing how to use CIMFUELS learning well and how to justify
it. One reason is that they do not improve from their knowledge. Learning for its own sake is
appropriate for academia and personal leisure, but not for business. Business learning must serve
a business purpose and be deployed for improvement, because corporations are instituted to
create profits.

Improvement
The quality revolution of Dr. W. Edwards Deming and Dr. J. M. Juran in the 1980’s taught the
importance of continuous improvement in order to make more money and even to survive.
Improvement must be regular and pervasive. Plants must improve their products, processes,
procedures, control systems, people, models, CIM systems, customer satisfaction and competitive
performance every day.
Consider one example: the US industry effort to comply with CAAA90 to make RFG without
degrading the remaining conventional mogas pool below the 1990 baseline. That’s quality
improvement! Clearly manufacturing improvement by adaptation is a fundamental requirement
for business success. The basic way to adapt properly for improvement is to align learning with
risk for optimum expected financial performance. Of course this is just as true for CIMFUELS as
it is for people.
So now we have RLI, with or without CIMFUELS. Success and survival require performance
improvement. Performance improvement (I) is based on relevant learning (L) that starts with
good reconciliation (R) of the data into information. So RLI is the link between data and success.
Business leaders should study the role of CIMFUELS for the RLI activity in their
manufacturing operations. Why bother? What is the incentive? Reconciliation alone is worthless.
Reconciliation for learning alone is worthless. However, proper use of RLI throughout the
CIMFUELS functions in concert with people and decision making can capture and sustain net
benefits exceeding 0.1 USD/bbl crude, and might even approach 0.2 for some refiners! Might
even be essential for long term survival! Recommend you connect R to L to I well. Then you can
connect data to profits. RLI provides the venue to guide the proper specification of model
relationships to be imposed when upgrading inexpensive raw data into valuable information. That
would be useful reconciliation.

CIMFUELS Editorial for FUEL Technology & Management, January/February 1997, p14 - 15
NPRA Computer Conference
Consulting Engineer & Vice President
Houston, Texas
……………………………………………………………………………………………………….
New techniques to determine the financial performance of computer systems
technology are emerging.
……………………………………………………………………………………………………….
The National Petroleum Refiners Association provides the premier annual international
conference on CIMFUELS and CIMCHEM technology and business. The 38th
NPRA Computer
Conference was held November 11 - 13, 1996 in Atlanta. (The first was held in 1958!) There
were 550 attendees this year from oil refineries, petrochemical plants and suppliers/vendors
around the world. Since attending this conference for the first time in 1972 and joining the NPRA
Computer Application Committee with 19 operating companies and 17 suppliers in November
1995, I have been privileged to see this group in action, growing and maturing significantly. The
leaders of CIMFUELS are involved in this conference.
The centerpiece of this conference is the selected papers and presentations by operating
company representatives, often co-authored by suppliers these days, on technical and business
accomplishments, experiences and needs.
Process Control Megatrends.
Process computer control has been a bedrock topic of NPRA Computer Conferences for many
years. Traditionally this has included basic and advanced dynamic control, multivariable control
(CMVPC), on-line optimization (CLRTO), scheduling, online integration and performance
measures. These are the five active functions of CIMFUELS that make money. Lately some have
excluded scheduling and integration from “process control” but as they go closed loop they
become basic functions of “process control”. This year the committee decided to invite four
speakers to take a broader view and report on megatrends in this burgeoning area. They came
from Ultramar, BP, Sunoco and Mobil; all experiencing profound changes: 1) merger, 2)
acquisition/shutdown, 3) public spin-off and 4) sell off/acquire/consolidation. This half day
session proved to be the highlight of the conference.
In 1982, John Naisbitt wrote MEGATRENDS, gave ten; nine were right, one remains open. In
1990, John Naisbitt & Aburdene wrote MEGATRENDS - 2000, gave ten new; three were right,
seven remain open. I offered these Process Control Megatrends:
1. Fast - cheap computers
2. Fabulous software
3. Strict quality & environmental compliance
4. Multivariable dynamics
5. Rigorous profit optimization
6. On-line scheduling
7. Real-time process unit economics
8. Integration: people, processes and computers for the business purpose

9. Profit performance
10. CIMFUELS an established, distinct, mature business
Ultramar, BP, Sunoco and Mobil presented compelling descriptions of their process control
activities, accomplishments and plans which illustrate the megatrends to the trained observer.
BP (1) described their “Vision for Optimal Commercial Refining” developed since 1994 for
the next ten years with input from 23 designated suppliers and consensus from eight BP refinery
managers worldwide. The reason for this vision in their belief the “average potential maximum
benefit through process control and optimization is 0.3 - 0.5 USD/bbl crude and through decision
support is an additional 0.1 to 0.2 USD/bbl crude”. This confirms previous reports (2, 3, 4).
Sunoco (5) described “Design and Integration Issues for Dynamic Blend Optimization” that
shows how constrained multivariable predictive control and nonlinear multiperiod optimization of
a complicated mogas blending operation connects process operation for components with product
tankage and marketing logistics. Tangible benefits of about 0.06 USD/bbl product (0.08
CDN/bbl) were realized. Sunoco claimed “economics drives the production of each blend.” This
simple statement remains an elusive goal for many mogas & middist blenders. This was another
breakthrough paper from this leader in applying CMVPC and CLRTO for financial gain, high
Solomon benchmark ranking and a Smithsonian Institute Award for technology innovation (6).
Mobil (7) described results to date from their CIMFUELS master plan at Jurong Refinery,
Singapore, built upon CMVPC of all major processes and progress toward CLRTO. They
revealed what they are doing, why and how without compromising their proprietary position.
The audience detected some big trends underway. Old barriers to quality measurement are
falling. New techniques to determine the financial performance of computer systems technology
are emerging. In order to harness computers to do our bidding to run a refinery we people must
tell the computer:
1) how the plant works = process model,
2) purpose and objective of the process = profit performance model,
3) rules = limits,
4) consequences for breaking the rules = penalty model for violating limits.
If we do these well, it will work well; if we do not, it will not. Computer technology has taught us
humans that we have not always done things the best or proper way, so we reengineer our
methods and work processes to get them right before we can deploy CIMFUELS well. Often we
do not have our act together, do not have sufficient consensus on our values and goals. Humans
set values, not computers. That portion of process control will always remain an art. If things do
not work well, it is never the computer’s fault. Pogo told us “we have met the enemy and he is
us”. Those who know their enemy are securing big victories.
……………………………………………………………………………………………………….
Computer technology has taught us that we have not always done things the best or
proper way, so we re-engineer our methods and work processes to get them right before
we can deploy CIMFUELS well.
……………………………………………………………………………………………………….

Other Technical Sessions
Eight more technical papers and presentations were given to the entire conference in sessions
on Human Aspects of Automation (desk top PC/workstation networks with OLE for Process
Control - OPC), Optimization, Planning and Scheduling (mogas blending and lube scheduling)
and Modeling and Simulation (rigorous and empirical approaches).
Q & A - Integration
One morning was devoted to a seven panelist question and answer session on Integration of
Technical and Business Applications in the Refining and Petrochemical Industries. They had
prepared questions and answers. They entertained spontaneous questions and comments from the
audience throughout. The many important and deep issues discussed and debated here will be
published soon. (Unfortunately one vendor panelist broke the rules by engaging in overt
commercial slide presentations of his company’s offerings; his abuse of privilege and fairness
prompted a severe reprimand to tarnish his reputation).
Operating company senior management struggling to survive, compete and prosper and having
particular difficulty coping with fast changing CIMFUELS technology and quantifying financial
benefits (to be gained or already gotten) would gain from this Q & A.
Breakouts
The conference included the popular new Information Exchange Breakout Sessions one
afternoon. The six sessions, each repeated twice, covered: Computer-Based Training, Modeling,
Planning & Scheduling, Expert Systems/Neural Networks, Multivariable Control and Plant
Information Systems. Lively but professional dialog among the operating companies and their
suppliers was well done in the sessions I attended. Many found participation in these breakouts
very worthwhile unique experiences.
Showcase
For a second year the well organized supplier Showcase replaced traditional hospitality suites
between the sessions. Twenty six product, service and technology solution suppliers and vendors
had modest booths with competent staff, brochures and computer demos. Dramatic improvements
in technology and business sophistication were evident. The role of suppliers has naturally
strengthened compared to the “do - it - themselves” groups inside large operating companies as
CIMFUELS technology and business matures. Piecemeal low cost competitive bidding for
products, tools and services based on simple features (fast, rigorous, easy to use, experienced) is
rapidly evolving toward solutions partnering for sustained business performance. CIMFUELS
solutions suppliers incorporate the proper products, tools, services, know-how and technology for
successful results (profit).
Featured Speaker - IBM Olympics
The consensus conference highlight was the luncheon speech “IBM’s Olympic Challenge” by
Roland Palmich, director of building and operating the entire computer support system for the
1996 Summer Olympic Games in Atlanta. He supported the 1.8 billion dollar enterprise to
conduct that two week affair for the 197 nations of the world. He described the role and
architecture of the real-time network of computing systems to support ticketing, scoring, results
reporting to wire services and news, credentials, security, scheduling and accounting. The
concepts and technology he used are quite similar to those needed for CIMFUELS and
CIMCHEM. The attendees got a good picture of our future.

As Shell Oil and Texaco ponder merging refining and marketing in the United States, as
Ultramar and Diamond Shamrock proceed to merge in California, Texas and Quebec, as Agip
copes with reducing sulfur in Italy, as Idemitsu and Nippon Oil work together to revive refining
margins in Japan, as refiners like Indian Oil, Aramco, PDVSA, Petronas, Petrobras, Pemex,
Caltex, Yukong and Marathon set their investment strategies, as the HPI competes to manufacture
cleaner fuels (RFG, CARB2, LSD), lubes, aromatics, olefins and polymers safely, CIMFUELS
technology business will come to dominate the business thinking of senior management along
with catalysts, processes and people. The NPRA Computer Conference covers that megatrend.
You should be represented there. Why? The net payouts are big, costs are vanishing, and risk can
be minimal.
References:
1. Schmotzer, R. E., V. Woyke and D. H. Richards, “A Vision for Optimal Commercial Refining”, Paper CC-96-129,
NPRA Computer Conference, Atlanta, 13 Nov 96.
2. Gonzales, R., “Implement Information Technology Tools Throughout The Refining Industry”, FUEL T&M, V6, n6,
Nov 96, p44.
3. Latour, P. R., “APC & RIS: Keys to Successful Business in the Reformulated Era”, FUEL Reformulation, V2, n2,
Mar 92, p14.
4. Latour, P. R., “Mission: Plan to use RIS/APC for Manufacturing RFG/LSD”, FUEL Reformulation, V4, n4, Jul 94,
p20.
5. Vermeer, P. J., C. C. Pederson, W. M. Canney and J. S. Ayala, “Design and Integration Issues for Dynamic Blend
Optimization”, Paper CC-96-130, NPRA Computer Conference, Atlanta, 13 Nov 96.
6. Pederson, C. C., D. R. Mudt, K. Bailey, and J. S. Ayala, “Optimize Hydrocracker Product Profits with CLRTO”,
FUEL T&M, V6, n1, Jan 96, p50.
7. Wong, K. T., and P. Ling, “Towards Fully Integrated Refinery Operation”, Paper CC-96-131, NPRA Computer
Conference, Atlanta, 13 Nov 96.

