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UNIT 4

Benefit Estimation

Before projects can be ranked and evaluated, benefits must be identifiedand quantified. This unit covers estimation of the control performanceimprovement that is likely to be achieved by a particular installation andtranslation of the change in control performance into changes in processand economic performance.

Learning Objectives When you have completed this unit you should:

A. Be able to estimate the control performance improvement from aspecific control improvement project.

B. Understand how control performance improvement can betranslated into process performance improvement.

C. Understand how process performance improvement can betranslated into economic performance improvement.

D. Be able to estimate economic performance improvement andpresent the benefits as a cash flow table.

4-1. How Much Money?

Unit 3 showed how to identify the most likely benefits from a controlimprovement project. This information usually is not sufficient for justifi-cation. Management also wants to know how much money the project islikely to cost and how much it is likely to make. This unit presents a step-

by-step procedure for benefit estimation. Benefits are estimated beforecosts in order to define the scope of a project. There is no point in specify-

ing and costing a DCS system to save $10,000 a year in operating costs.The project should be abandoned unless a simpler implementation can befound.

4-2. Information Requirements

To estimate the benefits from a control improvement, the following rele-vant information is usually needed:

Knowledge of present control performance An estimate of improved control performance

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34 UNIT 4: Benefit Estimation

Knowledge of applicable constraints

A process model that shows the interrelationship of variables

An economic model of the processThis list is not as formidable as it appears. Relevant information is usually asmall subset of what is known about the process. For instance, suppose thecontrol improvement being considered is better temperature control toimprove yield. Present control performance can be observed. Historicaldata are already available from log sheets, recorder traces, or computerfiles. Improved control performance can be estimated from similar loopsor from the guidelines in Section 4-3 of this Unit. The applicable con-straint, if any, is probably a temperature upper limit. The relevant process

model is simply a curve that shows the effect of temperature on yield. Thissteady-state information is part of process design data and may have beenupdated in plant tests. The only economic information needed, the worthof an incremental yield improvement, has already been calculated byplant accounting.

This information is used in the multistep procedure shown in Fig. 4-1.Present and improved control performance are compared to estimate con-trol improvement. If any constraints are applicable, control improvementis used to calculate the amount by which operating conditions can be

changed. This process change and the process model information are usedto calculate the process improvement. Finally, the process improvementand economic information are used to calculate benefits.

If the best practical economic performance of a unit can be estimated, thedifference between this value and the present economic performance canserve as an upper bound on the economic performance improvement thatcan be realized by control improvement. A range of 1-4% production

Fig. 4-1. Benefit Estimation Procedure

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UNIT 4: Benefit Estimation 35

increase (or cost decrease) has been quoted. Fig. 4-2 shows experience atone major chemical company.

4-3. Estimation of Control Performance Improvement

Control performance was defined in Unit 2 as a measure of how well thecontrol system achieves its objectives, expressed as a function of variabil-ity. Control performance improvement is measured by the reduction of

variability that is achieved, expressed as a percentage of original variabil-ity. For instance, suppose a temperature control feedback loop has beenoperating with a standard deviation of 10 degrees. The addition of feed-forward control reduces the standard deviation to 3 degrees. Control per-formance improvement is 100 x (10 3)/10 = 70%.

Control performance improvement from a proposed control scheme canbe estimated with some precision by building a dynamic simulation of theprocess and applying the proposed scheme to the simulation. The simula-tion should include measurement noise. This effort cannot be justified for

most projects, so control improvement must be estimated from the qualityof previous performance and some guidelines.

The loops that are susceptible to the greatest improvement are those withthe worst performance, usually because they are uncontrolled. Adjust-ment of the manipulated variable is manual and infrequent. Lack of con-trol is often due to absence of an on-line measurement. Fig. 4-3 showstypical effects of first adding on-line measurement, then control. Whenmeasurement has been added but control is still manual, error decreases

but still changes sign infrequently. Only after automatic control has been

added does the frequency of set point crossing increase significantly. Man-ual control is usually conservative. The operator seldom takes action untilhe or she is sure that an error is persisting.

Fig. 4-2. Realizable Performance Improvement by Better Control (Ref. 5)

OpportunityforPerformance

ImprovementM$

Cost of Goods Sold, M$

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If the only available information about a stream composition has comefrom laboratory analyses once a shift, on-line measurement and controlcan produce dramatic improvement. Reductions up to tenfold in 95% lim-its can be expected.

Some of the improvement is attributable to more frequent measurementand some to control.

Example 4-1:Superfractionators A and B were identical columns withsimilar control performance. The effects of adding on-line measurementand control to column A are shown in Fig. 4-4. First, an on-line gas chro-matograph to measure bottoms composition was added, which reducedroot mean square composition error to 0.32%. Composition error for col-umn B during the same period was 0.81%. The subsequent addition ofclosed-loop control further reduced composition error to 0.17%. Total con-trol improvement was 100 x (0.81 0.17)/0.81 = 79%.

