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O
il refinery and petrochemical plant operating targets
should be aligned directly with economics to maximize
profit rate in real time. Best operation is always near
properly set limits. The 1996 performance measure method1,2 for
process control and information technology (IT) that associates a
profit tradeoff with each controlled variable and key performance
indicator (CV/KPI) distribution allows hydrocarbon process
industry (HPI) operators who know the financial consequences
of violating limits to rigorously align risky operating conditions
to economics, generating profit >2 $/bbl crude refined. The risk
optimization method, application areas, accurate data value,
requirements, cash flows and 12 important conclusions to align
HPI operations to economics are given.
The art of operating HPI plants most profitably includes
proper setting of operating limits, specifications and constraints;
the feasible operating region window. If the limits are set too
tightly, the window is too small and profit is lost. If the limits are
set too loosely, the window is so big that external forces dominate
and profit is lost. Clifftent1,2 provides a rigorous method for set-
ting CV/KPI constraints and corresponding targets or setpoints
just right, optimally.
Clifftent was disclosed in 19961,2 to measure the financial
value of improved dynamic performance of any process, plant,
activity or system. The benefit source may be better measure-
ment, timely and frequent data, better operators, better loop
tuning, multivariable control, better models, better control valves,
faster computers, integrated data bases, IT, computer-integrated
manufacturing (CIM) or training. The cause or tool is irrelevant
to clifftent analysis; but its cost can be compared to its perfor-
mance benefit claim with clifftent for appraisal of merit. Without
a rigorous clifftent performance measure of financial value added
by components of CIM, technology and solutions, suppliers and
users cannot make rational investment, pricing, deployment and
maintenance decisions, i.e., nobody knows the financial merit of
what they are talking about.3–9 So they naturally rely on features,
intangibles, judgment, fads and the faith theory.9
Chemical engineering theory of process operation.
Since the 1960s, chemical engineering has developed a large
mathematical theory of process systems analysis and control10
that remains incomplete.
Variance. Process control reduces variability of CVs to run
HPI plants more smoothly and steadily, but it does not determine
where they should be run. It uses dynamic models to modify the
shape and sharpness of statistical distributions and variance of
important operating response variables. Control theory com-
mercialized in the 1980s is used to operate HPI plants around the
world. It is the intellectual foundation of the multibillion-dollar
instrument, control, automation, IT and CIM business9 that
serves them.
Mean. Chemical engineering system analysis and control
theory has neglected the companion problem to variance manage-
ment: determining the average, mean, target or setpoint value for
each operating variable of interest. Academia has naturally lost
interest in process control research because variance reduction
engineering was completed by 1980, commercialized by 1990,
and its value has been improperly measured ever since. The value
of reduced variance remains intangible, a mere prerequisite for
moving means in favorable directions by arbitrary amounts, and
the value of setting the mean correctly remains unrealized.
Careful study of instrument, control and IT benefit claims
reveals this universal inability to properly analyze and quantify the
value they create.9,11 Online QLP-type optimizers do not really
determine limit values and setpoints; they only pick the optimum
combination of constraining variables, without regard to variance
risk or violation penalties.1,12
Careful reading of the BP Texas City safety report13 with
knowledge of this weakness and remedy illustrates the HPI oper-
ating handicap and consequences.
Basis of clifftent. Clifftent is the fundamental method for
optimizing risky tradeoffs. All CVs/KPIs in HPI plants manifest
financial tradeoffs in the neighborhood of their limits.1,2,9,14–17
For maximum limits there is a process gain realized by approach-
ing the limit from below and an external penalty for exceeding it.
For minimum limits a process gain is realized by approaching it
from above and an external penalty for going below it. This is true
for flow, temperature, pressure, level, quality, speed, throughput,
velocity, energy supply and emissions.1
Every process plant CV/KPI has an associated profit tradeoff,
shaped like a tent, often with a discontinuous cliff near its limit,
constraint or specification, because it matters, it is key, we care
about its value, it affects profit, it has a clifftent profit function.
The profit tradeoff connects dissimilar phenomena affected by
operating conditions that impact long-term expected value profit
rate. Maximum theoretical profit when variance is zero is real-
ized just at the peak of the clifftent function. But CVs/KPIs also
vary; they are never perfectly controllable with zero variance. So
targeting them near the maximum steady-state profit point is
risky business.
Method. Clifftent requires two input functions1,2,7–9,14–17 of
the CV: its distribution or histogram and its steady-state profit
tent extending down from its limit peak in both directions. The
mathematical technique is to integrate their product to obtain
a number, the expected value (or weighted average) of profit
rate. The integration is repeated for different distribution means
Align HPI operations to economics
Clifftent optimizes risky tradeoffs at limits
P. R. Latour, CLIFFTENT Inc, Houston, Texas

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throughout the range to get a smooth hill curve for average profit
versus CV mean including variance uncertainty.The hilltop is eas-
ily located for the maximum profit setpoint (Fig. 1). Multivariable
controller CV weighting factors [dynamic matrix control (DMC)
needs equal-concern errors (ECEs)] are easily derived from the
smooth hill too.
Flawed benefit analysis. Clifftent showed the standard method
of control benefit analysis was fundamentally flawed.1 The stan-
dard method takes a given, arbitrary CV/KPI mean and variance,
proposes to reduce variance, postulates the reduction adds no value
(because variations cancel out, value is hidden or intangible), but
smoother operation is a prerequisite for moving the mean some
arbitrary amount toward the more valuable limit for a correspond-
ing steady-state gain (capacity, yield, utility saving) which, when
multiplied by a benefit rate/gain factor, estimates a financial ben-
efit. The rigorous clifftent steps reveal the flaws:
• The original mean is never optimum; it should be set opti-
mally first. This is easily done by clifftent, providing a plant ben-
efit on the order of all CIM variance reduction claims at trivial
cost. Optimize the setpoint first, to make money. (This is contrary
to the conventional practice of control first, optimization later.)
• Variance reduction does indeed add value; it’s of the same
order of magnitude as the value from optimizing the mean. Making
variance reduction value tangible is the second clifftent benefit.