CIMFUELS Editorial for FUEL Technology & Management, March/April 1997, p14-15
Commercial Practice - Tools vs. Solutions
Consulting Engineer & Vice President
Houston, Texas
……………………………………………………………………………………………………….
The commercial practices of the CIMFUELS business in this unstructured, fast
changing, high technology global business has suffered unduly from inadequate
distinction between tools and solutions.
……………………………………………………………………………………………………….
Continuing recent editorials on good management practice, this month we analyze the nature
of commercial offerings and practices between CIM providers and their fuel/petrochemical
operating company customers/clients.
History
Since the inception of the use of computers in the 1960’s the fuels manufacturing industry has
always experienced a blend of tools and solutions. Tools are usually developed first, then
combined and customized for commercial applications and working solutions. Inadequate
appreciation of the difference between the two and their different customer types has led to
confusion and caused many improper applications and failed endeavors.
With a plethora of powerful tools and technologies developed in the late 1980’s, a renaissance
of profitable CIMFUELS solutions is now underway around the world, reminiscent of the
performance based process computer control solutions deployed during that decade with tools of
the 1970’s. Meanwhile better tools developed during the 1990’s promise even higher
performance solutions soon.
Tools
Products, packages and methods are generic technical things that do not create value by
themselves. CIM tools are hardware, software and services. Hardware tools are computers,
analyzers, instruments, valves and networks. Software tools are models, algorithms, data bases,
controllers, optimizers, schedulers, interfaces, displays, modules and packages. Service tools are
methodology, know-how, techniques, skills and experienced people.
CIM tools are like groceries, home building supplies (lumber, wiring, concrete, paint), fabric
and thread, auto parts and musical instruments.
The customer for CIM tools is a system builder, integrator, maintainer, solution provider; not
the manufacturing plant operator system user. The customer of tools is a value added reseller, a
VAR. Large opcos have had their own central engineering and IT staff groups acting as VAR’s.
CIM tools are sold on their features: rigorous, accurate, fast, large, transportable, easy-to-use,
reliable, capable, good, the best, modern, high tech, low cost, low maintenance. They are usually
offered as “technology”; they are “technology driven”. HOW they work is emphasized over
WHAT they do for manufacturing fuels.

The purpose of a tool is to enable its customer VAR to use the tool for a higher purpose, to
solve a business problem by building a profitable system.
Solutions
CIM solutions are rather different. They are working computer based systems that perform a
useful (profitable) function integral to the plant process and organizational manufacturing
operation, contributing to their financial success. These system solutions are usually online
information systems and closed loop controllers/optimizers/schedulers that do something to make
more money. They always include hardware, software and service tools as components. They are
“alive” and changing. They invariably include “a customized model”.
VAR’s are like chefs, home builders, tailors, car dealers and orchestras.
CIM solutions for profit are like restaurants for a dining experience, house builders/realtors for
a home, clothing stores for a wardrobe, automobiles for transportation, orchestras for
entertainment.
The customer for CIM solutions is the operating company profit center that manufactures
fuels. The solution provider is the system builder, integrator, maintainer VAR, whether an opco
staff group or outside company.
Solutions are sold on performance, results, sustained benefits/risk, profit contribution. They
are purchased on WHAT they do rather than HOW they do it. A VAR solution business is built
on providing attractive benefit, price, cost to deliver and managed risk (that is profit for both). A
VAR and his client are interested in how to identify, capture and sustain significant economic
benefit from CIM (preferably for each other). Solution VAR’s are client driven, technology
driven and financially driven; combined in balance with no particular priority.
Commercial Arrangements - Tools
Tools and products are sold for a fixed price or right to use is licensed for a fixed period or in
perpetuity. They are often offered in combination to satisfy competitive bid specs for specific
opco projects selected for the lowest price which “satisfies the requirements”.
Tool suppliers believe that their profitability comes from wide markets, high volume sales to a
large number of customers. They naturally think the tool business is inherently more profitable
for them because tool making is what they do best. They prefer not to assume the risk that their
tool may be used improperly (unprofitably) in particular customized solutions.
Commercial Arrangements - Solutions
Solutions are sold on a cost plus basis, service fee basis, or right to use is licensed on a value
added basis. Sustaining business partnerships are built on performance; percentage splits are
based on benefit derived or net profit created along with the risk assumed between the parties.
Shared risk/shared reward (SR)2 arrangements for performance based licensing of technology
based profit solutions depend upon clearly defined performance measures. The value of the
provider’s know-how is manifested in his willingness to assume commercial risk for successful
performance. The client’s aversion to risk is manifested in his willingness to grant adequate
authority and reward incentive to his solution provider in order to secure his share of increased
tangible profits. Successful CIMFUELS profit improvement outsourcing programs are built on a
fair (SR)2 client/provider win/win relationship rather than a competitive lowest price bid spec
customer/supplier contract arrangement with penalties.

Successful CIMFUELS solution business arrangements recognize the merit of profit
incentives for the provider. People do not select a suit, automobile, restaurant, home, mutual fund,
surgeon of university by lowest cost competitive bidding. Corporations do not select accounting
firms, legal help, a banker or management consultants on lowest cost competitive bidding. Fuels
manufacturers no longer select CIMFUELS solution providers by low cost competitive bidding.
They select on their opinion of the highest expected long term net present value profit, duly
accounting for risk and assurance of lasting success.
Solution suppliers believe their profitability comes from enduring relationships with selected
compatible clients over a long time. They think the solutions business is most profitable for them
because what they know best is how to customize profit making CIM solutions for their selected
clients. They believe their knowledge and experience mitigates their risk of failure so they are
more willing to assume roles of authority for success if they gain sufficient rewards, profits. They
cash in on the principle that risk is mitigated by its mirror image: knowledge and experience.
Lasting CIM solution business partnerships properly align knowledge with risk; performance
with reward.
In-house or Outsource?
The biggest question for opco management seeking profitable computer integrated
manufacturing is what to do with their own company tools and solutions and what to obtain from
outside tool suppliers and solution providers. In earlier times the large operating companies
created research and engineering staffs to develop their own tools, technology and application
solutions (the do-it-themselves style) to improve competitive advantage because that was the only
feasible way before CIMFUELS was an established business in its own right. They had central
R&D and engineering groups and specialists, and plant organizations with development and
maintenance capabilities.
As CIM matures to a distinct stand alone business, separate from manufacturing operations
expertise (some doubt this is possible), manufacturing companies are naturally faced with an
ongoing / interesting / important / fundamental make or buy decision: do-it-all-themselves (what
does “all” mean?), obtain tools outside and do “all” VAR solutions themselves, or outsource part
or “all” CIM activities to VAR business partners. Some opcos have licensed the tools they have
developed through VAR’s; some have even gone into the VAR solutions business. With
reengineering, restructuring, reassessment of core competency, redefinition of businesses and
markets, rapidly changing CIM technology of profound significance and entrepreneurial CIM
practitioners (tool and solution builders) separating from opcos, fuel manufacturing businesses
should be continually alert to the technical, cultural and business trends as they affect this basic
make/buy question and their relationships to CIM suppliers/providers.
Fragmentation and Conflicts
The CIMFUELS business is currently experiencing excessive confusion and fragmentation.
On the customer/client side, if a VAR/opco considers a tool supplier as a solution provider (or
considers a grocery store to be a restaurant), a big conflicting disconnect occurs. Likewise when
the VAR/opco considers a solution provider as a tool supplier (or considers a restaurant to be a
grocery store), a big conflicting disconnect occurs. On the supplier/provider side, if a tool
supplier represents himself as a VAR solution provider to a VAR (who will consider him a
competitor) or an opco (who does not want tools), a major conflicting disconnect occurs.
Likewise when the solution provider represents his offering as a tool (to an opco who does not
want a tool) or to a VAR (who is in fact a competitor), a different conflicting disconnect occurs.

A simple example will illustrate this common problem. Consider a salesman of brain surgery
tools (scalpel, scissors, forceps, suture, needle) made of the finest steel, sharp, rust proof, pearl
handled in a lovely leather case. Who is the customer? A brain surgeon - yes; but surely not a
brain tumor patient! Promoting tool features to senior opco business leaders seeking profit
improvement solutions generates a similar negative reaction disconnect. Failing to properly relate
your offer and role to the right customer leads to trouble. Offer tools to VAR’s; provide solutions
to fuels manufacturers.
Beyond all this, CIM tool providers and solution suppliers deal with each other in project
cooperations, prime/sub contracts, strategic alliances and even mergers and acquisitions. It is
often not clear who the competitors are or who they will become. Confusion abounds (so what
else is new?).
The commercial practices of the CIMFUELS business in these unstructured, fast changing,
high technology global business climate times has suffered unduly from inadequate distinction
between tools and solutions, identification of customers/clients and their needs, definition of
business roles and objectives, and measurement of financial performance. However we see signs
of business maturity between opcos and their suppliers/providers (tools/solutions) as the
substantial critical value of CIM proves itself in sustaining competitive profit performance for
manufacturing ever cleaner fuels beyond 2000. When benefits from comprehensive CIM
solutions using the right tools reach 0.5 to 0.8 $/barrel of crude oil, manufacturing survival is at
stake.
Conclusion
To cross the bridge, each party should 1) know yourself, core competency, role, offering; 2)
know your customer and his needs; 3) know your competition and be different; and 4) know third
parties as potential suppliers/collaborators/customers. One forecast will prove true, the
CIMFUELS business in the next millennium will differ from the 90’s. It will play a dominate role
in optimally meeting future fuel quality standards, profitably. Stay tuned.