Many loops have on-line measurement and manual control. Either noautomatic controller has been provided or the controller is left in manualmode. Processes with long time delays (e.g., cement kilns) are often left inmanual because controller tuning is difficult. Reliable closed-loop feed-

back control can reduce 95% limits by up to a factor of 4. Maximumimprovement can be expected if the loop is difficult to control manually.

If the loop is already under automatic control most of the time, only lim-ited improvement can be expected. Advanced control refinements (e.g.,linearization, feedforward, decoupling) seldom have as much impact as

the initial application of a controller. Halving of 95% limits is a reasonableupper bound on expected improvement.

Fig. 4-3. Composition Control: Effects of On-line Analysis and Automatic Control

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4-4. Translation to Process Improvement

If a process has an unconstrained optimum, improved control can pro-duce process improvements and benefits without any changes to setpoints. The upper part of Fig. 4-5 shows power consumption curves for acooling tower. Since both pump and fan curves are concave, total power

consumption goes through a minimum at an approach (differencebetween air and water exit temperatures) of M degrees. Improved temper-ature control can tighten temperature distribution as shown in the lower

Fig. 4-4. Effects of On-line Analysis and Control on Distillation Tower Performance (Ref. 1)

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part of Fig. 4-5. Temperature approach stays closer to the minimum valueM and reduces average power consumption. The reduction can be quanti-fied by integrating the approach distribution over the total power con-sumption curve.

Most process operation is limited by constraints. These constraints may bephysical, regulatory, or economic. Pump capacity is a physical constraint.A limit on sulfur dioxide emission is a regulatory constraint. Price discon-tinuities are economic constraints. Constraints can be hard or soft. A hardconstraint cannot be violated; violation of a soft constraint is possible butincurs a penalty. The steady-state optimum of a constrained process

almost always lies on one or more constraints. Improved control allowsoperations closer to a limiting constraint and, therefore, closer to the opti-mum. The process set point must be moved closer to the constraint to real-ize benefits.

Fig. 4-5. Effect of Temperature Control on Cooling Tower Power Consumption

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Example 4-2:A reactor must be operated below a specified maximumtemperature. Yield from the reactor is a damped function of reactor tem-perature. Fig. 4-6 shows the effects of improved temperature control andset point change. Temperature variability decreases when control is

improved, but average yield increases only after control has beenimproved and the set point has been changed. Temperature exceeds theupper limit about as frequently as it did under original conditions. The setpoint cannot be raised before control is improved, or the constraint will beviolated too often.

The same situation is shown from a different viewpoint in Fig. 4-7. Tight-ening the quality distribution allows the target average to be shifted closerto the specification. The specification is a soft constraint, and some pointsin each distribution exceed it. The relationship between the controlledvariable and the constraint is preserved, as shown in Eq. (4-1), by main-taining the ratio of the standard deviation and the distance from theset point to the constraint.

(4-1)

Fig. 4-6. Operating Closer To a Constraint

1

1

------2

2

------=

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The percentage P of the distribution that violates the constraint is heldconstant (i.e., P1= P2). For this reason, this relationship appears in the lit-erature under the name of same percentage rule (Ref. 3). Another func-tion of variability (e.g., range, % within limits) can be substituted for inEq. (4-1). If the consequences of constraint violation are known and quan-tified and the distribution is known, it may be more profitable to vary therelationship and move the target average closer or farther away from theconstraint than is called for in Eq. (4-1). This point is illustrated in Exercise4-6.

Even when operation is not constrained, upstream or downstream processchanges may be required to realize process benefits. For instance, betterreactor control, producing an effluent with a higher concentration of adesirable product, may not result in a purer product until downstream

separation process conditions are changed.

Fig. 4-7. Quality Distribution Curves (adapted from Ref. 2)

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4-5. Translation into Dollars

Conversion of a process improvement into an economic improvement canbe easy or difficult. The degree of difficulty depends on the type of

improvement and the economic climate. Labor and utility savings are usu-ally easy to convert. Estimated savings per unit time, expressed in theappropriate units (kWh, man-hours, etc.) are multiplied by the incremen-tal unit cost. Utility incremental costs usually are readily available. Incre-mental costs of labor may depend on contractual agreements. Rawmaterial savings from yield improvement are calculated by the samemethod, multiplying unit improvement by unit cost.

The benefit from yield improvement of multiproduct processes (e.g., cata-

lytic cracking or ethylene production) may be hard to calculate. Increasedyield of one product is balanced by decreased yield of another, and theresulting benefit is dependent upon relative product values.