• The new mean with reduced variance should be reopti-
mized and moved an optimum amount toward the profitable
limit, determined rigorously by clifftent, for a third benefit. This
proves process control to reduce variance integrated with clifftent
to optimize setpoints generates tangible, believable benefits twice
those typically claimed from the standard method.1
Confirmation. The classic paper by Stout and Cline,15 the
founders of Profimatics, described the analytical method for justify-
ing computer control14–16 they used during the 1970s. Clifftent1,2
confirmed most of their results, generalizing and extending them
to all tradeoffs and risk distributions. Stout and Cline found:
• There are two parts of benefit determination: that due to
regulation about a mean setpoint and that due to optimum align-
ment of setpoints. Confirmed by clifftent.
• Benefit estimation “is a universal problem which, unfor-
tunately, does not have a universal solution.” True for analytic
solutions, but Clifftent shows it is not true for numerical integration.
Clifftent provides the universal solution.
• Simple relationships shed light on the relative value of better
regulation and optimization. True. Assumed profit is a smooth
quadratic or cubic in CV mean. Interesting but rarely valid. Ammo-
nia plant H/N ratio, catalytic reformer octane, distillation reflux and
furnace fuel/air ratio often have smooth profit hills but most CVs have
discontinuous tents, some with cliffs. All are handled by clifftent.
• Expectation operator of statistics is essential. They used a
goal of maximum expected value profit. Confirmed by clifftent.
• Assuming quadratic profit P(x) = A + Bx + Cx2, mean M, and
standard deviation, , Pavg = P(M) + C2 or ΔPavg = C(2
2 – 1
2)
only depends on C, not M. For linear profit functions, C = 0 and
regulation has no direct benefit. True.
• Benefit by shifting the average by optimization and reduc-
ing variability by control, or both, are estimated as two parts of
a unified analysis of these two separate activities. Both can be
quantified if handled together. Confirmed by clifftent.
• Benefit from setpoint optimization depends on the extent of
prior misoperation, ability to locate and track the optimum and
degree to which constraints interfere with operating at optimum.
Clifftent confirms and handles constraint situations.
• For linear profit functions, reduced variability provides no
direct benefit but allows moving the average closer to a constraint.
In such cases, regulation and optimization are closely related.
They should always be evaluated together. Confirmed by clifftent.
• Regulation can be accomplished by repetitive optimization,
but this approach is unduly complex and rarely attempted. Inter-
esting that DMC used repetitive LP optimization to commercialize
multivariable control in the 1980s, 10 years later.
• Solved an alkylation deisobutanizer with skewed cubic cost
function P = 264 – 72x + 24x2 – 2x3. The steady-state mini-
mum cost was 200 at x = 2.0. From their equations, Latour
derived optimum mean for x, M *
= 4 [4 ( 2
/ 6)]. For  =
0, M* = 2.00 and min P = 200. If  = 0.2, M* = 2.001667 and
min P = 200.0000333. If  = 0.5, M* = 2.010444 and min P =
200.001307. Confirmed by clifftent.
Application areas. A host of clifftent application areas explain
its role in aligning HPI setpoints to economics. Since it is the rig-
orous way to optimize risky tradeoffs, it has universal utility.
CV candidates. Candidate CVs/KPIs are1,2 flow, temperature,
pressure, level, quality, speed, vapor velocity, control valve posi-
tion, distillation flood, compressor surge, furnace draft, flaring
and recycle. Many vapor velocity limits featured for oil refineries17
have been reported recently. They each exhibit tent-shaped profit
functions for their means, variability and offer opportunities for
improved financial performance.
35
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Unit profit function peak (100), Time profit function mean (28.36), Time profit
function mean (28.81), Distribution SD, (1.250), Distribution SD (0.625)
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AGO pour point clifftent start.Fig. 1

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Wilson and Sheldon18 showed the well-known compressor
operating window map with limits and interior setpoints follow-
ing the general window presented by Latour.1 These should be
set properly with clifftent. Bloch gave graphs19 for gas turbine
vibration limits: normal, problem, abnormal and dangerous.
Establishing their financial consequences for each region provides
the profit decline portion of the profit tent as vibration increases
with capacity.
Profit meters.The clifftent results constitute a profit meter8 for
each CV/KPI. The pointer should be vertical when CV setpoint is
at optimum hilltop. This long-sought solution cannot be realized
without modeling the penalties for limit violation and integrating
risk management properly. Statistical process control to arbitrary
limits like 95% is replaced. Alarm systems with simple limit trig-
gers are also replaced. Clifftent is the computational engine that
provides the decision-making power Kennedy specified for the
executive interface for the process industry.20
CV/KPI profit meters can be combined to a master meter for
each process and refinery, which shows losses due to misaligned
means. They are much more useful and valuable than account-
ing methods for refinery margin, profit/barrel crude, profit/
day, production rate, conversion, energy consumption, utilities,
operating costs and number of alarms or loops on automatic,
because the information is directly actionable for learning and
improvement. These unified profit meters also show profit from
theoretically perfect, zero-variance control and hence losses due
to variability and upsets, which might be partially mitigated
by better control and IT. In short, customized benchmarks for
perfect alignment and control provide actionable performance
measures for HPI operations.
Multivariable controllers. Multivariable control (MVC) was
commercialized for HPI processes like ACU, FCC, HCU, DCU,
blending, olefins, aromatics and polymerization in the 1980s.
Each MVC needs CV weighting factors to differentiate penal-
ties for exceeding setpoints (DMC uses ECEs). Rather than set
them by experience, clifftent was invented1,2 to determine them
scientifically. It also provided a direct measure of the financial
gain from reducing CV variance, allowing MVC (or any CIM
solution) licensing based on value-added performance.
The best hydrocracker gasoline and diesel yield is usually with
maximum recycle cutpoint within HGO spec. For well-designed
HCUs this is near the dangerous high-temperature reactor run-
away point. B. Shepard21 gives an excellent review of a host of
temperature limit tradeoffs ripe for clifftent alarm application.
