1. sr2@msn.com
Pierre R. Latour, GUEST COLUMNIST
HPIN Control
Allan Kern, Zak Friedman and I have offered numerous ideas
to strengthen the practice of process control.1-4 Allan Kern inau-
gurated a fundamental assessment of process control engineer-
ing practice in the HPI since 1990.2 His experience maintaining
control systems in multiple refineries reveals that many challenges
remain. He offered an excellent summary of what we have learned.2
He reports the base layer, Field in Fig. 1, is healthy but not suffi-
ciently functional. “It is clearer every year that focus on the base layer
is the most urgent and promising strategy, and greatest opportunity, to
bring about fundamental improvements in operation and reliability
going forward.” The only way to build is from the bottom up.
Kern may be right, but identifying and curing the root cause of
failure is fundamental. I hope to supplement Kern’s recommenda-
tions with my own, as a consultant to many refineries for short
periods since 1966. I see an opportunity to renew the practice of
process control engineering in the new decade by analyzing the
primary causes of the disappointing performance shortfalls across
all layers of Fig. 1 and correcting basic flaws in the engineering
approach from the top-layer DSS down.1 Kern and I will try to
strengthen our profession and HPI operations.
I place much blame on flawed logic used to quantify the finan-
cial value of all process control and associated operations’ IT since
1970 that caused crippling disconnects between the layers. I
shall promote the view of an architect before construction begins
beyond after the house has been lived in, an engineer before a
bridge is built beyond after the bridge is used, a chef before din-
ner is prepared beyond after guests have dessert. I shall emphasize
identifying, capturing and sustaining significant value from any
process operation tool, technology or solution. The span of Fig.
1 is from basic measurements and actuators to refinery IT eco-
nomics. Proper goals, measurable performance, risks and costs,
including manpower, will count every step of the way. This is not
quite a top-down approach; it’s holistic.
Why the base layer has been weak. First, consider
why the base layer has been so weak for so long. Either it adds
insufficient tangible value (because it costs too much to fix and
maintain or doesn’t do much good) or engineers have failed to
properly quantify that value to justify maintenance and improve-
ment while piling on more layers. When the base layer is too far
removed from the DSS to run the plant properly and influence its
financial performance, people often resort to simple FaithTheory3
claims that have worn thin long ago. When they do attempt to
relate base-layer activity to process performance, they use flawed
logic,4 losing credibility. In other words, if the financial case for
the healthy field layer Kern desires is clear and compelling, it
should be made and confirmed regularly.
It’s like justifying tires without considering the value of the car,
lettuce without considering the salad and entrée. As Kern affirms,2
it cannot be done in isolation.
Engineering practice renewal approach. The engineering
practice renewal approach starts at the beginning, combining
all layers.
• Agree on how to operate HPI processes properly. All we
can do is select appropriate CV/KPI response variables, measure
them and modify their means and variances (distribution) to suit
our purposes.
• Agree on the purpose of HPI operations: maximum expected
value profit rate.
• Define the economic tradeoff sensitivity for every CV/KPI,
to allow alignment with the plant’s economic environment.
• Relate the main process control functions, components
and layers to CV mean or variance changes, and those changes to
average profit rate.
It is clear that focus from the top DSS layer is a promising
strategy to bring about fundamental improvements in opera-
tion and reliability going forward, with all layers. Analysis before
synthesis, always. In the end, Kern and I will unite to provide
guidelines for renewing the practice of process control engineering
during refinery golden ages and struggles. HP
LITERATURE CITED
1 Kern, Allan, “More on APC designs for minimum maintenance,” HP, Dec.
2009.
2 Kern, Allan, “Back to the Future: A Process Control Strategy for 2010,” HP,
Feb. 2010.
3 Latour, P.R., “Demise and keys to the rise of process control,” HP, March
2006, pp. 71–80 and Letters to Editor, Process Control, HP, June 2006, p.
42.
4 Latour, P.R., “Process control: CLIFFTENT shows it’s more profitable than
Expected,” HP, 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.
Process control practice renewal 2010
DSS
MPC
DCS/SIS
Field
• Decision-support applications
– Performance monitoring
– Operating target dashboards
– Alarm management, etc.