CIMFUELS Editorial for FUEL Technology & Management, May/June 1997, p10
Time Cycle Management
Pierre R. Latour
Vice President (Ret.)
Aspen Technology Inc.
In recent issues we moved from the five technical functions of CIMFUELS: PM, IT, CLRTS,
CLRTO and CMVPC, to some deeper principles of good management practice that are employed
with successful CIMFUELS, like RLI (Reconciliation, Learning, Improvement) and Commercial
Practice - Tools vs. Solutions. This time we describe another one - Time Cycle Management.
Description
Time Cycle Management, TCM, incorporates the traditional Plan, Do, See Loop - PDSL of
every day activity into the design and use of modern CIMFUELS systems by all levels of the
plant and company. The Japanese are famous for their appreciation and use of PDSL in their
work habits. PDSL was probably also among the ideas taught by the ancient Greeks. Plan first,
then act, then see what happens. Analyze, then synthesize, then learn. Time Cycle Management is
a principal technique to properly integrate CIMFUELS functions with time: connecting the
present with the past and the future.
Time Cycle Management for a fuels/petrochemical complex is commonly described (1) by a
circular connection among six different functions performed around a time cycle:
1) planning/goal setting which feeds,
2) scheduling which feeds,
3) detailed optimization which feeds,
4) control execution which feeds,
5) response measurement which feeds,
6) evaluation/appraisal which feeds, 1) planning/goal setting again.
These activities alternate between analysis, synthesis and learning. TCM provides one (of several)
functional descriptions of plant organization activities as well as CIMFUELS decision activities
and information flow.
The Time Cycle for manufacturing plants must be designed and managed before it can be
properly used. Its parameters should be quantified as much as possible. The purpose of the
PDSL’s must be clear. The consequences must be clear. The functions must be clear. The
information flows must be clear. The models and assumptions must be clear. The time horizons
forward into the future and backward into the past should be specific. The speed and frequency of
the cycle must be clear. (Speed is set by the time to execute each function and proceed around
one time cycle; frequency includes delay time between cycle restarts). The interface connections
between and among related Time Cycles (customers, suppliers, environment, corporation,
employees) must be clear. The mutual influence and dependence among related time cycles
merits design and management.
Each subsystem also incorporates a PDSL into its own TCM. For example CMVPC for
execution is built with methods to determine short term plan targets, set optimal move sequences,
secure proper output moves, measure response performance and evaluate discrepancies to revise
input to the next PDSL cycle for Constrained MultiVariable Predictive Control. Likewise,

successful CLRTO is built with goal setting (plan), optimization (do) and parameter updating
from response measurement (see) for Closed Loop Real Time Optimization. Every feedback
controller is built with a PDSL. Every fuel/petrochemical manufacturing process is run with its
PDSL’s. Clearly performance of the plant and business depends critically on the performance of
its associated TCM. Plants and businesses do not run without one.
Use
In prior decades real time data bases supporting CIM held only past history data (the facts);
but current supporting data bases include a variety of future information types (conjecture) for
TCM like contract commitments, forecasts, scheduled commitments, probable outcomes,
tentative plans, trial balloons. CIM functions and people work on this information to improve the
future. All functions and decisions must do this; that is all we can ever do! No wonder TCM is
central to CIM.
All levels of people in an organization use their own style of PDSL for their personal TCM
activities. Senior managers tend to emphasize planning over longer time horizons whereas
process operators tend to emphasize remembering troubling past experiences. Both should
probably place more emphasis on designing and using their complete TSM and better connecting
the longer term future and the longer term past with their present actions. Further they should
consider better connections between their individual TCM to each other’s TCM, to the TCM’s of
other people and processes in their plant and business and to the TCM’s of customers and
suppliers. Quantification is central to well operating TCM’s for people, plants and CIMFUELS.
Some (people and CIM functions and their TCM) emphasize safety or capacity or quality or
yield or inventory or energy or emissions or reputation or speed or reliability or accuracy or ease
or customer satisfaction or compliance or costs or profits in their TSM goal setting. Misalignment
of goals causes conflicts among some (people and CIM functions and their TCM). Unity of
purpose enhances performance and the chances for success among some.
Of course the TCM of the CIMFUELS system should harmoniously support the TCM of the
business, plant and organization. When it does, profitability invariably improves, when it does
not, profitability invariably declines. That is why people should learn to work on two tracks:
1) operate yourself with good TCM and
2) operate yourself to continually rebuild, improve and interface your personal TCM to others
and your environment.
That is also why you should
1) operate your process plant with good TCM and
2) continually rebuild, improve and interface your process plant TCM to connected processes
and its environment.
That is also why you should
1) use good TCM technology in your CIMFUELS system for yourself and your plant and
2) continually rebuild, improve and interface the TCM in your CIMFUELS system to
connected TCM’s and its environment.
Conclusion
Proper design and profitable use of CIMFUELS technology must incorporate well established
principles and practices of appropriate PDSL in Time Cycle Management so that CIMFUELS is
an integral and indispensable part of the plant, organization and business. That is how
CIMFUELS can enhance profits in a major way. I suspect the potential for manufacturing clean
fuels using excellent TCM with CIMFUELS exceeds 0.5 $/bbl crude.

On the other hand if you neglect good use of TCM you will surely fail. This neglect explains
many of the CIMFUELS disappointments. Intellectual order and discipline with TCM beats chaos
and anarchy without TCM every time.
Reference
1. Latour, P. R., "Role of RIS/APC for Manufacturing RFG/LSD", National Petroleum Refiners Association 1994
Annual Meeting, San Antonio, TX, March 21, 1994.

CIMFUELS Editorial for FUEL Technology & Management, July/August 1997, p10, 12
Intangible Benefits? Make Tangible!
Dr. Pierre R. Latour, PE
Consulting Chemical Engineer, SR2
Houston, Texas
……………………………………………………………………………………………………….
Since the purpose of clean fuels manufacturing is to add value, the purpose of people
assigned to manage these processes is to assess and improve the proper value to these
processes.
……………………………………………………………………………………………………….
In 1996 these editorials described CIM technology for fuels and petrochemicals. In 1997 they
evolved to management and business matters of CIMFUELS like Tools vs. Solutions, Time Cycle
Management and Decision Support. This time we address a serious CIM solutions business
practice weakness: making intangible benefits tangible.
What is the value of information? We have already shown how that depends on what is done
with the information to add value, create wealth, increase profits. Performance of CIM functions
that use information create value. How do we quantify the benefits of CIM functions?
Situation
CIM activities and investments for business should be (and usually are) justified like all other
business activities and investments, with an estimated forecast of performance and benefits.
Benefits are usually described in two categories: 1) tangible, quantified physical improvements
with their corresponding financial credits and 2) intangible areas of perceived real merit that are
not quantified financially. The latter are not quantified because either they cannot be, they might
be but we do not know how, their value is controversial or their financial size is too small to merit
the effort.
The tangible benefit category usually quantifies system performance in terms of product
yield/quality, plant capacity and operating costs. Combined as a gross financial benefit cash flow
against system installation and support expenses cash flow gives profit cash flow by difference.
The intangible benefit category usually lists some nice traditional “motherhood” areas like
smoother operation, improved flexibility, quicker response, better utilization of people, improved
customer satisfaction, less likely safety mishaps, more accurate and timely information, better
decisions, ease-of-use, better environmental compliance and closer management coordination.
Upon deeper analysis, all of these can eventually be modeled quantitatively for their impact on
strategic financial performance.
People Set Value
There is one activity that computers cannot ever do better then people without human
leadership: assessing the monetary value of things. Every store has price tags on merchandise,
shoppers determining values and buyers/sellers negotiating terms of transfer. Every restaurant
menu describes dishes for a price and customers study it to determine value. The value/demand
relationship for everything is worked and set by people globally, daily.

Every refinery has a money balance superimposed by people (society, customers, investors) on
its mass, energy and momentum balances, giving the value added chain to the fuels
manufacturing transformation process steps. Since the purpose of clean fuels manufacturing is to
add value, the purpose of people assigned to manage these processes is to assess and improve the
proper value to these processes. People are in the business of quantifying that value and modeling
the influences that effect and optimize the value added. Money is the measure for keeping score.
Quantify
Profitable commercialization of CIM technology has been severely hampered by lack of
attention to proper modeling and quantification of the identified intangible benefits, making them
tangible. Said differently, profitability of fuels manufacturing has been severely hampered by lack
of attention to proper modeling and quantification of the performance contribution from
commercial CIM technology. This is a human failure, not a shortcoming of computers or CIM.
Solid commercial (as distinguished from technical) CIM progress comes from critical attention to
separating the real value which ultimately affects yield value, capacity value and operating costs
value as manifested somewhere in the financial accounting performance of the business, from the
nice sounding adjectives for intangibles that do not really make any money.
Genuine CIM successes invariably report clear, measurable, verifiable profit performance.
CIM failures invariably cannot report clear, measurable, verifiable profit performance. So what is
the lesson? Work better to quantify those intangible benefits (sometimes they are as large as the
initial tangibles). Then capture and sustain them. Be clear on now to keep score for success.
What makes the basketball a great game is widespread agreement on the criteria for a three
point basket and a two point one. What makes baseball a great game is widespread agreement on
the criteria for a home run and a foul ball; a run scored and a tagged out. What makes the
Olympics long jump a great event is widespread agreement on the criteria for an overstep foul
and a legitimate jump. What will make CIMFUELS great is widespread agreement on the
methods for quantifying its financial performance.
Dynamic Performance
Recent developments of CLIFFTENT technology (1) provide the rigorous solution to an
important long standing open question: how to quantify the financial benefit from reduced
variance, less uncertainty, smoother operation, more uniform product quality, steadier approach
to limits/specs/safety margins and improved dynamic performance of fuels manufacturing plants?
Employing the CLIFFTENT function for unit profit (versus each dependent response variables of
interest) along with their statistical distributions allows determination of these financial benefits
and proper (optimal) setting of targets on the dependent response variables for CIM functions like
multivariable controllers (CMVPC), nonlinear optimizers (CLRTO), schedulers (CLRTS) and
planning LP. CLIFFTENT shows the need to model the unit profit function on both sides of the
spec (the process credit for closer approach and the penalty for exceeding). This method allows
easy quantification of many former intangible benefits to correctly assess the potential and
realized financial improvements. (This financial quantification step has been missing from the
quality revolution of the 1980’s inspired by Demming, Juran and Crosby.)
Air Quality
The US EPA, OTAG and state environmental agencies are attempting to build multivariable
dynamic control and optimization systems to manage and then “improve” air quality as a means
to improve human health (cost effectively). CIM technology and systems engineering deployed
for fuels and petrochemicals manufacturing (as well as all manufacturing) could contribute to the
success of these government efforts in many ways.