Refinery CLRTO and planning LP. The author worked for
three application companies from 1980 to 1996 offering online
closed-loop real-time optimization (CLRTO) of olefin plants,
FCCs and other processes, using rigorous chemical engineering
process models (cracking chemistry, coking, distillation VLE, heat
and component mass balances, compression, pressure drop, data
reconciliation) and LP, SLP, CLP, QP or NLP profit optimizers.
Observing them commercially in action, one quickly sees they
are merely constraint set corner pickers, often selecting the same
corner for long periods.9,12,22–23
And the corner is usually obvious to a good process engi-
neer and operator. One CLRTO discovered the FCC reactor-
regenerator differential pressure should be minimum because
air blower discharge should be minimum and wet gas compres-
sor suction should be maximum. This unremarkable discov-
ery should be known to any FCC process engineer; air blower
capacity increases with lower discharge pressure; wet gas com-
pressor capacity increases with suction pressure; FCC capacity
and profit increases if both compressors are run at maximum.
CLRTO has been a disappointment because it adds little value,
costs a lot to implement and is expensive to maintain. It doesn’t
do very much.
The important issue is how to set the dependent variable
constraint values properly for an LP or NLP.23 Corner position
is more important than corner selection.23 Since an LP has no
information about the financial consequences for exceeding its
constraints, this issue is outside the realm of LP. LP-based MVC
like DMC added ECE to handle this limitation. Constraint posi-
tion setting is properly done by clifftent. The chemical engineer
modelers neglected to go beyond their process models and opti-
mizers to the plant interface to its surroundings (where value is
created or destroyed13) when limits are exceeded and determine
the tradeoff tent profit models for CVs. One large solution sup-
plier even disconnected its abnormal situation management
business from its established process-centered control business
throughout the 1990s and, not surprisingly, could not quantify
the value of their offerings.9
Isomerization incident. An isomerization unit raffinate split-
ter bottom level in Texas City, Texas, exceeded its upper alarm
limit during a 2005 startup, filling the tower, spilling to an out-
of-date vent system, causing an explosion and several deaths.13
By rigorously aligning operations to economics, HPI business
management can accomplish all 10 of the traditional proposals in
the Baker report13: leadership, safety management systems, safety
awareness, safety culture, clearly defined expectations, support for
line management, leading and lagging performance indicators,
process safety auditing, board of director’s monitoring and becom-
ing an industry leader. These well-known ideas don’t quantify
money-making, safety-reducing actions without a model-based
method for aligning HPI operations to economics, directly setting
risky targets numerically with actual knowledge of how refinery
processes behave physically and financially to maximize expected
value of net-present-value (NPV) profit for all CVs/KPIs. Model-
ing the cliff beyond raffinate splitter high level is fundamental to
isomerization operation. Modeling all inventory max.-min. limit
cliffs is fundamental to HPI operation.
Pipeline corrosion. Crude oil pipeline operating temperature
has a clifftent profit function. As temperature increases the pump-
ing capacity increases but so does the line corrosion rate. The
current value of marginal production should match the current
expected increase in maintenance costs. A corroded pipeline near
Prudhoe Bay, Alaska, leaked unexpectedly in 2006, curtailing
about 5% of US production for several weeks.
Pipeline capacity litigation. FERC ruled24 a natural gas
pipeline company withheld capacity from California because it
did not consistently operate its pipeline at or near its maximum
allowable operating pressure (MAOP) continuously, ignoring
the fact that MAOP is a safety limit, not a setpoint for average
operating pressure. Clifftent provides a rigorous mathematical
legal defense rebutting such mistaken ideas. The pipeline com-
pany lost its appeal.25
US gasoline sulfur spec. Clifftent was used to properly deter-
mine the best motor gasoline sulfur specification for the entire
US gasoline pool.26 An analytic solution provided the EPA and
lawmakers with the optimum benefit-cost tradeoff. Assuming
reasonable published refining investment and asthmatic health
credits, the target should be about 170 ppm. The losses to the
nation for using inaccurate input data were quantified. Adopting

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a standard, rigorous method like clifftent would have reduced
the litigation manpower by $100 million and the protracted
litigation process to get compliance two years earlier, saving the
country $15 billion. It provides the basis for overturning US
Supreme Court decisions based on Justice Scalia and Ginsburg
rulings that stated costs and technical feasibility do not play as
factors in federal CAAA90 decisions on how to clean the nation’s
air because they could find no way of avoiding a morass of confu-
sion because it’s too complicated.26 Sadly, the morass continued
through low-sulfur diesel specification setting in 2005. Clifftent
clears this legal morass.
Fieldbus instrumentation. Intelligent field measurement
devices now digitally communicate inexpensively, providing more
accurate, timely, reliable and useful information about process
plants.27,28 But their business challenge remains: So what? What
is fieldbus worth? How can the HPI operate better? Where will
sustained profits appear? How does one prove profit increases to
investors? How does one justify improved instruments beyond
simple cost savings, manpower reductions, government compli-
ance, everybody does it, or its wonderful so have faith? Without
a sound method for measuring the financial consequences from
improved process performance, one must resort to simple cost-
saving claims like reduced maintenance and manpower. If some-
one can determine the CV variance reduction performance of
fieldbus instruments, Clifftent can measure its financial value.1
Fear of cliff. At the outset, clifftent1,2 showed the value of
quantifying the consequences of exceeding limits, inspiring chem-
ical process control engineers to extend their modeling attention
beyond the interior process side of the profit tent to the external
side where activities like recycle, redo, repair, refund, reject, relief,
repeal, repeat, replace, reprocess, reprimand, rerun, resample,
reship, resubmit, return, reversal, revise, rework, i.e., the world
of “re”1occur.