• Multivariable predictive control
• Distributed control systems
• Safety instrumented systems
• Work practices
• Process
• Equipment
• Field devices
Automation layers.Fig. 1
The author, president of CLIFFTENT Inc., is an independent consulting chemical
engineer specializing in identifying, 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.
94
I APRIL 2010 HYDROCARBON PROCESSING

2. HPIN CONTROL
The author is a principal consultant in advanced process control and online
optimization with Petrocontrol. He specializes in the use of first-principles models
for inferential process control and has developed a number of distillation and reactor
models. Dr. Friedman’s experience spans over 30 years in the hydrocarbon industry,
working with Exxon Research and Engineering, KBC Advanced Technology and since
1992 with Petrocontrol. He holds a BS degree from the Israel Institute of Technology
(Technion) and a PhD degree from Purdue University.
HYDROCARBON PROCESSING AUGUST 2010
I 13
clifftent@hotmail.com
PIERRE R. LATOUR, GUEST COLUMNIST
HPIN CONTROL
In April 2010, I followed Allan Kern’s automation reassessment
editorial1 with a call to renew the practice of process control and
IT in the HPI. Now I follow his thoughtful July 2010 editorial2 on
continuous improvement or core competency. After 50 years, it is
time to restart with a reminder of the purpose of instrumentation,
control systems, IT and CIM, critique weaknesses and offer ways
to strengthen their financial performance.
As I write this, BP is struggling to contain its Deepwater Horizon
well and operate its Texas City, Texas, Refinery. Refiners are strug-
gling for profitability and survival worldwide. The current HPI
operating problem is not instruments, valves, control algorithms,
tuning, modeling, alarm management, displays, computers, KPIs,
best practices, Six-Sigma, ISO9000, SQC, software, informa-
tion, technology, maintenance, training, management, organiza-
tion, awareness or culture. These are useful ideas that should be
converted to mathematically based actions with the appropriate
performance measure.
It’s KNOW HOW. Lack thereof. Insufficient competency.The
HPI needs the knowhow to identify, capture and sustain maximum
expected value profits to always operate right.
Situation. HPI plants are operated by adjusting process oper-
ating conditions: setpoints, specs and limits on controlled vari-
ables (CVs) and key performance indicators (KPIs). All we can
do is specify a CV mean and reduce its variance. While process
control does the latter, there is no standard method for the
former, so it is done by human experience. Therefore, the pro-
cedure for assessing the value of reduced variance or dynamic
performance is incomplete and invalid. Process control, IT and
CIM continue to suffer from a lack of a rigorous standard finan-
cial performance measure. People do not agree on the purpose
of systems and how to keep score; like whether a touchdown is
worth 6 points or 5.
Purpose. The purpose of tools, products, layers and systems
is to operate plants better: safely and efficiently, as measured
by long-term profitability. This is done by identifying CV/KPI
measurements that affect profitability, specifying setpoints that
optimize the risky financial tradeoffs associated with each and
controlling them tightly about those optimum setpoints. My
assumption is the only thing operators can affect are process
operating conditions (mean and variance), encompassed by suf-
ficient CVs/KPIs. While the knowhow for step 3 has been com-
mercialized since 1960, the failure to adopt a standard method3,
4 for step 2 impedes our ability to relate CVs/KPIs to financial
performance. This causes confusion for step 1, inability to specify
appropriate models and IT, ad hoc estimates of financial value of
step 3 and inability to capture benefits from step 2 with IT.
In April 2010 I blamed flawed logic used to quantify the
financial value of all process control and IT since 1970 as a basic
cause of the crippling disconnects between the layers, compo-
nents and technologies.
Kern writes about core competencies for operational excel-
lence.2 The method for establishing setpoints to optimize risky CV
tradeoffs shows what those core competencies should be and pro-
vides the information requirements they should provide for operat-
ing excellence.The important inputs are near-term forecasts of CV
uncertainties (variance from data historians), process and economic
sensitivities to CV means and limit violation consequence cliffs
(from models and business). This is the framework for IT require-
ments and continuous improvement to determine and maintain
this information in real time and act upon it faithfully.
New idea. The basic idea is to direct the attention of HPI oper-
ations management to the rigorous way3 to determine setpoints
to maximize expected value profit from risky tradeoffs for every
meaningful CV/KPI. This is essential to deal with disasters plagu-
ing the HPI like refinery explosions, drill-hole well leaks and
environmental damage, while maximizing real profit potential.