……………………………………………………………………………………………………….
The lesson is for humans to model science and to debate and assign the monetary value
for the benefits and costs. Then the corresponding optimum tradeoff targets and net
benefit to society can be calculated.
……………………………………………………………………………………………………….
For example the developments and experiences for quantifying common intangible benefits
and properly setting target specs with methods like CLIFFTENT show the ingredients and way to
properly tradeoff the benefits from improved air quality (human health credits must be modeled
and quantified) against the costs, accounting for unmeasured disturbances and
uncertainty/reduced variability. The lesson is for humans to model the science and to debate and
assign the value (monetary) for the benefits and costs. Then the corresponding optimum tradeoff
targets and net benefit to society can be calculated. People should avoid ad hoc arbitrary setting
of specs and limits based on verbal qualitative (sometimes emotional) arguments, without proper
modeling analysis and quantitative valuation.
Conclusion
In the end we are always faced with a two part modeling question; one for science and one for
art. We must model the physics, chemistry and engineering of the process, plant or system
behavior. We must also quantify the human values/desires/goals/needs/wishes that connect our
opinions to desirable and undesirable performance outcomes of the system. Currently we do this
quantitatively with money and qualitatively with adjectives (some say political debate has too
much hot air). Since computers (and most people) cannot act well on the nonquantified
adjectives, we should naturally endeavor to quantify how much we want what we want in order to
harness the computer (and most people) to do our bidding well. This full quantification of
objectives and benefits is central to CIMFUELS success; and perhaps to most other successes as
well.
There are numerous CIMFUELS examples where intangible benefits were quantified and
found to equal or exceed the tangible benefits. This is why the value of CIMFUELS may be
double the 0.5 USD/bbl crude oil commonly reported (2, 3). This is also why it is so easy to loose
money while refining oil. You must really know what you are doing, why you want to do it, and
what the risks are, quantitatively, these days to create exciting profits from manufacturing clean
fuels. CIM technology for quantifying the intangibles from improved performance is critical to
competitiveness. It may also speed up the resolution of political conflicts in larger areas like air
quality standards, something we all would cherish.
References
1. Latour, P.R., “Process control: CLIFFTENT shows it’s more profitable than expected”, Hydrocarbon Processing,
V75, n12, December 1996, pp. 75-80.
2. Latour, P.R., "Mission: Plan to Use RIS/APC for Manufacturing RFG/LSD", Fuel Reformulation, V4, n4,
July/August 1994, p. 20.
3. Latour, P.R., "Benefits of Modern Refinery Information Systems for Manufacturing Cleaner Fuels", API
Reformulated Fuels Conference, American Energy Week 1995, George R. Brown Convention Center, Houston, TX,
January 31, 1995.

CIMFUELS Editorial for FUEL Technology & Management, September/October 1997, p14
Data Management; Decision Support
Houston, Texas
In recent issues we moved from the five active technical functions of CIMFUELS: PM, IT,
CLRTS, CLRTO, CMVPC, to some deeper principles of good management practice that are
employed with successful CIMFUELS, like RLI (Reconciliation, Learning, Improvement),
Commercial Practice - Tools vs. Solutions and Time Cycle Management. This time we describe
another one - Data Management; Decision Support - DM & DS.
If you attended the World Conference on Refining Technology and Reformulated Fuels in San
Antonio March 18 - 20 and studied all the papers (oil/auto, EPA/OTAG, refining/ethers/lubes,
catalysts/processes, management strategies/international trends) you will notice the compelling
need for proper Data Management and Decision Support to achieve Computer Integrated
Manufacturing of fuels.
Data Management
The duties of top management of fuels manufacturing include the design and maintenance of
the processes, the organization and the data management systems to satisfy customers, comply
with regulations and generate shareholder profits according to their strategic business plan.
One well established segment of corporate data management is financial, which is handled by
accounting. Most fuels operating companies have been extending these narrow data management
capabilities to many other segments of their manufacturing activities in the 1990’s, to form the
bedrock of their real time CIMFUELS practices. They are connecting feed stocks to products,
utilities to maintenance, processes to people, and customers to government for value added -
money.
To start, management must recognize they must create information as well as products in order
to create wealth to fulfill their corporate purpose. They must manage data and the information
derived from the data well. In fact they must develop and maintain methods and systems and staff
so that their organizations manage this data very well (compared to their competition).
Managing data starts with identifying: definitions and meaning, sources and uses,
classifications by types, accuracy and truthfulness, timing and variance, repeatability and
redundancy, dependencies and relationships, assumptions and status, responsibilities and
authority, flow and distribution (measurement/collection/verification/storing/reporting/
maintaining/discarding). Several modern data base management packages will execute data
management as we desire, and guide us to specify what we desire, but identifying the design and
performance remains very much a human endeavor.
Data must be transformed into useful information. People define information and usefulness of
data as it relates to successful performance of the three active CIMFUELS functions described
here last year which make the operating decisions for manufacturing fuels: CLRTS, CLRTO and
CMVPC for scheduling, optimization and control.

Successful businesses have good and improving data management; they consciously manage
their data well to support their business purposes. Unsuccessful businesses have weak and
deteriorating data management; they fail to recognize the fundamental role good data
management plays for success. They naturally fail to understand the corresponding benefits from
good CIMFUELS.
Decision Support
The primary (perhaps sole) purpose of information is to support decisions and actions taken by
people and the three active CIM functions that can make money: scheduling (CLRTS),
optimization (CLRTO) and control (CMVPC). These decision making activities of people and
CIMFUELS must be properly defined by management to allow the company to fulfill its purposes
and meet its goals.
Design, maintenance and improvement of the decision making processes in any company or
plant is critical to its enduring success. Clarification of objectives, roles, responsibilities and
authority is essential. Obviously the decision making mechanism must be clear before its useful
decision support structure can be devised, based on a good management system.
While CIM functions for automatic closed-loop decisions proliferates and extends, the role of
people is changing to cover the weak CIM capabilities of valuing, learning, maintaining,
improving, evaluating, auditing and managing. People remain the leaders, goal setters and change
agents.
Conclusion
These general comments are offered to explain the common frustrations from CIMFUELS
disappointments and failures. These are never the computers’ fault; they are always some
person’s fault. The widespread disappointing financial performance from petroleum refining in
North America, Europe, Russia, Japan, Australia, South America and Africa, for many years,
suggests managers now focus more attention on incorporating what was learned from the quality
initiatives of the 1980’s and the reengineering/restructuring underway in the 1990’s into extensive
and cohesive CIMFUELS systems and organizations built with current and coming CIMFUELS
technology.
This may be the only way to revive profitability from manufacturing clean fuels and basic
petrochemicals which satisfy investors, delight consumers, comply with environmental quality
standards and provide safe and challenging career opportunities.
Make sense? Got a better way? Can you see that proper use of computers for CIM is basic to
survival of your fuels business worldwide?
Proper design and profitable use of CIMFUELS technology must incorporate well established
principles and practices of Data Management and Decision Support, DM & DS, so that
CIMFUELS is an integral and indispensable part of your plant, organization and business. That is
how CIMFUELS can enhance profits in a major way. I suspect the potential for manufacturing
clean fuels using excellent DM & DS exceeds 0.2 $/bbl crude. On the other hand if you neglect
good use of DM & DS you will surely fail.

CIMFUELS Editorial for FUEL T & M, November/December 1997, p14, 16
Benefit Potential >1.0 $/bbl Crude
Pierre R. Latour
Hart’s Fuel Technology & Management Advisory Board Member
and Consulting Chemical Engineer, SR2
The most difficult part of CIMFUELS is identifying, capturing and sustaining significant
economic benefits from comprehensive use of computer related technology to manufacture fuels
and petrochemicals more profitably.
1990
Experience in North America, Europe and Japan during the 1980’s led to a generalized
potential expectation (1-5) for CIMFUELS applications in a typical oil refinery around 1990
given in Table 1. It was thought to be or exceed 0.6 $/bbl crude refined.
Quality
With much tighter and more complex air and fuel quality specifications for a cleaner
environment, the credit for compliance with little giveaway has grown substantially. There is
more customizing of product blend batches for seasons and customer locations. That increases
CIM potential.
Price Volatility
With electronic connection of global financial markets for manufacturing and exchanging
crudes, products, and components, the competitive transients on refining margins have grown
substantially. That increases CIM potential.
Capacity
With little increase in refining equipment size in North America, Europe and Japan in spite of
increased demand and throughputs, processes are now run much closer to safety and mechanical
limits. That increases CIM potential.
Op Costs
With recovery of H2 from fuel gases, cogeneration of electricity and steam and customized
catalysts, optimum management of utilities and operating costs has increased impact on
manufacturing performance. That increases CIM potential.
Customized Components
Rather than making blend components from the processes with fixed qualities for all different
product blends (by just varying the component amounts among different blends), the component
properties are customized by the processing conditions and mode scheduling for each product
blend batch. Interaction between gasolines, diesels, mid distillates, fuel oils, petrochemicals and
lubes are considered, hourly. Real time economics, inventory and future plans are included.
Accurate intermediate stream transfer prices (ISTP’s) depending on the quality, source and
disposition of the intermediate component are needed for component buy/sell decisions and value
added tracking.

