ditorials, features, and letters to the editors of CONTROL magazine during the past several yearshave described and decried the state of commercialpractice of process control engineering and automa-

tion. Application and justification of analyzers, actuators,instrumentation, control systems, plant modeling, algo-rithms, control computers, information systems, projectservices, software, systems integration, and maintenance arefragmented, risky, and not sufficiently profitable.

Many large technology suppliers are troubled by lack ofprofitable business growth, recognition, and appreciation oftheir value-added offerings. Academia has lost interest inadding to the mountains of mathematical publications gen-erated since 1960.

Free literature and technical conferences have beenavailable around the world for decades, yet, in plant man-agement circles, turmoil and confusion reign about thevalue (potential and realized) of deploying the enormoussuite of technology available for control, optimization,scheduling, and IT. Management continues to say, “Showme where the money comes from, how much I get, withwhat risk.” Reports of potential benefits (1,3) have not led torealization. Investment in the field is shrinking; the fieldhas become a business priority backwater. Many good peo-ple and companies have left. Something is amiss. Why?What’s wrong?

Myths and MeasuresWhy should we do control? What is the purpose of control-ling things? What is the unifying universal objective? Howdo we decide what things to control? How do we measurethe performance of control; the value of controlling better?How do we know how successful we are at improved controland how delighted we are with our achievements?

Too many say they do control because it’s good, necessary,cool, modern, everyone is doing it, fun, neat, important.These are simple myths.

Too many say the purpose of control is to reduce fluctua-tions, minimize variations, minimize the integral of errorsquared, smooth things out, stabilize operations, reduceupsets, increase speed of response, move closer to limits,improve operations, improve quality, increase yield/capacity,save energy/utilities, help operators, increase reliability,improve safety, reduce emissions, cut maintenance costs,reduce manpower, etc., ad nauseam. More myths.

These justifications are often repeated by people who

believe the purpose of a process plant is to make product.But the purpose of all process plants, in all industries world-wide, is to add value to repay investors and governments(some do this by shutting down to stop hemorrhaging losses).The purpose of process control is to add value, create wealth,increase profits, and make more money. Always. Ethicallyand legally, of course. Humanity decided long ago to meas-ure its commercial values with money.

Process control suffers from lack of an agreed-upon,meaningful measure of performance. An essential ingredientfor baseball is universal agreement: if the ball goes left of the

left field pole it’s a foul; to the right it’s a home run. We can-not start a Super Bowl in front of 100 million viewers with adebate on whether a touchdown is worth five or six points.The Olympic Games are truly great and interesting whenthe performance measures for broad jump, marathon,slalom, and high hurdles are thoroughly understood andagreed upon. Great sports are built on consensus on the per-formance measures for success.

In the process industries, such a measure is expected netpresent value profit (ENPVP). Control engineers and busi-ness people should adopt as a primary purpose to identify, cap-ture, and sustain significant economic benefits from theprocess or system for their customers, investors, and them-selves by using the appropriate products, tools, techniques,

R E P R I N T E D F R O M C O N T R O L , M AY / 2 0 0 2

TURBINE FLOWMETERS VS. PROCESS CONTROL?

WHY INVEST IN PROCESS CONTROL?

Understanding the Real Benefits in Quantitative Terms Is the First Step to Proving Payback. By Pierre R. Latour

FIGURE 1.

DETERMINE THE OPTIMUM SETPOINT

QUANTIFYING THE RISK AND COSTS ASSOCIATED WITH OFF-SPEC FUEL OIL BY

CALCULATING THE PROFIT PER BARREL (RED) LETS US SEE THAT, IN THIS EXAMPLE,

SIMPLY MOVING THE MEAN OF THE PERCENT SULFUR DISTRIBUTION (BLUE) FROM

0.6% TO 0.758% CAN RAISE THE PROFIT RATE FOR THAT DISTRIBUTION (PINK) TO AN

AVERAGE DAILY PROFIT OF $7,433 FROM $6,806.