Focusing on subcategory objectives like energy, yield, capacity,
quality, inventory, safety, manpower and technology, rather than
optimizing the risky financial tradeoffs among them is a basic
handicap to success because they are all connected. While safety
violations can never be eliminated permanently, surprises can
be reduced, remedy plans deployed and learning from mistakes
strengthened. Taking intelligent calculated risks is preferable to
taking unintelligent uncalculated risks.
Adopting the rigorous method3 for setpoints that optimize
risky tradeoffs provides the way to evaluate the value of instru-
ments, components, layers, models, IT and solutions. This is
the proper path to renewal and success. In the end, Kern1, 2
and Latour3, 4 will unite to provide guidelines for renewing the
practice of process control engineering during refinery golden
ages and downturns. HP
LITERATURE CITED
1 Kern, Allan, “Back to the Future: A Process Control Strategy for 2010,”
Hydrocarbon Processing, February 2010.
2 Kern, Allan, “Continuous improvement or core-competency,” Hydrocarbon
Processing, July 2010.
3 Latour, P. R., “Process control: CLIFFTENT shows it’s more profitable than
expected,” Hydrocarbon Processing, 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.
4 Latour, P. R., “Demise and keys to the rise of process control,” Hydrocarbon
Processing, March 2006, pp. 71–80 and Letters to Editor, Process Control,
Hydrocarbon Processing, June 2006, p. 42.
Process control practice renewal 2010—purpose
The author, president of CLIFFTENT Inc., is an independent consulting chemical
engineer specializing in identifying, 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.

3. HPIN CONTROL
The author is a principal consultant in advanced process control and online
optimization with Petrocontrol. He specializes in the use of first-principles models
for inferential process control and has developed a number of distillation and reactor
models. Dr. Friedman’s experience spans over 30 years in the hydrocarbon industry,
working with Exxon Research and Engineering, KBC Advanced Technology and since
1992 with Petrocontrol. He holds a BS degree from the Israel Institute of Technology
(Technion) and a PhD degree from Purdue University.
HYDROCARBON PROCESSING OCTOBER 2010
I 15
clifftent@hotmail.com
PIERRE R. LATOUR, GUEST COLUMNIST
HPIN CONTROL
In August 2010, I followed Allan Kern’s “Continuous
Improvement or Core-Competency editorial”1 with a review of
the purposes of process control and IT in the HPI. After 50 years,
it is now time to standardize the method for determining the
financial performance of instrumentation, control systems, IT
and CIM, so we can justify them properly, set setpoints correctly,
critique weaknesses, offer ways to strengthen the HPI operations
and process control practices, and make more money.
No one should play a game until they understand how to keep
score. Proper performance measures are central to any renewal of
process control. If the purpose of HPI plants is to make products
that create profit, then the purpose of all plant components,
including process control and IT, is the same as all the other plant
equipment and components.
In April and August 2010 editorials, I blamed the flawed logic
used to quantify the financial value of all process control and IT
since 1970 as a basic cause of the crippling disconnects between
the layers, components and technologies. The flawed perfor-
mance logic of process control since its inception is this: reduced
CV variance is good but of no intrinsic value because the credits
and debits cancel each other. It is just a necessary prerequisite for
moving the CV mean in the profitable direction toward a limit,
specification (spec) or constraint by some arbitrary amount,
which produces steady-state benefits of one kind. This flaw was
identified and corrected in 1996.2, 4
Missing method. The discovery that every CV/KPI has a
risky expected-value profit tradeoff to be optimized provides the
process industries with the mathematically rigorous method for
setting operating conditions.2 They can be optimized indepen-
dently; benefits are additive and application is greatly simpli-
fied.2–6 These tent-shaped profit tradeoffs often have penalty
cliffs near specs and limits. Steep cliffs and chasms abound in
refinery operations. Their location and magnitude should be
identified and considered when setting setpoints. The clifftent
profit tradeoff function for each CV/KPI must be determined
before setpoints can be aligned properly with economics.5, 6
Once this knowhow is understood and adopted, the thinking
is clear; the needed information is tied to a standard decision
method and actions to make money, and, lo and behold, the
financial value of dynamic process control to reduce CV variance
is quantified.2–6 This is the knowhow needed for process control
and IT renewal.