These increase CIM potential.
Functions
Much improvement in the assessment of financial performance of the five active CIMFUEL
functions that can make money: PM, CMVPC, CLRTO, CLRTS and INT during the early 1990’s
provides much needed maturity to the CIMFUELS business. The know how to engineer them,
link them, use them and maintain them to run refineries better has strengthened. That increases
CIM potential.
Intangibles
Improved methods like CLIFFTENT (6, 7) to rigorously quantify intangible benefits and the
financial value from improved dynamic performance (reduced variance, smoother operation)
converts these former intangibles into more tangibles. They show the value of intangibles often
exceed the old incomplete tangibles so new tangibles may be double the old incomplete tangibles
typically quoted. That increases CIM potential.
2000
With the experience now in hand from North America, Europe, Middle East, Japan, SE Asia,
CIS and China during the 1990’s we are led to update the general potential expectation from
CIMFUELS technology actively running an oil refinery around 2000 as given in Table 2. The
potential is 1.0 $/bbl x 83.2 kkbpd = 29.1 billion $/y; or NPV (30y, 10%) = 275 billion $. This
should help opco management prioritize CIMFUELS activity within their businesses; this should
help CIMFUELS solution providers and tool developers prioritize their business activities also.
Conclusion
The benefit potential from CIMFUELS has increased 50 % during the decade of the 90’s; it
will exceed 1.0 $/bbl crude by 2000. Opco’s should be able to realize this for costs around 30 - 40
% of gross benefit with proper supplier arrangements, thereby obtaining net profit, before taxes,
of 0.6 - 0.7 $/bbl crude refined. This could be viewed as a variable annuity of substantial NPV,
for free, if you know how to identify, capture and sustain such CIMFUELS performance.
References
1. Latour, P.R., "Role of RIS/APC for Running Refineries in the 1990's", Fuel Reformulation, V2, n2, March 1992, p.
14.
2. Amos, J.D. and Latour, P.R., "Advancements in Refining Control and Information Technology", Presentation for
WEFA-European Oil Refining Conference, Hamburg, Germany, June 16, 1992.
3. Latour, P.R., "Role of RIS/APC for Manufacturing RFG/LSD", National Petroleum Refiners Association 1994
Annual Meeting, San Antonio, TX, March 21, 1994.
January 31, 1995.
6. Latour, P.R., "Modeling Intangible, Hidden Benefits from Better Product Quality Control", International Conference
on Productivity and Quality in the Hydrocarbon Process Industry, Hydrocarbon Processing Magazine/Coopers and
Lybrand, Houston, TX, February 27, 1992, Hydrocarbon Processing, V71, n5, May 1992, p. 61.
7. Latour, P.R. “Process control: CLIFFTENT shows it’s more profitable than expected”, Hydrocarbon Processing,
V75, n12, December 1996, pp. 75-80.

TABLE 1. CIMFUELS Benefits from Typical 135 kbpd Refinery – 1990 (Ref 1, 3, 4)
Application UScts/bbl Unit Feed Unit Feed/Crude UScts/bbl Crude
Adv Control & Opt
Atmos Crude Distillation 4 - 8 1.00 4 – 8
Vacuum Distillation 5 – 10 0.30 2 – 3
Catalytic Cracker 20 – 30 0.30 6 – 10
Catalytic Reformer 10 – 20 0.20 2 – 4
Hydrocracker 15 – 25 0.20 3 – 5
Delayed Coker, Visb 15 – 40 0.15 2 – 6
Alkylation 15 – 30 0.08 1 – 2
Light Ends 10 – 20 0.10 1 – 2
Blending 5 – 12 0.80 4 – 10
Subtotal --------- ---- 25 – 50
Potential --------- ---- 20 – 40
Refinery Info Systems
Perform Measure 5 – 10
Plan & Schedule 10 – 20
Opns Optimization 10 – 20
Integration 20 – 40
Subtotal 45 – 90
Potential 40 – 80
Total Potential 60 – 99+
Year 1990 Average Refinery *480 = Worldwide
Number of refineries 1 480
Crude, kkbpd 0.135 64.8
*0.8 $/b = kk$/d 0.108 51.8
*0.35 kd/y = kkk$/y 0.0378 18.1
NPV(10y,10%), kkk$ 0.232 111
NPV(20y,10%), kkk$ 0.322 154
NPV(30y,10%), kkk$ 0.356 171

TABLE 2. CIMFUELS Benefits from Typical 160 kbpd Refinery – CY 2000, UScts/bbl Crude
Application Unit Feed/ PM CMVPC CLRTO CLRTS INTEG TOTAL
Crude
Atm Crude Distillation 1.00 1.5 9.0 2.0 2.0 1.5 16.0
Vacuum Distillation 0.30 0.4 3.0 1.0 0.4 0.6 5.4
Catalytic Cracker 0.30 1.0 8.0 3.0 1.0 1.0 14.0
Catalytic Reformer 0.20 0.3 3.0 1.0 0.3 0.3 4.9
Hydrocracker 0.20 0.5 5.0 1.0 0.4 0.6 7.5
Delayed Coker, Visb 0.15 0.5 5.0 1.0 0.5 0.5 7.5
Alkylation 0.08 0.3 1.5 1.0 0.1 0.2 3.1
Light Ends 0.10 0.3 1.5 1.0 0.1 0.2 3.1
Blending- mogas 0.30 0.5 6.0 3.0 3.0 1.0 13.5
Blending- mdist 0.50 0.3 4.0 2.0 2.0 0.6 8.9
Oxygenates 0.03 0.1 0.4 0.2 0.1 0.1 0.9
H2, sulf, util, cogen 0.02 0.5 5.0 1.0 0.4 0.6 7.5
BTX 0.05 0.3 3.0 1.0 0.3 0.3 4.9
Olefins 0.20 1.0 8.0 3.0 1.0 1.0 14.0
Lube 0.05 0.5 5.0 1.0 1.0 0.5 8.0
Offsites, lab 1.00 0.3 3.0 2.0 2.0 2.0 9.3
Refywide Connect 1.00 1.0 3.0 6.0 8.0 3.5 21.5
Subtotal --- 9.3 73.4 30.2 22.6 14.5 150.0
Potential --- 5 50 20 15 10 100
Year 2000 Refinery *520 = World Refng
Number of Refineries 1 520
Crude, kkbpd 0.160 83.2
*1.0 $/b = kk$/d 0.160 83.2
*0.35 kd/y = kkk$/y 0.056 29.1
NPV(10y,10%), kkk$ 0.344 179
NPV(20y,10%), kkk$ 0.477 248
NPV(30y,10%), kkk$ 0.528 275

CIMFUELS Editorial for FUEL Technology & Management, January/February 1998, p12, 14
RISK/VALUE - What’s Wrong with this Picture?
Pierre R. Latour, SR2
Hart’s Fuel Technology & Management Advisory Board Member and Consulting Chemical
Engineer
CIM has the potential to generate substantial profits for the hydrocarbon fuels manufacturing
industry. In the last editorial we showed the potential benefit (not profit) exceeds 1.0 $/bbl crude
x 75 kkbpd crude refined worldwide x 365 d/y = 27 billion $/y. For the typical 160 kbpd refinery
the potential k$/sd benefits are: 34.2 for ACU/VAC, 22.4 for FCC, 35.8 for mogas plus middle
distillate blending, 160 for the entire fuels refinery and 22.4 for a 1 bill lb. C2=/y (= 454 kt/y)
liquids feed olefin plant.
But CIM is a risky endeavor for the uninitiated. Current commercial practice was described in
the Mar 97 issue of FUEL T&M. How should the CIMFUELS technology solution providers and
their opco customers address risk/value? BP and others have reported recently on new thinking
for gain sharing with suppliers (1-3).
Cash Flow Pictures
First we draw basic cash flow pictures for four different commercial arrangements and ask:
what’s wrong with this picture? Each cash flow picture looks like a home ARM. Assume a
typical 160 kbpd refinery, gross benefit 1 $/bbl crude, TVM = 10 %/yr. Table 1 shows the Install
only, no maintenance approach common for application projects. Table 2 shows an Installation
and Maintenance arrangement. Table 3 shows the gross Benefit Split between client and supplier
on a predetermined percentage. Table 4 shows the Profit Split between client and supplier on a
predetermined percentage. Each table shows a steady benefit rate which of course would actually
vary with time. Each table provides net present value over 30 years discounted by the assumed
time value of money because a good strategic CIMFUELS objective for client and supplier is to
maximize the expected value of NPV (30y).
What’s wrong with Table 1? We offer a brief comment to stimulate thinking. The client NPV
(30y) = 493 kk$ is a figment of my imagination; it will never be realized. It’s expected
probability in vanishingly small. Such a profit rate has never been sustained automatically during
29 years of neglect by anyone in history. This is a very risky arrangement for any opco who lets
his supplier go away.
What’s wrong with Table 2? It is very unlikely any supplier will generate such a powerful
sustained profit rate for a client in exchange for such a meager profit rate for himself (his staff
and stockholders). The incentives are way out of balance and client NPV (30y) = 472 kk$
remains elusive, not likely to be realized. Its expected probability is quite low.
What’s wrong with Table 3? This arrangement is a substantial improvement. The client NPV
(30y) = 400 kk$ is much more secure than the values in Tables 1 & 2. If the supplier’s profit
potential is large enough to cover his risk and inspire him to maximize benefit, Table 3 is feasible
(provided benefit can be measured quantitatively for invoicing purposes of course). In fact client
bears no cost or risk. The deal looks like a large variable annuity, for free. The risk is aligned with
the supplier (presuming he is knowledgeable and experienced and knows the risk is rather low if

he is empowered to deploy CIMFUELS technology profitably). The supplier deserves more profit
because he assumes more risk. Client NPV (30y) has a fair expected probability. However cost
cutting is not properly rewarded.
What’s wrong with Table 4? If profit is measurable and fairly split, this picture is rather
beautiful. The client and supplier are both focused on profit rate performance to each. They work
to create wealth from improved process operation rather than from each other as poor adversaries.
Such an arrangement is designed to mitigate risk and maximize expected net profit NPV (30y) for
each using the knowledge of the parties charged to capture profits every step of the way. This
client NPV (30y) = 381 kk$ has the highest expected probability of achievement. Client
maximizes his expected NPV when the split is optimum. (Experience has shown the optimum
split to client increases with plant size and profit/cost ratio).
Partnering Alliances
Partnering between CIM solution providers and their opco clients must be built on a proper
financial foundation to succeed and last. BP reported (1) successful gain sharing with Brown &
Root and six contractors for a North Sea development with 54% for the contractors and 46% for
BP. BP’s original conventional project cost estimate was 675 kk$, the gain sharing budget was
560 kk$ (17% less) and the final cost was 435 kk$ (22% less again). Further, the gain shared
project was completed six months early. BP reported a similar arrangement with Foster Wheeler
for an FCC revamp (2). BP is following similar principles for a refinery expansion (3) and for
CIMFUELS (10).
Halliburton (4) reported on the success of performance based risk and reward arrangements for
technology licensing to oil and gas operators.
Amoco (5) is engaged in a major refining strategy “hi-pro” to boost returns by 100 kk$ by Dec
98 involving improved planning, control, scheduling and optimization (they included three of the
five basic CIMFUELS functions).
Mitigating Risk
Since it is well established that we learn more by studying failure than success (6), “those who
made good decisions tend to think through the ultimate outcome of their actions. They thought
long term … frequently revisited assumptions and decisions. We learn from the study of
dictatorships (public and private) how inefficient they are because they fail to delegate, empower
and incentivize people at the lower levels who do the work. Success comes from remembering
long term goals and keeping score fairly.”
Among the ten basic principles of economics (7) are: 1) people face tradeoffs, 2) the cost of
something is what you give up to get it, 3) rational people think at the margin, and 4) people
respond to incentives. Successful CIMFUELS and CIMCHEM arrangements follow these
principles. Long standing disputes (8) on how to prioritize and commercialize R&D and
technology (universities, government, industry) are best resolved by proper connection of
technical function to performance, performance to value and risk to value and rewards.
Recent work (9) shows the profound role of risk analysis in managing human endeavors and
the ways savvy people align knowledge and experience with risk sharing to cope. CIMFUELS
technology creates big low risk benefits best when people are able to quantify and computerize
their goals, objectives and values. In other words they know how to tell a CIM system (i.e.
computer) what objective function they want their plant to optimize. New CIM technology is