A recent exchange11 of Letters to the Editor of Hydrocarbon
Processing among three well-published chemical process control
engineers illustrate the situation. One expressed a personal aver-
sion to running a furnace “near” its limits for fear of accident,
and then revealed he uses an arbitrary soft limit within a hard
limit supplied by others and an additional arbitrary favorite 5%
cushion for setpoints to soft limits. This is the consensus attitude
among process control engineers throughout the HPI who lack a
better way. Further, he criticized the clifftent approach of leading
people to take unjustified risks by moving setpoints too close to
dangerous and unsafe conditions (clifftent results are only based
on input economic factors; the math has no such bias). Obvi-
ously his standard ad hoc approach may be either too safe or too
dangerous because it does not optimize risky tradeoffs. So either
way looses money, guaranteed. Premeditated, calculated risk-
taking has a potential for superior average results over unforeseen,
arbitrary risk-taking.
Many control engineers use inferential modeling of stream
properties to reduce response time and variance without the far
more important financial clifftent profit tradeoff model of furnace
effects to set means correctly.
Control engineers would be better advised to consider the
purpose of furnaces: to make money.29 They should model30,31
the rates of coking, corrosion, metal fatigue, wear and tear and
probability of fire versus the influencing CV (temperature, veloc-
ity, product quality, unit capacity) and convert them to financial
costs as is routinely done for preventive maintenance27 and insur-
ance. Modeling the consequences for exceeding limits is required
to align HPI operations to economics and reverse the decline of
process control throughout the HPI.9 An example will illustrate
the value of accurate cliff data.
Value of accurate data. Clifftent analysis of atmospheric gas oil
(AGO) pour point provides the merit for improved control using
an inferred product quality for AGO/Aresid TBP cutpoint and
an accurate furnace carburization model. Since AGO is preferred
to Aresid vacuum unit feed by as much as $5/bbl, the incentive
to increase its yield by higher furnace temperature is compelling.
While capacity-limited furnaces do have severe breakdown cliffs,
sound models for increased coil coking and metal fatigue show
cost increases that eventually trump strong AGO yield gains well
before dangerous cliffs occur.30 Nevertheless, this example will
show the significance of an abnormally large cliff.
A careful tradeoff optimization in Table 1 shows the credit for
reducing pour point standard deviation 50% (with a better quality
inferential model, multivariable controller or a fieldbus pour point
analyzer), or by 40%, and the value if operated using the correct
40% rather than incorrect 50%. Table 1 also shows the proper
operation if the cliff is –30 compared to incorrect –50. Input
assumptions are AGO pour point mean is 28°C, standard devia-
tion is SD = 1.25°C, specification is max 30°C, AGO production
is 20,000 bpd and profit tent sensitivities are in Table 1.
The Start column shows the mean should be increased to
28.36 for $729/day (Fig. 1). The SD 1/2 column shows 50%
SD reduction provides $6,952/day dynamic benefit at 28.36 and
an additional $1,494/day if the mean is increased to 28.81 (Fig.
2). The SD 3/5 column shows the corresponding results if SD
were reduced only 40%, the mean should be 28.67 (Fig. 3). (Of
course clifftent cannot determine which input is correct, except
if its results are unrealistic.) The Value column shows if operat-
ing at the correct 40% mean 28.67, compared to incorrect 50%
mean 28.81, the gain is $146/day. So this determines the value
of operating with the correct input SD. Note absolute profits and
dynamic benefit are less in the SD 3/5 column than in the tighter
control SD 1/2 column. We always see greater gains from the first
Table 1. AGO pour point
Inputs Start SD 1/2 SD 3/5 Value Start 2 SD 1/2 Value
Spec °C 30
Unit profit $/bbl 5
Capacity kbpd 20
Slope left 0.4
Slope right –0.5
Curve left –4
Curve right –5
Cliff k$/day –50 –30
Mean °C 28.00 28.36 28.36 28.81 28.00 28.67 28.81
Std dev °C 1.250 0.625 0.750 0.750 1.250 0.625 0.625
Results
Profit $/day 85,269 92,951 92,097 92,708 86,358 94,606 95,003
Dynamic $/day – 6,952 6,099 – – 6,280 –
OptMean °C 28.36 28.81 28.67 28.67 28.67 28.94 28.94
OptProfit $/day 85,999 94,445 92,854 92,854 88,326 95,146 95,146
Mean $/day 729 1,494 757 146 1,968 540 143
Total $/day 729 8,447 6,856 146 1,968 6,820 143

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HYDROCARBON PROCESSING december 2008
I 107
30% reduction in standard deviation than from the second 30%;
the law of diminishing returns prevails for process control.
The Start 2 column shows if the furnace model cliff was
-30 rather than -50, the mean should be increased to 28.67 for
$1,968/day (Fig. 4). (The 28.67 here and in the SD 3/5 column
is a coincidence.) The SD 1/2 column shows 50% SD reduction
provides $6,280/day dynamic benefit at 28.67 and an additional
$540/day if the mean increased to 28.94. The Cliff value column
shows if operating at correct -30 cliff mean 28.94, compared to
incorrect -50 cliff mean 28.81, the gain is $143/day. So this deter-
mines the value of operating with the correct input cliff. Note
absolute profits are greater and dynamic benefit is less in the -30
cliff SD 1/2 column than in the -50 cliff SD 1/2 column.
Of course the magnitudes of the results may be small; but that
they can be determined from first principles and confirm intuitive
directions are very relevant. This proves one can determine the
financial gain from using more accurate input data. This satisfies
a basic need of the IT business; allowing it to mature from a cost
to a profit center. Clifftent also shows the significant input data
that should be supplied by any modern IT system; the CV mean
profit tradeoff tent with any cliff.
The value of accurate data depends on their use with some
adjustment algorithm like clifftent to align operations to econom-
ics. Unused accurate data are worthless.
Requirements. CV/KPI mean, variance and distribution,
readily available from control system historians since the 1970s,
are useful for operators to forecast near-term variance.
HPI plants have sound chemical engineering process models
for the physical effects of changing CV/KPI average values; these
are multiplied by economic sensitivities like differential product
values to get the financial sensitivity of CVs/KPIs. Most plants
also have long experience with the financial consequences of
exceeding limits, abnormal situations, equipment wear and tear,
customer dissatisfaction, HAZOP, flaring, safety and emission
noncompliance.