The reason IT and instrumentation cannot prove their finan-
cial merits is because the value of information depends on what
is done with it; if nothing is done, it is just worthless data. Use-
ful information is a universal component of operating decisions
and actions to set CV/KPI setpoints and control tightly about
them. Which is why adopting a standard best practice for set-
ting setpoints provides the means for assuring that what is to be
done with instruments, computers and IT is worthwhile2, 4–6 It
also defines the way they manifest themselves in improved plant
performance. This is crucial for specifying and justifying instru-
mentation, control and IT requirements.
Benefit. The HPI can reap substantial, visible money by gather-
ing and using its appropriate business knowhow information to
adjust setpoints to operate at their best. Operating conditions are
aligned with economics.5, 6 The premium on modeling the conse-
quences for exceeding specs, breaking limits, noncompliance and
unsafe situations is clear and integrated with corresponding CVs.
The thinking process is strengthened; the culture is changed.
The premium on forecasting near-term variance for profit risk
management determines information and learning requirements,
which BP has apparently lacked at its Texas City, Texas Refinery
since 2005.
Watching Thad Allen’s C-SPAN reports on the Deepwater
Horizon spill clean-up with clifftent knowhow, you see Allen
describe risky value trade-offs with lots of cliffs everywhere, from
drill hole pressure tolerances to wave heights for the BOP lift.
Adopting the rigorous method that optimizes risky tradeoffs for
setpoints provides the way to evaluate the value of instruments,
components, layers, models, IT and solutions.2 This is the proper
path to renewal and success. In the end, Kern1 and Latour2–6
will unite to provide guidelines for renewing the process control
engineering practice during refinery golden ages and downturns.
Next time, I will cover the consequences of violating limits to
fulfill the purpose of process control. HP
LITERATURE CITED
1 Kern, A., “Continuous improvement or core-competency,” Hydrocarbon
Processing, July 2010.
2 Latour, P. R., “Process control: CLIFFTENT shows it’s more profitable than
expected,” Hydrocarbon Processing, December 1996, pp. 75–80. Republished
in Kane, L., Ed., “Advanced Process Control and Information Systems for the
Process Industries,” Gulf Publishing Co. 1999, pp. 31–37.
3 Latour, P. R., “Does the HPI do its CIM business right?” HPIn Control,
Guest Columnist, Hydrocarbon Processing, V76, n7, July 1997, pp. 15–16
and “Optimize the $19-billion CIMfuels profit split,” V77, No. 6, June 1998,
pp. 17–18.
4 Latour, P. R., “Demise and keys to the rise of process control,” Hydrocarbon
Processing, March 2006, pp. 71–80, and Letters to Editor, Process Control,
Hydrocarbon Processing, June 2006, p. 42.
5 Latour, P. R., “Set vapor velocity setpoints properly,” Hydrocarbon Processing,
Vol. 85, No. 10, October 2006, pp. 51–56.
6 Latour, P. R., “Align HPI operations to economics—CLIFFTENT optimizes
risky tradeoffs at limits,” Hydrocarbon Processing, Vol. 87, No. 12, December
2008, pp. 103–111.
Process control practice renewal 2010—performance
The author, president of CLIFFTENT Inc., is an independent consulting chemical
engineer specializing in identifying, 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.

4. HPIN CONTROL
clifftent@hotmail.com
PIERRE R. LATOUR, GUEST COLUMNIST
HPIN CONTROL
In my April 2010 editorial, I initiated and justified a call
for renewal of process control and IT practice in the hydrocar-
bon processing industry (HPI) with a review of the basics. In
August 2010, I reviewed the purpose of process control and IT.
In the October 2010 editorial on performance, I described a
standardized method for determining financial performance of
instrumentation, control systems, IT and CIM.1 Now, I turn to
the consequences for exceeding limits and violating specs of con-
trolled variables (CVs) and key performance indicators (KPIs).
Unforeseen occurrences. In 1995, a major refiner indicated
experiencing unforeseen occurrences in four refineries costing
$60 million annually. The problem is “unforeseen.”