making this easier to do; in fact it now may be easier to tell a computer than a human! That can
be really risky!
References
1. Knott, D., “BP sharpening focus on improved shareholder value, efficiency”, O&GJ, 8 Jul 96, p22.
2. Donnelly, I.S. and J.W. Haynes, “FCCU Revamp Performance Enhancement by Alliancing”, Hydrocarbon
Engineering, V2, n4, Jul/Aug 97, p 58.
3. Knott, D., BP slates world-scale expansion at Grangemouth complex”, O&GJ, 18 Aug 97, p 15.
4. Terry, J., “Operators must become bigger stakeholders in technology”, O&GJ, 21 Jul 97, p 49.
5. Peckham, Jack, “Amoco Outlines Aggressive Profit Goals for Wall Street”, Octane Week, Vol. XII, n37, 22 Sept 97,
p 3.
6. Barlow, Jim, “Success is a series of good decisions”, Houston Chronicle, Sect B, 30 Sep 97.
7. Norton, Rob, “Some Things Economists Actually Agree On”, FORTUNE, 13 Oct 97, p 36.
8. Thayer, A. M., “Science and the Profit Motive”, C&EN, 25 Aug 97, p 38.
9. Bernstein, Peter L., “AGAINST THE GODS - The remarkable Story of Risk”, John Wiley, 1996.
10. Ahmad, Pasha, “Managing Process Information with the Internet”, Paper CC-97-121, NPRA Computer Conference,
New Orleans, 17 Nov 97.

Table 1. PROJECT CASH FLOW - KK$/QTR
Typical Fuels
OIL REFINERY: Crude kbpd = 160 BENEFIT, $/bbl crude = 1.0
Install only No Maintenance 0 TVM%/y 10
End Qtr Benefit PRICE* Client net SUPPLIER COST Supplier net
Q0 0 1.00 -1.00 1.00 0.00
Q1 0 2.00 -2.00 2.00 0.00
Q2 0 2.00 -2.00 2.00 0.00
Q3 0 2.00 -2.00 1.00 1.00
Q4 0 0.50 -0.50 0.50 0.00
NPV(1y) --- 7.17 -7.17 6.24 0.93
Q5 14.40 0 14.40 0 0
Q6 14.40 0 14.40 0 0
.... ---- --- ---- --- ---
.... ---- --- ---- --- ---
Q40 14.40 0 14.40 0 0
NPV(2-10y) 307.31 --- 307.31 --- 0
NPV10y 307.31 7.17 300.14 6.24 0.93
% Benfit 100 2.33 97.67 2.03 0.30
Q41 14.40 0 14.40 0 0
Q42 14.40 0 14.40 0 0
.... ---- --- ---- --- ---
.... ---- --- ---- --- ---
Q80 14.40 0 14.40 0 0
NPV20y 446.67 7.17 439.51 6.24 0.93
% Benfit 100 1.60 98.40 1.40 0.21
NPV30y 500.41 7.17 493.24 6.24 0.93
% Benfit 100 1.43 98.57 1.25 0.19

Typical Fuels
Install & Maintain: Price = 0.6 Cost = 0.5 TVM%/y 10
End Qtr Benefit PRICE* Client net SUPPLIER COST Supplier net
Q0 0 1.00 -1.00 1.00 0.00
Q1 0 2.00 -2.00 2.00 0.00
Q2 0 2.00 -2.00 2.00 0.00
Q3 0 2.00 -2.00 1.00 1.00
Q4 0 0.50 -0.50 0.50 0.00
NPV(1y) --- 7.17 -7.17 6.24 0.93
Q5 14.40 0.60 13.80 0.50 0.10
Q6 14.40 0.60 13.80 0.50 0.10
.... ---- ---- ---- ---- ----
.... ---- ---- ---- ---- ----
Q40 14.40 0.60 13.80 0.50 0.10
NPV(2-10y) 307.31 12.80 294.50 10.67 2.13
NPV10y 307.31 19.97 287.34 16.91 3.06
% Benfit 100 6.50 93.50 5.50 1.00
Q41 14.40 0.60 13.80 0.50 0.10
Q42 14.40 0.60 13.80 0.50 0.10
.... ---- ---- ---- ---- ----
.... ---- ---- ---- ---- ----
Q80 14.40 0.60 13.80 0.50 0.10
NPV20y 446.67 25.78 420.90 21.75 4.03
% Benfit 100 5.77 94.23 4.87 0.90
NPV30y 500.41 28.02 472.39 23.61 4.40
% Benfit 100 5.60 94.40 4.72 0.88

Typical Fuels
OIL REFINERY: Crude kbpd = 160 BENEFIT, mbpd crude = 1.0
(SR)2 Benfit split Client% = 80 Suplir% 20 TVM%/y 10
End Qtr Benefit Price Client net SUPPLIER COST Supplier net
Q0 0 0.00 0.00 1.00 -1.00
Q1 0 0.00 0.00 2.00 -2.00
Q2 0 0.00 0.00 2.00 -2.00
Q3 0 0.00 0.00 1.00 -1.00
Q4 0 0.00 0.00 0.50 -0.50
NPV(1y) --- 0.00 0.00 6.24 -6.24
Q5 14.40 2.88 11.52 0.50 2.38
Q6 14.40 2.88 11.52 0.50 2.38
.... ---- ---- ---- ---- ----
.... ---- ---- ---- ---- ----
Q40 14.40 2.88 11.52 0.50 2.38
NPV(2-10y) 307.31 61.46 245.85 10.67 50.79
NPV10y 307.31 61.46 245.85 16.91 44.55
% Benfit 100 20.00 80.00 5.50 14.50
Q41 14.40 2.88 11.52 0.50 2.38
Q42 14.40 2.88 11.52 0.50 2.38
.... ---- ---- ---- ---- ----
.... ---- ---- ---- ---- ----
Q80 14.40 2.88 11.52 0.50 2.38
NPV20y 446.67 89.33 357.34 21.75 67.59
% Benfit 100 20.00 80.00 4.87 15.13
NPV30y 500.41 100.08 400.32 23.61 76.47
% Benfit 100 20.00 80.00 4.72 15.28

Typical Fuels
Premier (SR)2 Profit split:
Client % = 80
Supplier
% = 20
TVM,
%/y 10
End Qtr Benefit Price Client net SUPPLIER COST Supplier net
Q0 0 0.80 -0.80 1.00 -0.20
Q1 0 1.60 -1.60 2.00 -0.40
Q2 0 1.60 -1.60 2.00 -0.40
Q3 0 0.80 -0.80 1.00 -0.20
Q4 0 0.40 -0.40 0.50 -0.10
NPV(1y) --- 4.99 -4.99 6.24 -1.25
Q5 14.40 3.28 11.12 0.50 2.78
Q6 14.40 3.28 11.12 0.50 2.78
.... ---- ---- ---- ---- ----
.... ---- ---- ---- ---- ----
Q40 14.40 3.28 11.12 0.50 2.78
NPV(2-10y) 307.31 70.00 237.31 10.67 59.33
NPV10y 307.31 74.99 232.32 16.91 58.08
% Benfit 100 24.40 75.60 5.50 18.90
Q41 14.40 3.28 11.12 0.50 2.78
Q42 14.40 3.28 11.12 0.50 2.78
.... ---- ---- ---- ---- ----
.... ---- ---- ---- ---- ----
Q80 14.40 3.28 11.12 0.50 2.78
NPV20y 446.67 106.73 339.94 21.75 84.99
% Benfit 100 23.89 76.11 4.87 19.03
NPV30y 500.41 118.97 381.44 23.61 95.36
% Benfit 100 23.77 76.23 4.72 19.06

H P In Control Columnist, Hydrocarbon Processing, June 1994, p23

H P In Control Guest Columnist, Hydrocarbon Processing, July 1997, p15-16
Does the HPI do its CIM business right?
Houston, Texas
This H P In Control editorial space has covered process control technologies, definitions,
development, applications and issues very well for many years. Coverage has broadened from
instrumentation to advanced process control, optimization of operations, scheduling, information
systems and Computer Integrated Manufacturing: CIMFUELS & CIMCHEM. Lately some
management problems have been raised, a sign of business maturity. I believe CIM has abundant
powerful technology capable of generating great value from the HPI, but its commercial success
is hampered by business issues, not technology.
Situation
Two years ago (1) a process control consultant bemoaned continuing failures of CLRTO
(closed loop real time optimization) and even many advanced computer control applications he
has seen in his troubleshooting work (probably because his experience is limited to failed
situations rather than successful ones). He tends to blame poor engineering and inadequate
technology for the failures he has seen for so many years. In the Feb 97 edition of H P In Control
the guest editorial by the same troubleshooter depicted numerous technical reasons for too many
advanced process control failures and summarized them as “lack of support by opco
management”. He offered ten remedies; all were management procedures and guidelines. He
correctly identified lack of industry methods for intermediate stream transfer prices (which CIM
can provide) and unit feed characterization (which CIMFUELS can also provide) as basic
barriers.
What is the real cause and remedy? Why do CIM investments still seem risky, too often
ending in failure? While I do not dispute his specific findings and agree CIM investments remain
too risky for opcos, I know of numerous outstanding CIM successes. I suspect the general
underlying cause of disappointments is really a weak business arrangement between supplier and
user.
In the March 97 edition of H P In Control, Les Kane reported “Control services taken for
granted”. He found end users are expecting control engineering services from hardware vendors,
for free. He quoted extensively from a supplier executive bemoaning his inability to get “fair” pay
for services and from the same consultant decrying reengineering staff downsizing, poor
technology packaging and faulty engineering which contribute to disappointment and
discouragement.
Why doesn’t management appreciate process control? Why so much trouble and confusion in
the CIM business these days? Why so many disconnects between supplier and customer? What’s
the reason? What is going on? What is wrong? Can CIM really make a difference anyway? Can it
make money? Big money? If so, how and how much? Can anyone prove it? Who should have
responsibility and commensurate authority for results? Performance? Lasting success? Profit?
Identifying, capturing and sustaining (20 - 30 years) significant (0.5 - 1.0 $/bbl crude refined)
economic benefits? Why? Who should shoulder the commercial risks? Who should sustain the