Modern IT and CIM systems have, or should have, provided
this information to process operators and managers since the
early 1990s. It should be captured in clifftent profit sensitivity
functions for every CV/KPI, forecast for near-term guidance and
used to properly, optimally set CV setpoints. Frequent resetting
of setpoints by clifftent ensures proper real-time alignment of
operating conditions to economics.
CIM cash flows. CIM benefit claims in 1990 were around
1 $/bbl crude refined,9,17,32 depending on plant and economic
complexity and variability, but these could never be adequately
verified, leading to great disappointment. Regularly resetting
setpoints accurately with clifftent in 2006 delivers >2 $/bbl crude
profit (= benefit – cost) to operating companies that know what
they are doing with little cost or risk.9,17,32 Typical cash flows,
like a 30-year mortgage, for a 300-kbpd refinery are shown in
Tables 2–5.
Project only. Table 2 illustrates the conventional quick payout
installation project approach without maintenance, reported by
35
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Unit profit function peak (100), Time profit function mean (28.36), Time profit
function mean (28.81), Distribution SD, (1.250), Distribution SD (0.625)
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AGO pour point clifftent SD 1/2.Fig. 2
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AGO pour point clifftent SD 3/5.Fig. 3

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process control, IT and CIM project proponents for decades.4,9,32
What’s wrong with this picture?
• No maintenance: Nothing can generate value for long with-
out proper, sustained human involvement. NPV values are math-
ematical figments of imagination, probability of realization only
0.1. Client NPV(30y) reduced from $2,317 million to expected
$231 million.
• Supplier profit share of expected benefit is only 0.07% and
of potential $2,318 million benefit, cost is only $0.190 million
or 0.008%, grossly unfair and unrealistic. Expected profit is only
$0.170 million, a ridiculously small amount from such a serious
endeavor. At least the supplier profit is early and quite secure,
because all commercial risk is with the client.
A recent 16-month olefin plant (300 ktpy C2=) optimizer
project33 claimed a $12.5 million/year profit increase from higher
production for a one-month investment payback without future
maintenance. This means the benefit was 1.9 US cts/lb C2=,
investment was $1.042 million and client profit NPV(12.5, 30y,
10%) = about $110 million. If the supplier’s margin is 10%, its
profit NPV = $0.1 million. This seems rather small. Clifftent can
be used to measure, confirm and sustain such performance.34
Project and maintenance. Table 3 illustrates the conventional
quick payout project approach with minimum-cost maintenance,
done by supplier or client. The probability of full realization
is markedly higher, client-expected profit jumps from $231 to
$1,154 million and supplier-expected profit jumps from $0.17
to $2.11 million. Both win a lot. What’s wrong?
• Maintenance is not profit driven. Probability of realization
is still too low because supplier incentive to identify, capture and
sustain is missing.
• Supplier profit share is only $2.11 million, 0.18% of
expected benefit and 0.09% of potential benefit. All commercial
risk remains with client.
Benefit split. Table 4 illustrates the situation when the CIM
solution provider assumes all commercial risk in exchange for
a percentage of benefit created, a shared risk—shared reward
(SR2) business alliance that recognizes client and supplier are
not adversaries, but long-term partners to identify, capture
and sustain profits for both from improved HPI plant opera-
tion.4,9,32,34 Now the benefit measurement method for the 2 $/
bbl profit and the agreed % share become of overriding impor-
tance. The supplier grants the client 85% of the benefit the
supplier creates with no client cost or risk. The probability of
realization is very high. The client can expect $1,872 million;
the supplier $328 million. Both win a lot more. In fact the
client’s net cash flow is always positive; this is a free, no-risk,
variable annuity. What’s wrong? Not much. Commercial risk is
well aligned with know-how, providing the client with a proper
performance guarantee.
• Some knowledgeable clients may not like their supplier to
have sole authority and responsibility for costs.
35
3524 25 26 27 28 29
Unit profit function peak (100), Time profit function mean (28.36), Time profit
function mean (28.67), Distribution SD, (1.250), Distribution SD (0.750)
30 31 32 33 34
35
40
40
40
45
45
45
50
50
50
55
55
55
60
60
60
65
65
65
70
70
70
75
75
75
80
80
80
85
85
85
90
90
90
95
95
95
100
100
100
0
0
1
1
2
2
3
3
4
4
5
5
6
6
7
7
8
8
9
9
10
10
11
11
12
12
13
13
AGO pour point clifftent start 2.Fig. 4
Table 2. Project cash flow—kk$/qtr
Typical fuels oil refinery: crude kbpd = 300 Benefit, $/bbl crude = 2.0
Install only, zero maintenance TVM, %/y = 8.0
Client Supplier Supplier
End qtr Benefit Price* net cost net
Q0 0 0.10 -0.10 0.09 0.01
Q1 0 0.10 -0.10 0.06 0.04
Q2 0 0.10 -0.10 0.06 0.04
Q3 16.20 0.10 16.10 0.06 0.04
Q4 32.40 0.10 32.30 0.03 0.07
NPV (1y) 45.20 0.48 44.72 0.29 0.19
Q5 54.00 0 54.00 0 0
Q6 54.00 0 54.00 0 0
.... – – – – –
.... – – – – –
Q40 54.00 0 54.00 0 0
NPV (2-10y) 1,271.58 – 1,271.58 – 0
NPV (10y) 1,316.78 0.48 1,316.30 0.29 0.19
% benefit 100.00 0.04 99.96 0.02 0.01
Q41 54.00 0 54.00 0 0
Q42 54.00 0 54.00 0 0
.... – – – – –
.... – – – – –
Q80 54.00 0 54.00 0 0
NPV (20y) 2,001.00 0.48 2,000.52 0.29 0.19
% benefit 100.00 0.02 99.98 0.01 0.01
NPV (30y) 2,317.93 0.48 2,317.45 0.29 0.19
% benefit 100.00 0.02 99.98 0.01 0.01
Probability 0.10 0.95 0.100 0.98 0.904
Expected NPV 231.79 0.46 231.34 0.28 0.17
Expect % benefit 100.00 0.20 99.80 0.12 0.07
* Nonrefundable, not at risk Bold inputs

7. process control/informaTion systems
HYDROCARBON PROCESSING december 2008
I 109
• There may be further opportunity to increase expected prof-
its by true profit sharing.