Limits and specs. Engineers and operating managers specify
the location values for equipment limits, quality specifications,
alarms, tolerances and safety factors. Obviously, limit and spec
settings are critical to long-term HPI performance. One can
operate far from them, uneconomically, or too close to them,
uneconomically. These limit values are input to LP planning and
scheduling systems as problem-boundary limits. The optimum
solution invariably lies at an intersection of a combination of con-
straints, but the location of the intersection is set by these input
values. They are input to online process NLP optimizers (and
MVC as “equal concern errors”) as problem boundary limits, as
well. The optimizer determines the best constraint combination
(rarely an interior hilltop), but the location of the intersection is
set by these input values. (This is why process optimizers don’t
actually determine the best process control setpoint values; those
are inputs set by people.)
Inside limits. Engineers use process models to determine the
physical consequences of moving setpoints toward limits. They
associate an economic factor to determine the financial merit
from the move. This may be the slope of an LP profit function.
Beyond limits. Engineers often say exceeding limits is forbid-
den because disaster follows, and the consequences are too hard
to contemplate—let alone model. Never do it. That is too easy.
Sometimes, this is because standard tools like process models,
LP and online NLP optimizers do not handle these external mod-
els well. Yet, operators and control systems must do something
when an alarm sounds. Here is the opportunity.
Slopes, drop-offs and cliffs. Determine what happens as a
limit is violated or a spec is exceeded.1 Model it physically and
economically. Some quality violations have a smooth price pen-
alty depending on degree; some have a discrete penalty indepen-
dent of degree. Some increase equipment wear and tear gradually;
others destroy equipment suddenly. Some emissions are harmless;
others are deadly. Knowledge of these consequences is essential to
optimally avoiding and mitigating them. The penalty slope and
magnitude of any exterior cliff should be modeled with as much
fidelity as the interior process models.
Consequences of consequences. If the violation cliff
is severe, the HPI normally operates safely away. If the viola-
tion penalty is small, then the HPI normally operates closer to
it. I have seen a gasoil pour-point spec with a mild penalty for
exceeding and a strong credit for approaching; therefore, exceed-
ing it was common. I have also seen study of limit violations
leading to contingency plans that manage and mitigate them
when they inevitably occur—shrinking the cliff. The financial
consequences for inadequate attention to modeling limit viola-
tion consequences are consequential. The problem is that the
approach tolerances are vague, ad hoc and empirical.
Risky tradeoffs everywhere. Working on computer con-
trol of many refineries and petrochemical plants around the world
since 1966, I can confirm that every HPI operator thinks this way
but must rely on experience and judgment to set setpoints unless
they adopt a risky tradeoff optimizer using the best information
available to quantify the thinking process and stay on top of their
plant and business daily.
Process control renewal. The objective is not simply to
renew the practice of process control. The goal is to run better.
The heart of the matter is setting setpoints right by optimizing
risky tradeoffs rigorously. Historic practice relies too much on
fallible human experience, judgment and empiricism without a
standard procedure. The remedy is for the HPI to adopt rigorous
methods for setting setpoints and process control, and IT adopt-
ing this decision process as their guide for control-performance
determination and information requirements value.
Conclusions. Associating a profit tradeoff with each CV, one
can connect alarms and process control; HPI plant operation and
its surroundings; operations to customers; process control and
IT to profit centers. Formerly unforeseen occurrences are fore-
seen. The best way to improve HPI operation is to do it the way
everyone has been doing it all along, but with more rigor, science,
mathematics, economics and knowhow. This was offered to the
HPI in 1996, 1 worth > $1/bbl depending on how well the refiner
understood the process, business and CV clifftents. HP
LITERATURE CITED
1 Latour, P. R., “Process control: CLIFFTENT shows it’s more profitable than
expected,” Hydrocarbon Processing, December 1996, pp. 75–80.
Republished, “Advanced Process Control and Information Systems for
the Process Industries,” Gulf Publishing Co., 1999, pp. 31–37.
Process control practice renewal—consequences
The author, president of CLIFFTENT Inc., is an independent consulting chemical
engineer specializing in identifying, 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.
86
I DECEMBER 2010 HYDROCARBON PROCESSING

5. HPIN CONTROL
clifftent@hotmail.com
PIERRE R. LATOUR, GUEST COLUMNIST
HPIN CONTROL
I followed my April 2010 call for renewal of process control
and IT practice with editorials on purpose in August, performance
in October and consequences in December. Now I turn to selec-
tion of controlled variables (CVs).