losses of failure, and reap the rewards of success? Who has the know how and capability to
deliver? Is the knowledge and expertise properly aligned with commercial risk and reward? Are
the incentives aligned well? Is CIM technology licensed properly to secure lasting improvement
in profitability for all involved?
Analysis
The Greeks taught 2500 years ago: analysis comes before synthesis. My analysis of the
situation is the CIMFUELS and CIMCHEM business practice has not matured to match the
powerful technology developments in the 1990’s. Users are not making enough money from
CIM; suppliers aren’t either. That is because the business is not done right between them.
CIMFUELS has plenty of technology but commercial success remains an elusive backwater
because it is rarely licensed based on results from sustained performance, with proper sharing of
risk and reward between opco customer and business solution provider of know how.
The CIM business suffers from inadequate distinction between TOOLS and SOLUTIONS.
Suppliers and opco users both fail to properly distinguish between TOOLS (which are used by
VAR’s and SI’s) and SOLUTIONS which work and make money from plants. CIM is still sold
too much by suppliers and bought too much by opcos as TOOLS rather than SOLUTIONS. There
is a big, very big difference!
The primary tools to build and maintain CIM solutions are hardware components, software
packages, and services. Computers, instruments, actuators and networks are hardware tools.
Models, algorithms, data bases, neural nets and interfaces are software products/packages tools.
Engineering, programming, project installation, maintenance, management, upgrading, training
and consulting are services tools. Tools are sold like groceries and building supplies: based on
features, how it works and low cost. Who is the tools customer?
The proper buyer of CIM tools is the value added reseller (VAR), system integrator (SI),
prime engineering contractor or outsourced CIM solution provider (designer, installer,
maintainer, partner). The original CIMFUELS solution providers were the inside engineering and
technology arms of the large oil refining and petrochemical opcos.
CIM solutions are working, real-time, mostly closed-loop systems that participate directly in
running processes, plants and businesses in harmony with the organization. They must work, be
used, be maintained, evolve, change, grow and make money just like the
plant/organization/business. CIM solutions are sold like dining experiences and housing
developments: based on problems solved, reliable performance, value added. Development cost
and how it works are much less important to the solution customer than what it does, how it is
useful and how much value it creates.
Who is the solutions customer? The proper buyer of CIM solutions is the fuels/petrochemical
opco profit center. They buy know how from solution providers for large, low risk profit results.
Synthesis
The Greeks also taught 2500 years ago; if analysis is right synthesis becomes obvious. Tool
sellers should sell tools to solution providers, not opco profit centers. Opco profit centers should
buy CIM solutions from solution providers, not tool salesmen. Otherwise, disconnects arise,
confusion abounds and failure follows. So long as tool sellers posture themselves incorrectly as
solution providers, they will experience the disconnects manifested in demands for free services
and low costs to mitigate the opco’s fairly perceived risks (without a qualified solution provider).

So long as solution providers posture themselves incorrectly as tool salesmen to opco’s they
will experience the disconnects manifested in demands for low cost competitive bids for high
performance solutions as commodities. So long as SI/VAR’s fail to adequately distinguish
between suppliers of the tools they need and their solution provider competitors they will
experience disconnects of identity unable to articulate their own role, core competency,
proprietary know how and value added to themselves and their opco customers. So long as opco
end users fail to adequately distinguish between purchasing tools and solutions from their
suppliers, they will suffer disconnects like buying from a grocery store or builder supply when
they should go to a restaurant or realtor.
How does one sell/buy a CIM solution? Not as a tool or product! Not as a hodgepodge
assembly of low cost competitively bid components. Since no one ever bought a dining
experience, home, car, suit, entertainment, college education, vacation, share of stock, or heart
surgery as a lowest cost competitively bid product that “meets the requirements specs”, so
profitable CIM solutions should not be bought as tools or products.
We must synthesize a different (new or old?) paradigm for doing CIM business. One proven
method for SOLUTION technology is to license right to use for a running royalty, based on value
added performance. Supplier receives a percent of process benefit generated or a percentage of
profit = benefit less costs, depending upon the nature of cash flow risk assumed by supplier.
Supplier and client become long term business partners incentivised with a sound financial
arrangement between them to achieve sustained profits for each other.
The Shared Risk - Shared Reward (SR2) method of technology licensing is a rather old idea
called “taking a percentage” or “a piece of the action” in other circles. Risk and knowledge
should be aligned properly to assure reward is shared fairly. When CIMFUELS benefits exceed
0.5 $/bbl crude refined (2, 3, 4), opcos make a big mistake to dismiss their solution provider after
successful installation, assuming such performance will be sustained automatically forever
without maintenance care. Nothing ever works that way, not even great CIM technology! The
NPV (30y, 10%) for 200 mbpcd x 0.5 $/bbl = 347 kk$. If solution provider is retained to secure
20 - 30 % of that for himself, the remaining 70 - 80 % is much more secure for the opco than the
100 % (which is really a figment of imagination). Fortunately, rigorous methods for quantifying
the financial benefit from improved dynamic performance of processes now exist (5, 6).
Conclusion
Profit improvement for all CIM business participants will come from clarification of need of
each customer, role of each supplier and appropriate commercial relations between them. Know
yourself, your company, your core competency, and your weaknesses. Sellers should select and
know your customer, his needs, how you add to his value and your own, how you can share in
value creation/risk taking (because they go together, but risk is mitigated by knowledge,
fortunately). Know your competitors and suppliers. Buyers should know your supplier, his needs,
how he adds value to you and you to him, how you share risk bearing with value creating.
Beware of selling tools if buyer is buying solutions; selling solutions if buyer is buying tools;
buying tools if seller is selling solutions; buying solutions if seller is selling tools. All four
disconnects lead to failure. If you buy/sell tools the right way or buy/sell solutions the right way,
you materially strengthen the chances and magnitude of success and profit (although it’s never
guaranteed!). Buying/selling solutions properly is much more like normal business transactions
between people as partners based on performance and value added.

References
1. Friedman, Y. Zak, “What’s wrong with unit closed loop optimization?” Hydrocarbon Processing, Oct 95.
2. Latour, P.R., "Role of RIS/APC for Running Refineries in the 1990's", Fuel Reformulation, V2, n2, March 1992, p.
14.
January 31, 1995.
5. Latour, P.R., "Modeling Intangible, Hidden Benefits from Better Product Quality Control", International Conference
on Productivity and Quality in the Hydrocarbon Process Industry, Hydrocarbon Processing Magazine/Coopers and
Lybrand, Houston, TX, February 27, 1992, Hydrocarbon Processing, V71, n5, May 1992, p. 61.
V75, n12, December 1996, pp. 75-80.

Letter to the editor, Hydrocarbon Processing, January 1998, p 45
Decision-making and modeling in petroleum refining
Pierre R Latour
SR2 for CIMFUELS
Houston, Texas
HP has done it once again! Congratulations for publishing the uniquely useful paper by Hans
Hartmann (formerly at Shell) “Decision-making and modeling in petroleum refining” (1).
Hartmann did a nice job of describing the shortcomings of Linear Programming (LP was
developed for refinery planning in the 1960’s) for Computer Integrated Manufacturing of Fuels
(CIMFUELS in the 1990’s).
After describing three LP shortcomings:
1) hard to provide discrete processing options,
2) hard to accommodate dynamic timing and storage because all LP model activity occurs
simultaneously and
3) not designed to handle nonlinear curvatures and hills so LP solution is always determined
by the supplied values of constraints for the optimum combination of independent and dependent
variables, Hartmann does a good job describing how these are dwarfed by shortcoming
4) data inaccuracy for matrix slopes, economic prices and softness of dependent variable
constraints which have uncertainty and risks.
The troubling consequences include:
1) meaningless intermediate stream transfer prices from LP shadow values and ridiculous
economics structures,
2) audits done by comparing actual performance of material balance flows only (not qualities
or financial objectives) against the unrealistic plans generated by LP which thwarts a refiner’s
ability to reconcile, learn and improve operating financial performance and
3) interpretation of LP results remains an art requiring experienced economics and scheduling
planners with detailed knowledge of all refinery processes and business to be of much use.
Quadratic Programming (QP was developed for refinery process optimization in the 1980’s)
and CLIFFTENT (developed recently for properly setting limits) can be combined to largely
overcome these shortcomings. QP retains all the power of LP but accommodates the Hessian
matrix of second derivatives along with the LP Jacobian matrix of first derivative slopes for
realistic representation of nonlinear curves and hills built from rigorous first principles Chemical
Engineering simulation models of mass, energy, momentum and kinetics to solve the data and
dynamic inventory shortcomings. Mathematical proofs of convergence to nonlinear optima and
commercial success with Closed Loop Real Time Optimization (CLRTO) for ACU, FCC, mogas
blending and olefin plants show the clear path to refinery wide CLRTO by 2000. Commercial QP
model packages are now available from at least six software companies.
CLIFFTENT (2, 3, 4) provides the method for connecting uncertainty, risks and dynamic
performance for proper setting of dependent variable limits to determine the financial value of
reduced variance of smoother operations. Correct response variable limits are soft because they
are at the top of profit function hills found by QP, not at sharp corner intersections of dependent
variable constraints artificially constructed to fit LP formulation.