EE

-0.20-0.20 0.200.20 0.600.60 1.001.00 1.401.40 1.801.80% S

0205 F- Justify 6/10/02 9:32 AM Page 41

and services to maximize the ENPVP. The word “expected”has an important statistical definition; the words “net presentvalue” have an important financial definition. The word“profit” has a significant modeling meaning.

The Clifftent function (2) provides the rigorous means tomeasure financial value of dynamic performance. Process con-trol, maintenance, and IT now have their measure for winning.

Control Risk to Make MoneyControl engineering and technology is a basic method fordeploying knowledge to mitigate risk or uncertainty. It inte-grates knowledge of the process physical behavior, econom-ic impacts, disturbances, key measurable responses, inde-pendent adjustments, and optimal control theory for non-linear/multivariable dynamic systems to build computer-integrated systems of models, measurements, actuators, andcontrol algorithms.

Feedback control basically swaps variations in independ-ent variables we care about for variations in independentvariables we care less about. That is how control mitigatesand manages risk. That is the source of profit. The key phys-ical performance claim is the reduction in a properly speci-fied variance. All control system components should play arole in this reduction.

How does this make money? The traditional answer fordecades has been: Reduced variations in a key measureddependent response variable about its base case mean doesnot make money per se because the variations average out,but it is a necessary prerequisite to allowing us to move themean somewhat in the profitable direction toward a limitor specification. This provides a steady-state averageimprovement like yield, capacity, or utilities. Multiply thephysical gain by the right economic factor and benefit,$/day, is achieved.

This classic universal approach is wrong. It is incomplete.It relies on at least four of the myths described above. A better calculation can be done using Clifftent.

First, determine if the base case mean is near its desiredtarget (setpoint) and, more importantly, whether the setpointis optimally set to maximize ENPVP. Calculate what theoptimum setpoint is, and the profit gain for moving themean from its base case value to its base case optimumvalue. The low-sulfur fuel oil example below illustrates howthis is done with Clifftent (2,4).

People unfamiliar with Clifftent naturally skip this vitalfirst step, and there may be as much easy profit in this step asin all subsequent control endeavors. Further, if one cannotdetermine the optimum setpoint of a proposed controlledvariable, there is no (not little, but no) basis for controlling it.Clifftent proves this mathematically.

Second, determine the ENPVP for reducing dynamic vari-ance by the proposed amount at the same setpoint. Thisalways makes money, but people unfamiliar with Clifftentnaturally skip this step and assume it has zero intrinsic value.Unfortunately, this myth misses typically half the provabletangible benefit.

The literature must describe this as intangible—in otherwords, the benefit from good control is typically twice thatclaimed by the classical incomplete method. This is one ofthe great tragedies of process control. Clifftent is the rigorousmethod for quantifying the financial value of improveddynamic performance of any system. It completes the quali-

ty work of Deming, Juran (5), and Crosby in the 1980s byquantifying the profit from quality control.

Third, determine the maximum ENPVP and correspon-ding new optimum setpoint for the reduced-variance situa-tion. This gives the third profit gain component, which isclose to that determined by the classical approach, with onemajor difference: It is now optimal.

Fourth, the traditional method moves the mean an arbi-trary distance, ad hoc, non-rigorous, because no one bothersto model the financial consequences of violating the limit orspec. Most assume it’s forbidden, unknowable, infinite.Clifftent shows that this missing ingredient, the penalty ofviolating the limit or spec, is just as important as the eco-nomic credit factor for approaching the limit. That is thekey. The example below illustrates how one can never opti-mize setpoints in the neighborhood of limits without knowl-edge of the penalty for exceeding the limit.

WHY INVEST IN PROCESS CONTROL?

FIGURE 2.