Estimation of the profit from an increase in productive capacity is easy ifall the added production can be sold at a fixed price. The additional unitsof production are simply multiplied by the incremental profit per unit.Help should be requested from marketing if it is likely that added produc-tion will affect the price structure. If the added production cannot be soldat a profit, the capacity increase must somehow be used to reduce costs,

perhaps by displacing higher-cost production at another site.

An improvement that results in emission reduction may require anotherstep before a direct conversion into economic terms is possible. If the pre-vious emission level has been acting as a plant constraint, emission reduc-tion shifts the operating level at which the constraint applies and allowshigher production or more efficient operation. These benefits can then bequantified.

Example 4-3:A power plant rated at 1000 megawatts can produce only

850 MW before overloading its scrubber, as shown by the solid line in Fig.4-8. Control improvements to the scrubber will shift emissions to thedashed line, allowing generation of 950 MW before reaching the emissionlimit. If the incremental cost of energy at the plant is $0.10/kWh and theadded generation will replace a gas turbine costing $0.11/kWh to operate,savings attributable to better scrubber control will be ($0.11 $0.10)/kWhx 1000 kW/MW x (950 850)MW = $1000/hr.

Estimation of the dollar return from a product quality improvement oftenrequires assistance from marketing, and even then it may be hard to

define. Product improvement in a competitive market is a leapfrog pro-cess. An improved product may be the best on the market this year andcommand a premium price, but a competitor's upgrade can reverse the sit-

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uation and erase the premium. Assuming that future pricing over time canbe estimated, added revenue benefits are calculated for each year by mul-tiplying product volume by the difference in price attributable to the qual-ity improvement.

4-6. Representation as Cash Flow Table

Management wants to know not only how much money the project islikely to make but also when. A project that returns 10 times its invest-ment is not necessarily attractive if the return is 30 years hence. The usualway to describe benefits as a function of time is with a cash flow table.Conventional representation starts at time zero and presents cash flow inone year increments. The table extends over the life of the project.

References

1. Friedmann, P. G., and Fletcher, R. J., 1967. Control of aSuperfractionator. Instrumentation in the Chemical and PetroleumIndustries, 4, p. 49. Plenum Press.

2. Latour, P. W., 1976. The Hidden Benefits from Better ProcessControl. ISA paper 76-528, presented at ISA/76.

3. Matrin, G. D.; Turpin, L. E.; and Cline, R. P., 1991. Estimatingcontrol function benefits,Hydrocarbon Processing, 70, 6, pp. 68-73.

4. Murrill, P. W., 1999. Fundamentals of Process Control Theory, 3rded. Research Triangle Park, NC: ISA.

Fig. 4-8. Power Plant Emissions

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5. Trevathan, V., 2004. Notes for ISA Course MT10, Planning,Justifying and Executing Automation + Control Projects.

Exercises

4-1. In Example 4-2, is the maximum temperature limit hard or soft?

4-2. A shell-and-tube heat exchanger is used to heat a liquid. It is desirable toheat the liquid as much as possible, but liquid temperature must be keptunder 120C. The temperature is now controlled by a closed-loop feedbacktemperature controller that manipulates a steam valve. Temperature setpoint is 115C. A change to cascade control is proposed, adding a pressurecontrol inner loop as shown in Fig. 4-9. It is expected that this controlimprovement will allow the temperature set point to be raised to 119C. Isthis expectation reasonable?

4-3. A control room consolidation eliminates four jobs. The labor contract statesthat workers displaced by automation will remain on the payroll for threeyears or until they reach retirement age, whichever comes first.

One of the displaced operators retires in 1.5 years. Two operators of asimilar unit retire in two years and must be replaced. Each operating jobcosts the firm $35,000/year in wages and benefits. The process has a useful

Fig. 4-9. Shell-and-Tube Heat Exchanger Cascade Control (Adapted from Ref. 4)

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life of ten years. What are the benefits from the consolidation project?Present the results as a cash flow table. Assume that the consolidation iscompleted in one year.

4-4. A product has an upper limit on component X of 500 ppm. The process isoperated to turn out product with an average of 400 ppm of X. More preciseflow metering will tighten the composition distribution, reducing 90%limits (the endpoints of a range that includes 90% of the distribution) from100 ppm to 40 ppm. In the absence of other information, where shouldthe new set point be located?

4-5. If each ppm increase in average X content decreases operating costs of theproduct in Exercise 4-4 by $0.01/kg, and recycling product with X> 500ppm costs $1.00/kg, how much will the flow metering project save per

kilogram?

4-6. More information is available about the process described in Exercises 4-4and 4-5. It is estimated that after more precise flow metering is installed,composition set point will be related to percent, exceeding the 500 ppmupper limit according to Table 4-1. Given this added information, can amore profitable set point than 460 ppm be specified?

Composition Set

Point, ppm% Recycled

450 2

460 5

470 10

480 20

490 33

500 50

Table 4-1. Effect of Composition Set Point on Recycling

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