CLIFFTENT connects process Chemical Engineering models to their surroundings: safety,
maintenance, customer satisfaction, environmental regulations and economics; the land of Re (2).
CLIFFTENT is the comprehensive method to interface steady state QP optimizers to Constrained
Multivariable Predictive Controllers (CMVPC) to achieve CLRTO.
QP and CLIFFTENT give the correct intermediate stream transfer prices (for flow, quality,
recycle H2) for buy/sell decisions of intermediate components and the correct economic structure
for value added tracking among process unit profit centers. These all transform the art of LP
results interpretation into the science of modeling processes, objectives, penalties from the
surroundings, risks/uncertainty and human economic values.
This letter is a call to reform the CIMFUELS practice of using 1960’s LP techniques which
has become a hammer that leads many to see things as nails, to 1990’s QP/CLIFFTENT
technology for improved profit performance from CIMFUELS during the next millennium. So
the answer to the title is YES! The rapid decline of LP beyond 2000 is ordained. Hartmann’s nice
paper inspires us to apply better ways. The profit potential exceeds 0.5 $/bbl crude (5) so the
stakes are high.
References
1. Hartmann, J.C.M., “Decision-making and modeling in petroleum refining”, Hydrocarbon Processing, Vol. 76, No.
11, Nov 1997, p. 77.
Vol. 75, No. 12, Dec 1996, p. 75.
3. Latour, P.R., “Quantify quality control’s intangible benefits”, Hydrocarbon Processing, Vol. 71, No. 5, May 1992, p.
61.
4. Grosdidier, P., “Economics of blend giveaway”, Hydrocarbon Processing, Vol. 76, No. 11, Nov 1997, p. 55.
5. Latour, P.R., “CIMFUELS Editorials”, FUEL Technology & Management, bimonthly issues since Sept 1995.

H P In Control Guest Columnist, Hydrocarbon Processing, June 1998, p17 - 23
Optimizing the $19-billion CIMfuels profit split
Houston, Texas
This H P In Control editorial space has covered process control technologies, definitions,
development and applications very well for many years. Coverage has broadened from
instrumentation to advanced process control, optimization of operations, scheduling, information
systems and Computer Integrated Manufacturing solutions: CIMFUELS & CIMCHEM. In July
1997 we raised the question “Does the HPI do its CIM business right?”
We followed that with a letter to the editor in Jan 98 on “Decision-making and modeling in
petroleum refining”. Others have decried the disconnect between maintenance and the
manufacturing operations business. CIMFUELS business can bring these together.
Situation
The worldwide potential benefit of 1.0 $/bbl crude is 29 billion $/y or NPV (30y, 10%) = $275
billion (1, 2, 3). Deducting supplier costs of about 35 % leaves a profit of 0.65 $/bbl = 19 billion
$/y or NPV (30y, 10%) = $180 billion to be split between operating companies and their
CIMFUEL solution suppliers. Opcos should realize 70 % of that, 13 billion $/y or $126 billion.
(There were two typos in (1): p. 14, col. 4, para. 2, after line 3 insert “all different product blends
by just varying the component amounts” and para. 3, line 2 change “ISTFs” to “ISTPs”.)
Three shared risk - shared reward cash flow pictures (4) for opco - supplier alliances show
how to identify, capture and sustain such performance. Slightly reducing the theoretical opco
profits could give a much higher probability of realization if the supplier is engaged to sustain the
profit performance from CIMFUELS technology long term, with proper alignment of supplier
reward with his risk and know how. Failure to properly account for risk in risky CIM applications
like IT integration and closed-loop optimization (CLRTO) or scheduling (CLRTS) has caused
many disappointments and failures.
Real Objective Measure
The best performance objective measure for successful use of CIMFUELS technology during
the next decade and millennium is expected value of net present value profit over a long time
period, like 30 years.
EXPECTED VALUE PROFIT = PROFIT FORECAST x REALIZATION PROBABILITY
CIMFUELS business leaders should go beyond forecasting cash flow value from CIM
performance to include experience based risk measured as probability of realization. The three
cash flow scenarios (4) clearly showed some high cash flows with very low probability of
realization and consequently low expected value long term profit.

Commercial experience of CIMFUELS and CIMCHEM investments since 1965 suggests the
relationship between split of profit between opco and supplier and the expected value of long term
profit for the opco has the shape of the Laffler Curve (for government revenues as function of
taxation rate) shown in Figure 1. If opco % profit is too low on the left side, opco expected profit
is obviously low and increases with his split. However, if opco demands an excessive % of profit,
the supplier’s diminished incentive and reward for his sustained long term effort and risk
assumption to generate such profits for opco inevitably diminish the suppliers’ performance (in a
capitalistic market without slavery), total benefit declines and opco profit declines along the right
side. Here low probability of realization overcomes higher theoretical cash flow and opco’s
expected value long term profit diminishes with his % take (it can and often does vanish). The
right side limit leaves 0 % profit for the supplier which guarantees no rational supplier will or can
accept such a 30 year assignment, leaving no profit for the opco either.
Optimizing the Split
The basic conclusion of course is that an optimum % profit split exists at the top of a smooth
hill for maximum opco (and supplier) expected value profit. This is the win - win situation most
strategic alliances seek. These rather basic principles explain why low cost competitive bidding
and focus on short term installation or revamp projects or tools rather than profit sharing from
performance based solutions for long term system performance has caused so many non optimum
and even failed CIMFUELS “project” investments. Opcos often prefer to think about adversarial
squeezing of the CIMFUELS supplier for profits which the supplier does not have rather than
engaging him to work cooperatively to squeeze the profits from their source, non optimum plant
operations, rewarding suppliers for assuming more risk and working to maximize expected value
of long term profits for the opco. All too often both sides are in a rush to “finish” installations and
leave. Opcos make a grave mistake when they let talented suppliers run away rather than stick
around to be accountable for the long haul of sustained success.
Technology Licensing
The profit potentials reported (1,2,3) assume CIMFUELS technology is licensed based on its
performance, supplier shoulders more commercial risk commensurate with his experience and
opcos agree to profit splits that optimize opco expected net present value of long term profit. If
profit splits err much to the right of the hill top, the predicted profits quickly disappear. CIM
solution suppliers remain financially weak and fragmented businesses with high staff turnover,
subject to frequent mergers and acquisitions. While modeling of cash flow profit performance
needs greater attention, modeling of risk versus reward is even more important for lasting
success. That is why analysis of the sustained results as a function of the profit split is so
fundamental. (The same applies to how the supplier company splits its realized profit among its
project implementation/maintenance team, technology tool developers, sales force and
shareholders). Solution technology should be licensed based on shared risk - shared reward at the
split which optimizes expected value of profit for both.
Plant Size Affects Best Split
Returning to Figure 1 we also illustrate the optimum split to the opco shifts to the right for
refineries where the ratio of profit to cost to deliver increases, as would be expected from larger
capacity refineries with higher complexity of processing to make a host of
fuel/petrochemical/lube products in volatile product and crude supply markets. However the hill
is sharper and opco losses are more sensitive to a non optimum split, so getting the split right is
more important for big plants. The conclusion is high profitability opportunities deserve the most
accurate assessment of risk/probability of lasting success and proper shared risk - shared reward
arrangements between opcos and their providers.

Unhappily, in their eagerness to skim - the - cream, pick - the - fruit or grab - the - quick -
profits when the potential is high, most seem to lose interest in “esoteric” modeling of
performance benefits let alone risk probabilities. When they see the real potential of CIMFUELS
they want it all, now, even from a refinery that has been operating and “optimized” for over thirty
years! This idea has also led to much grief. Hopefully, such immature business attitudes will
decline in popularity as industry leaders recognize the profound opportunity CIMFUELS offers
for making more money from manufacturing the clean fuels of the future using computers for
PM, CMVPC, CLRTO, CLRTS and INTEGRATION, the five active CIM functions (2,3) that
make money.
Conclusion
Beware of selling tools if the buyer is buying solutions; selling solutions if the buyer is buying
tools; buying tools if the seller is selling solutions; or buying solutions if the seller is selling tools.
All four disconnects lead to failure. If you buy/sell tools the right way or buy/sell solutions the
right way, you materially strengthen the chances and magnitude of success and profit (although
it’s never guaranteed!). Buying/selling solutions properly is much more like normal business
transactions between people as partners based on performance and value added, shared risk -
shared reward. Modern business principles for value added and risk sharing (5) are essential for
successful CIMFUELS solutions to capture that $19 billion/yr.
The big CIMFUELS money comes from correctly telling the computer 1) how the plant works
(easier to do these days), 2) what the real plant objective function is (not so easy), 3) what the
best dependent variable limit settings are (now much easier, (6)) and 4) what the penalty will be
for violating the limits (not so easy, (6)). If the HPI will do these well, with accuracy and fidelity,
opcos will get 13 billion $/y net throughout the next millennium and the suppliers can get the
other 6 billion $/y they have earned. That is true potential.
References
1. Latour, P. R., “Benefit Potential >$1.00/bbl Crude”, FUEL T&M, Nov/Dec 1997, p 14, 16.
2. Latour, P. R., "Role of RIS/APC for Running Refineries in the 1990's", Fuel Reformulation, V2, n2, March 1992, p.
14.
3. Latour, P. R., "Mission: Plan to Use RIS/APC for Manufacturing RFG/LSD", Fuel Reformulation, V4, n4,
4. Latour, P. R., “Risk/Value: What’s Wrong With This Picture?”, FUEL T&M, Jan/Feb 1998, p. 12, 14.
5. Stern, Stewart, “Why EVA Works”, FORTUNE, May 25, 1998.
6. Latour, P. R., “Process control: CLIFFTENT shows it’s more profitable than expected”, Hydrocarbon Processing,
V75, n12, December 1996, pp. 75-80.

Fig. 1 Optimum Benefit Split %

Author - Pierre R Latour
Pierre R Latour is an independent consultant specializing in measuring financial
performance of HPI information and process control systems (CLIFFTENT™) to support Shared
risk – Shared reward (SR2™) licensing of profit sustaining solutions. He has been a vice
president of engineering, marketing, business development, project implementation and
consulting at Aspen Technology, Dynamic Matrix Control Corp, Setpoint, Setpoint Japan and
Biles & Associates. Dr Latour co-founded the last three firms and held senior positions at Shell
Oil and DuPont. He has worked on contracts for 50 HPI companies worldwide on most processes
like ACU, FCC and olefins. Dr Latour holds a BS, ChE, from Virginia Tech and a PhD, ChE,
from Purdue. He served as Captain, US Army, NASA, Houston. Dr Latour has authored 60
publications. He was CIMFuels Editor for FUEL and is a registered PE in TX & CA. Email:
clifftent@hotmail.com.

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