• There exists an optimum or best % split for each situation,4
depending on many risk-mitigating factors acquired from long-
term, worldwide HPI CIM experience.
Profit split. Table 5 illustrates the situation when the CIM
solution provider assumes commercial risk for performance but
shares costs in a profit-sharing arrangement, an extended SR2
alliance. The supplier grants the client 84% of the profit. The
probability of realizing the true profit potential is even higher.The
client can expect $1,906 million; the supplier $363 million. Both
maximize their expected NPV(30y) profit streams. What’s wrong?
Not much. Risk is properly aligned with knowhow.
• The supplier must disclose its costs and profits, which it may
be reluctant to do with some clients.
• The client is out a modest expense, $0.180 million after six
months.
Obviously benefit and cost will vary quarter to quarter over
long periods. Probabilities are inaccurate without appropriate
experience. These tables are intended to illustrate ways to quan-
tify financial partnerships for sustaining CIM solutions. They
have been proposed for compensation to research, engineering
and marketing divisions of large operating companies that offer
process control, IT and CIM to their affiliates, joint ventures and
outside clients.32
Clifftent32 provides the performance metric to allow CIM
solution providers to identify, capture and sustain significant ben-
efits for their clients and themselves by aligning their know-how
with commercial risk, SR2.
Results. The conclusions and summary that result from these
ideas have lasting importance for HPI operating best practices.
Conclusions. Some important conclusions are offered to help
run HPI plants by aligning setpoints to economics.
• Set setpoints optimally with clifftent and measure the finan-
cial value of change. Do this whenever any input changes.
• Measure the financial value of variance reduction from pro-
cess measurement, control and IT solutions and components
with clifftent. All they can do is modify the distribution position
and shape.
• Measure the financial value of accurate process and economic
sensitivity data with clifftent. Specify the critical data needed for
plant operating decisions with clifftent. Quantify the financial
losses from using erroneous data, assumptions and models.
• Never invest in instruments, controllers, optimizers, databases,
IT or CIM unless the solution supplier and customer concur on the
Table 3. Project cash flow—kk$/qtr
Typical fuels oil refinery: crude kbpd = 300 Benefit, $/bbl crude = 2.0
Installation and maintenance: Price = 0.10 Cost = 0.05 TVM, %/y = 8.0
Client Supplier Supplier
End qtr Benefit Price* net cost net
Q0 0 0.10 -0.10 0.09 0.01
Q1 0 0.10 -0.10 0.06 0.04
Q2 0 0.10 -0.10 0.06 0.04
Q3 16.20 0.10 16.10 0.06 0.04
Q4 32.40 0.10 32.30 0.03 0.07
NPV (1y) 45.20 0.48 44.72 0.29 0.19
Q5 54.00 0.10 53.90 0.05 0.05
Q6 54.00 0.10 53.90 0.05 0.05
.... – – – – –
.... – – – – –
Q40 54.00 0.10 53.90 0.05 0.05
NPV (2-10y) 1,271.58 2.35 1,269.22 1.18 1.18
NPV (10y) 1,316.78 2.84 1,313.94 1.47 1.37
% benefit 100.00 0.22 99.78 0.11 0.10
Q41 54.00 0.10 53.90 0.05 0.05
Q42 54.00 0.10 53.90 0.05 0.05
.... – – – – –
.... – – – – –
Q80 54.00 0.10 53.90 0.05 0.05
NPV (20y) 2,001.00 4.10 1,996.90 2.10 2.00
% benefit 100.00 0.21 99.79 0.11 0.10
NPV (30y) 2,317.93 4.69 2,313.24 2.40 2.29
% benefit 100.00 0.20 99.80 0.10 0.10
Probability 0.50 0.95 0.499 0.98 0.919
Expected NPV 1,158.97 4.46 1,154.51 2.35 2.11
Expect % benefit 100.00 0.38 99.62 0.20 0.18
* Nonrefundable, not at risk Bold inputs
Table 4. Project cash flow—kk$/qtr
Typical fuels oil refinery: crude kbpd = 300 Benefit, $/bbl crude = 2.0
(SR)2 Benefit split: client % = 85 Supplier % = 15 TVM, %/y = 8.0
Client Supplier Supplier
End qtr Benefit Price* net cost net
Q0 0 0.00 0.00 0.09 -0.09
Q1 0 0.00 0.00 0.06 -0.06
Q2 0 0.00 0.00 0.06 -0.06
Q3 16.20 2.43 13.77 0.06 2.37
Q4 32.40 4.86 27.54 0.03 4.83
NPV (1y) 45.20 9.04 36.16 0.29 8.75
Q5 54.00 8.10 45.90 0.05 8.05
Q6 54.00 8.10 45.90 0.05 8.05
.... – – – – –
.... – – – – –
Q40 54.00 8.10 45.90 0.05 8.05
NPV (2-10y) 1,271.58 190.74 1,080.84 1.18 189.56
NPV (10y) 1,316.78 197.52 1,119.26 1.47 196.05
% benefit 100.00 15.00 85.00 0.11 14.89
Q41 54.00 8.10 45.90 0.05 8.05
Q42 54.00 8.10 45.90 0.05 8.05
.... – – – – –
.... – – – – –
Q80 54.00 8.10 45.90 0.05 8.05
NPV (20y) 2,001.00 300.15 1,700.85 2.10 298.05
% benefit 100.00 15.00 85.00 0.11 14.89
NPV (30y) 2,317.93 347.69 1,970.24 2.40 345.29
% benefit 100.00 15.00 85.00 0.10 14.90
Probability 0.95 0.950 0.950 1.00 0.950
Expected NPV 2,202.04 330.31 1,871.73 2.40 327.91
Expect % benefit 100.00 15.00 85.00 0.11 14.89
* Nonrefundable, not at risk Bold inputs

8. process control/information systems
110
I december 2008 HYDROCARBON PROCESSING
measureoffinancialperformanceofsuchinvestmentssothatadequate
profitwillbegenerated.Clifftentistherigorousscorekeeper.Thisisan
example of the adage—never engage in a competitive contest without
understanding and concurrence on scorekeeping for success.