How do we select CVs? Well, just how do we go about select-
ing operating conditions to be controlled about setpoints by manip-
ulating valves? Why we do select particular flows, levels, pressures,
temperatures, compositions, efficiencies, capacities and yields for
measurement and stabilization about desired setpoints near limits?
How do we determine the production, inventory, product quality,
utility consumption and equipment limit setpoints? From the host
of candidate refinery operating conditions, what is the underlying
basis for selecting a relatively few for control to operate the HPI?
We know how to measure CVs. We know how the manipulated
valves affect them. We know about instruments, control algo-
rithms, models, optimizers and displays. Somehow, we determine
what is important, what we care about and what counts. Since we
know how much equipment costs to procure, install and maintain,
we proceed to engineer and use instruments, control systems,
computers and IT to operate about predetermined CV setpoints.
But why? What is wrong with this picture? What is missing?
Why is it so hard to rigorously quantify the financial value of
improved HPI operation? Why do we have so much trouble jus-
tifying maintenance? How do we figure out the merit of control
and information system components and solutions?
Pause a moment to think about how we know and quantify
values. We determine what is important, what we care about,
what counts. What we mean is that the average of a candidate
CV affects profit rate. Profit is sensitive, and errors can be costly.
There is great potential from quantifying the steady-state profit
rate increase as the mean is moved to a limit, determining the pre-
cise location value for that limit, and the financial consequences
for exceeding that limit. Establishing this tent-shaped tradeoff is
critical for selecting CVs at the outset and controlling them. If
the sensitivity is low and the profit tent shallow, the CV is not
important. If sensitivity is high and the profit tent steep, the CV
is important. We select CVs/key performance indicators (KPIs)
because they have interesting and significant trade offs.
Significance. Often, there is a severe cliff at the limit, encoun-
tered when a machine, safety, government or customer penalty
is incurred. CVs that encounter high cliffs are critical. Knowing
the location, slope and height of those cliffs is central to the busi-
ness of HPI operation. So, one should determine and maintain
the steady-state profit rate cliff function of CVs as a first order of
business for operating properly and selecting the appropriate CVs
for doing so. If you do not know CV/KPI cliff limits, you do not
know your operating business.
This function can be combined with the CV distribution
to determine the actual expected value profit hill to find the
best setpoint to optimize each risky tradeoff. This is the profit
meter for every CV/KPI. If there ever was an activity that needs
to pay close attention to identifying, capturing and sustaining
financial values by locating and re-optimizing risky tradeoffs,
it is the HPI.
Once the best practice of process control is driven as the stan-
dard method for operating HPI plants, the determination of the
value of control components becomes routine and we know the
value of our work. We know the information that we need to
make best decisions, and we can make them faithfully on the best
information available because we know what we are doing with
that information. Now we have a complete method for selecting
CVs and KPIs. We know how to pick them and why.
Distillation dual temperature control? Kern,1 Fried-
man, Shinsky and I agree that tightly controlling top and bottom
temperatures or dual-ended compositions is not a good idea.
Beyond their technical arguments, my additional reasons stem
from HPI separation business economics.
For separations like distillation, absorption and extraction,
one key component is invariably more valuable than the other,
providing the incentive for their separation. There will be a maxi-
mum specification for the less valuable component in the valuable
stream and an optimum recovery vs. utilities for the valuable key
in the less valuable stream. One end invariably has an important
customer-quality specification, while the other has a smooth
recovery vs. utility financial tradeoff. The former hard target is
best controlled by material balance split; the latter soft target by
utility reoptimization when feed composition, feedrate, compo-
nent value or utility costs change. The economic incentive for
tight soft-target control for a nearly flat profit hilltop can be quite
small. This situation is true for olefin plant cold-side recovery,
aromatics recovery, refinery saturates and unsaturates gas plants,
crude-oil distillers and natural gas plants.
So, first, ask why control temperatures at both ends simulta-
neously? What is the financial merit? How should their setpoints
be determined to align the column operating conditions to its
economics? Avoid working on control systems that don’t make
much money. HP
LITERATURE CITED
1 Kern, A., “Weighing in on dual-temperature control,” Hydrocarbon Processing,
Vol. 89, No. 11, November 2010, p. 15.
Process control practice renewal—select CVs
The author, president of CLIFFTENT Inc., is an independent consulting chemical
engineer specializing in identifying, 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.
90
I JANUARY 2011 HYDROCARBON PROCESSING