EVALUATE PROCESS IMPROVEMENTS

-0.20-0.20 0.200.20 0.600.60 1.001.00 1.401.40 1.801.80% S

REDUCING THE PERCENT SULFUR STANDARD DEVIATION FROM 0.23 TO 0.07 AND

ADJUSTING THE SETPOINT TO 0.860% BRINGS THE PROFIT RATE FOR THE IMPROVED

DISTRIBUTION (BROWN) TO AN AVERAGE DAILY PROFIT OF $9,333, CLOSE TO THE THE-

ORETICAL MAXIMUM OF $10,000.

0205 F- Justify 6/10/02 9:32 AM Page 42

WHY INVEST IN PROCESS CONTROL?

Clifftent CapabilitiesClifftent provides a rational, rigorous method for setting lim-its, targets, and tolerances with uncertainty (2,4). It unifiesstatistics and process control. It does quantifiable risk man-agement. It properly sets dependent variable constraint val-ues for linear and quadratic programming (which often addsmore value than the programming solution itself). It sets theoperating limits for online process optimizers (which oftenadds more value than the optimizers alone). It sets limits forproduction and inventory schedulers (which often adds asmuch value as the scheduler itself). It incorporates the valueproposition to key performance indicators used to justifylarge IT, MES, and value-chain management projects. (IThas suffered notoriously for decades from lack of a rigorousfinancial performance method.)

Clifftent needs two input functions for each dependentcontrolled variable (CV): the statistical distribution and thesteady-state profit rate as a function of the CV mean. Thedistribution forecast may be Normal Gaussian, Gamma, orarbitrary. The steady-state profit rate always increases (lin-early or not) from the left toward the limit or spec anddecreases (linearly or not) toward the right away from thelimit. It is shaped like a tent.

Steady-state profit defines a tradeoff. It may have a dis-continuity at the limit, like a cliff. All value functions drop tonegative values at left and right extremes. The method com-bines these two functions in a proprietary way to provide theactual profit as it varies with the mean of the distribution,ENPVP (2,4).

The uncertainty distribution provides roundness to thereal profit function; it is always a smooth hill. The hilltoplocates the maximum ENPVP: that’s all there is. Slopes arecritical. Cliffs are critical. Curvature is important.Distribution changes are significant.

One side of the input profit function combines the physi-cal process model with its economic sensitivities. The otherside connects the process to its surroundings: customers,suppliers, environment, and maintenance.

So how do we select candidates to be control variables?They have a Clifftent function. Their value affects profits. Thesteeper the tent slopes, the more sensitive, critical, and inter-esting the variables are. The higher the cliffs, the more criti-cal and interesting they are. Forget about controlling variableswith shallow tents and tiny cliffs. They are of no consequence.

Low-Sulfur Fuel Oil ExampleSuppose we are in charge of making 10,000 bpd of low-sul-fur fuel oil (LSFO). Our product should contain no morethan 1 wt% sulfur. Last year the average of our cargoes was0.6% with standard deviation of 0.23%. A sulfur analyzersalesman claims we can reduce the deviation at least 30%

with his $50,000 instrument. An advanced control solutionprovider claims he can reduce it 70% for $1 million.

As we over-desulfurize below 1%, refinery models quanti-fy the increased costs (from lost yield, higher catalyst andhydrogen consumption, and sulfur plant H2S load) to be$5/bbl/%S plus some quadratic curvature. Our power cus-tomer contract for LSFO specifies it will accept off-spec car-goes with a penalty of $0.6/bbl plus $3/bbl/%S plus somequadratic curvature for spec exceedance. He must injectexpensive 0.2% S diluent to his boilers to maintain SO2emissions. If our LSFO were always at 1.0% sulfur our prof-it would be $1.0/bbl x 10kbpd = $10k/d. What to do?

Figure 1 shows five curves. The horizontal axis is percentsulfur from 0-1.8%. The blue curve is the quality distribution,normal with mean = 0.6, standard deviation = 0.23. Red is thesteady-state profit function: $1.0/bbl at 1.0%, a $0.6/bbl cliff tothe right, further decline in product value with higher sulfurcontent, and profit decline to the left from higher desulfur-ization costs.