• Set constraint values for LP planners and online, closed-
loop, real-time optimizers with clifftent.
• Connect dissimilar models for the plant and its surround-
ings, like abnormal situations, alarms, maintenance, safety, envi-
ronment and customers with clifftent for each CV/KPI.
• Unify dissimilar objectives like yield, quality, energy, capac-
ity, run length, reliability, maintenance, environmental com-
pliance, safety and delivery to one objective to be maximized:
expected NPV profit rate, with clifftent.
• Deploy best-practice techniques like modeling, multivari-
able control, risk management, setpoint optimization, statistical
quality control, alarm management, six-sigma quality and CIM
comprehensively with clifftent.
• Build rigorous risk management into setting of all CV/KPI
setpoints with clifftent.
• The basic operator job is to use historic variance experience
for short-term forecasting of variance and resetting targets accord-
ingly; closer to constraints when smooth operation warrants and
backing off when upsets are imminent.
• Use the rigorous clifftent method for operating HPI plants
for maximum expected NPV profit from near-term forecasts of
variance (operators), process behavior (engineering), limit viola-
tion penalties (specialists) and economics (business), to selected
CV/KPI (profit center management).
• Recognize there is a nonzero optimum frequency of limit vio-
lations (Figs. 1–4); confront, model and mitigate them. Learn from
mistakes by improving clifftent input data accuracy for improved
future profit. Develop and sustain a culture of assessing the finan-
cial consequences of exceeding limits and specifications.
Clifftent is not a new paradigm for operation, it really is a
rigorous method for operating as people do now, optimizing risky
tradeoffs by breaking the problem into components, using tested
scientific principles of comprehensive mathematical modeling,
accurate data, statistics, risk management, appropriate human
values in economic tradeoffs and optimization for best operating
practice. It allows operators to view results and inputs holisti-
cally, gain consensus with knowledgeable experts, inform input
data suppliers of the use and significance of their data, inform
all involved of the consequences of clifftent setpoint setting and
financial performance measurement. It’s the best way to align
setpoints to economics for those who know how to run HPI
process plants. That’s the best way to identify, capture and sustain
significant profit growth from CIM.1,2,4,6–9,14–17,32,34
Remember some fundamentals:
• Risk: Don’t be afraid to go out on a limb, that’s where the
fruit is.
• I like my porridge not too hot and not too cold, but just
right. Goldilocks.
• It’s always better to play it on the safe side. Greek philosopher,
430 BC.
• Never play a game until you know how to score, to deter-
mine whether you win or lose. Kindergarten teacher.
• Show your customer the benefit of your offering. Sidewalk
lemonade salesman. HP
Literature cited
1 Latour, P. R., “Process control: CLIFFTENT shows it’s more profitable than
expected,” Hydrocarbon Processing, V75, n12, December 1996, pp. 75–80.
Republished in Kane, Les, Ed, “Advanced Process Control and Information
Systems for the Process Industries”, Gulf Publishing Co., 1999, pp. 31–37.
2 Latour, P. R., “CLIFFTENT: Determining Full Financial Benefit from
Improved Dynamic Performance,” Paper C01,Third International Conference
on Foundations of Computer-Aided Process Operations, Snowbird, Utah,
July 5–10, 1998. Proceedings published in AIChE Symposium Series No. 320,
V94, 1998, pp. 297–302.
3 Sharpe, J. H. and Latour, P. R., “Calculating Real Dollar Savings from
Improved Dynamic Control,” Texas AM University Annual Instruments
and Controls Symposium, College Station, Texas, January 23, 1986.
4 Latour, P. R., “Does the HPI do its CIM business right?” HP InControl
Guest Columnist, Hydrocarbon Processing, V76, n7, July 1997, pp. 15–16 and
“Optimize the $19-billion CIMfuels profit split,” V77, n6, June 1998, pp.
17–18.
5 Latour, P. R., “Decisions about risk reduction,” Letter to Editor, Hydrocarbon
Processing, V80, n6, June 2001, p. 39.
6 Latour, P. R., “Quantifying financial values,” HP InControl Guest Columnist,
Hydrocarbon Processing, V80, n7, July 2001, pp. 13–14.
7 Latour, P. R., “Why Invest in PROCESS CONTROL?” CONTROL, Vol.
XV, n5, May 2002, pp. 41–46.
8 Latour, P. R., “Why tune control loops? Why make control loops?,” guest
columnist editorial, Hydrocarbon Processing, V81, n9, September 2002, pp.
11–12.
9 Latour, P. R., “Demise and keys to the rise of process control,” Hydrocarbon
Processing, V85, n3, March 2006, pp. 71–80 and Letters to Editor, Process
Control, Hydrocarbon Processing, V85, n6, June 2006, p. 42.
10 Coughanowr, D. R., L. B. Koppel, Process Systems Analysis and Control,
McGraw-Hill, 1965.
11 Friedman, Y. Z., G. D. Martin, P. R. Latour, “Letters on Solomon’s APC
Table 5. Project cash flow—kk$/qtr
Typical fuels oil refinery: crude kbpd = 300 Benefit, $/bbl crude = 2.0
Premier(SR)2 Profit split: client % = 84 Supplier % = 16 TVM, %/y = 8.0
Client Supplier Supplier
End qtr Benefit Price* net cost net
Q0 0 0.08 -0.08 0.09 -0.01
Q1 0 0.05 -0.05 0.06 -0.01
Q2 0 0.05 -0.05 0.06 -0.01
Q3 16.20 2.64 13.56 0.06 2.58
Q4 32.40 5.21 27.19 0.03 5.18
NPV (1y) 45.20 7.48 37.72 0.29 7.19
Q5 54.00 8.68 45.32 0.05 8.63
Q6 54.00 8.68 45.32 0.05 8.63
.... – – – – –
.... – – – – –
Q40 54.00 8.68 45.32 0.05 8.63
NPV (2-10y) 1,271.58 204.44 1,067.14 1.18 203.26
NPV (10y) 1,316.78 211.92 1,104.86 1.47 210.45
% benefit 100.00 16.09 83.91 0.11 15.98
Q41 54.00 8.68 45.32 0.05 8.63
Q42 54.00 8.68 45.32 0.05 8.63
.... – – – – –
.... – – – – –
Q80 54.00 8.68 45.32 0.05 8.63
NPV (20y) 2,001.00 321.93 1,679.08 2.10 319.82
% benefit 100.00 16.09 83.91 0.11 15.98
NPV (30y) 2,317.93 372.88 1,945.05 2.40 370.49
% benefit 100.00 16.09 83.91 0.10 15.98
Probability 0.98 0.980 – 0.90 –
Expected NPV 2,271.58 365.26 1,906.31 2.16 363.11
Expect % benefit 100.00 16.08 83.92 0.09 15.98
* Nonrefundable, not at risk Bold inputs

9. process control/informaTion systems
HYDROCARBON PROCESSING december 2008
I 111
Survey,” Hydrocarbon Processing, V85, n10, October 2006, pp. 45–46.
12 Friedman, Y. Z. (and Latour, P. R.), “Dr. Pierre Latour’s views on APC,” HPIn
Control editorial, Hydrocarbon Processing, V84, n11, November 2005, pp.
17–18.
13 Baker, J., et al, “The Report of the BP U.S. Refineries Independent Safety
Review Panel,” January 2007.
14 Stout, Thomas M., “Economic Justification of Computer Control Systems,”
Automatica, V9, 1973, pp. 9–19.
15 Stout, T. M. and R. P. Cline, “Control System Justification,” Instrumentation
Technology, V23, n9, September 1976, pp. 51–58.
16 Stout, Tom, “Hidden Benefits of Computer Control- TRW, 1960s,” personal
communication to Pierre Latour, November 5, 1976.
17 Latour, P. R., “Set vapor velocity setpoints properly,” Hydrocarbon Processing,
V85, n10, October 2006, pp. 51–56.
18 Wilson, J. and A. Sheldon, “Matching antisurge control valve performance
with integrated turbomachinery control systems,” Hydrocarbon Processing,
V85, n8, August, 2006, pp. 55–58.
19 Bloch, H. P., “Vibration limits for gas compressors,” Hydrocarbon Processing,
V85, n10, October 2006, p 11.
20 Kennedy, J. P., “Executive interface for the process industry,” Petroleum
Technology Quarterly, Summer 2002, pp. 113–117.
21 Shepard, B., “Controlling hydrocracker reactor temperature,” Hydrocarbon
Processing, V85, n10, October 2006, pp. 75–80.
22 McMahon,T. K. (and Latour, P.R.), “CLIFFTENT For Process Optimization,”
CONTROL, V17, n12, December 2004, p. 66.
23 Hartmann, J. C. M., “Ramp up LP models with audits and transparent
reporting methods,” Hydrocarbon Processing, V83, n9, September 2004, pp.
91–96; “Decision-making and modeling in petroleum refining,” Hydrocarbon
Processing, V76, n11, November 1997, pp. 77–81.
24 Wise, William A., “An Open Letter from El Paso,” Houston Chronicle,
October 9, 2002.
25 Davis, M., “CEO Wise begins swan song, prepares to exit El Paso,” Houston
Chronicle, February 12, 2003.
26 Latour, P. R., “What is the optimum U.S. mogas sulfur content?,” Hydrocarbon
Processing, V81, n11, November 2002, pp. 45–50.
27 Kane, L. A., et, al., “HPI Market Data Book 2007,” Hydrocarbon Processing,
September 2006, pp. 31–39.
28 Yu, H. B. and M. Peluso, “Fieldbus enables single-loop integrity with control-
in-the-field,” Hydrocarbon Processing, V85, n10, October 2006, pp. 69–72.
29 Friesen, T. and R. Patel, “Upgrade your furnace for clean fuels,” Hydrocarbon
Processing, V85, n9, September 2006, pp. 77–82.
30 Aguado, J. M., “Minimize furnace tubes carburization,” Hydrocarbon
Processing, V85, n9, September 2006, pp. 43–48.
31 Hamidi, A. A., S. Gargani and P. Dehghani “Optimizing crude oil fired heat-
ers,” Hydrocarbon Processing, V85, n9, September 2006, pp. 113–117.
32 Latour, P. R., “CIMFUELS,” bi-monthly contributing editorial, FUEL
Reformulation, September 1995–February 1998.
33 Vettenranta, J., M. Sourander, et. al., “Dynamic real-time optimization
increases ethylene plant profits,” Hydrocarbon Processing, V85, n10, October
2006, pp. 59–66.
34 Latour, P. R., “Align Olefin Operations to Economics—Clifftent optimizes
setpoints,” presented at 2007 Spring AIChE Meeting Ethylene Producers
Conference, Houston, Texas, April 23, 2007. Published in Conference
Proceedings CD.
Pierre R. Latour, President of CLIFFTENT Inc., is an inde-
pendent consulting chemical engineer specializing in identify-
ing, capturing and sustaining measurable financial value from HPI
dynamic process control, IT and CIM solutions (CLIFFTENT) using
performance-based shared risk–shared reward (SR2) technology
licensing. Dr. Latour justified and installed advanced control on most oil refinery and
petrochemical processes for 62 clients worldwide since 1966. He was a vice presi-
dent of business development, marketing, engineering, project implementation and
consulting at AspenTech, Dynamic Matrix Control Corp, Setpoint and Biles Assoc.
Dr. Latour was chairman of Setpoint Japan. He cofounded the last three firms. Dr.
Latour was an engineer at Shell Oil and DuPont, served as Captain in the US Army
and Apollo Program Simulation Branch Manager, NASA, Houston. He has authored
67 publications and was CIMFUELS editor for Fuels. Dr. Latour is a registered PE, was
CONTROL magazine Engineer of the Year in 1999 and Purdue University Outstand-
ing Chemical Engineer in 2007. He holds BS, ChE, VaTech, MS and PhD degrees in
chemical engineering from Purdue University.