The pink curve is ENPVP rate depending on quality mean,as the blue curve slides from left to right, incorporating the dis-tribution. We find our average profit is only $6,806 per day, farfrom the perfect $10,000. But if the mean were moved to 0.758we would gain $627 per day to $7,433. While we incur moreoff-spec product, we gain more from operating costs. At thisbase optimum, 15.0% of product is off-spec and 0.43% isunprofitable.

Next assume the advanced control supplier reduces thestandard deviation to 0.07. The green curve is this narrowerdistribution forecast. The brown curve is the ENPVP ratedepending on quality mean as green curve slides from left toright, incorporating the narrower distribution. Now profitjumps $1,446 to $8,880 per day at the new optimum 0.758mean. This is the value from reduced variance only,improved smoothness, tighter control, better dynamic per-formance. The money comes from reduced operating costson the left tail plus reduced spec violations on the right tail.This is real tangible money. It is always a combination of twoor more tradeoff phenomena, never just one. If the cliffpenalty were omitted, this benefit can never be quantified; itwould remain intangible and hidden.

Finally, we see the opportunity for more profit from ourimproved risk management capability. Move the mean far-ther right to the new hilltop at 0.860% sulfur to gain another$454 to $9,333 per day. This is 93.3% of $10,000 with perfectcontrol. This money comes from reduction in operating costslarger than the additional penalty from more off-spec prod-uct. This tradeoff is optimized.

Figure 2 shows the same five curves with the green distri-bution at its new optimum position, a mean of 0.860. Only2.56% of product is off-spec and virtually none of it is

0205 F- Justify 6/10/02 9:32 AM Page 44

unprofitable at the new optimum. Note also the traditional steady-state benefit of $454 is only

454/(454+1446) = 24% of the total process control credit.Seems worthwhile to quantify the 76% pure dynamic controlportion rather than leave it intangible.

Clifftent proves it is usually better to play on the safe side(unless the cliff is small and the safe-side slope steeper thanthe unsafe-side slope, when it may be more profitable to playon the unsafe side).

In 1980 the potential profit (benefit minus cost) for com-puter-integrated manufacturing for petroleum refining was$1/bbl crude x 65 million bbls/day, worldwide. In 2000 withcleaner gasoline and diesel it increased to $1.2/bbl x 75 mil-lion bbl/day (1).

Adding petrochemicals, polymers, and natural gas doublesthis potential for the hydrocarbon processing industry (HPI).That’s $65 billion per year profit from the HPI for somebody.The cost to capture this profit is less than half that (gross ben-efit = $1.5/bbl). Maybe some progress has been made, butsurely we can do better.

Pierre R. Latour, consulting chemical engineer, Clifftent Inc.,Houston, may be reached at clifftent@hotmail.com.

References1. Latour, P.R., “Benefits of Modern Refinery Information

Systems for Manufacturing Cleaner Fuels,” API FuelsConference, 1995.

2. Latour, P.R., “Process Control: Clifftent Shows It’s MoreProfitable Than Expected,” Hydrocarbon Processing, Vol. 75,No. 12, pp 75-80.

3. Latour, P.R., “Does the HPI Do Its CIM Business Right?”Hydrocarbon Processing, Vol. 76, No. 7, July 1997, pp 15-16and “Optimize the $19 Billion CIMFuels Profit Split,” Vol. 77,No. 6, June 1998, pp 17-18.

4. Latour, P.R., “Clifftent: Determining Full Financial BenefitFrom Improved Dynamic Performance,” Proceedings of theThird International Conference on Foundations of Computer-Aided Process Operations, AIChE Symposium Series No.320, Vol. 94, 1998, pp 297-302.

6. Juran, J.M., “Juran on Leadership for Quality—An ExecutiveHandbook,” Macmillan Free Press, New York, 1989.

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