PROCESS AND PLANT OPTIMIZATION

Pattern recognition displays captureadvanced process control benefitsOperational score graphs provide a real-time indicationof a controller's performance

E. CHANG, Equistar Chemicals, LP, Corpus Christi, Texas

M onitoring a large number of multivariable controllers(MVGs) to ensure that they are being properly operated tomaximize benefits has been made easier by de\'eloping pat-

tern recognition displays. These displays aid operators and engineersin reducing the time required to recognize improperly set controllerlimits that might negatively impact plant profitability.

The hydrocarbon and chemical processing industry has expendedsignificant resources o\'er the past several decades on advanced processcontrol (APG) to improve plant profitability. Once APG is commis-sioned, a sustained performance program must be in place to continueto achieve benefits. One major aspect of sustained performance is theeffectiveness in which operators use the Al'C on a daily basis.

Like most other sites, the Equistar Chemicals plant in GorpusChristi, Texas, used the APG controller's on/off status as the primarymeasure of controller performance. But a more meaningRil measurevi as sought to ensure that the controllers were being used effectivelyand to their fUll extent. Such a measure was developed in the formof a controller's "operational score." Graphs that plot the results oithis online calculation provide the operators and support staff a real-time indication of how a controller is being operated. Key to thesegraphs is the use oi pattern recognition, whidi significantly reduces tlietime required to evaluate a controllers operational score. This articledescribes how this operational score calculation was developed andapplied to a deethanizer coltimn MVG.

Need for more meaningful indication. Once APG is com-missioned, how does the plant track the performance of APG control-lers and determine that they are being used properly to maximizebenefits? Simply tracking the on/off status tells very little about howeffectively an MVC is being used and whether it is generating the ben-efits it was designed to generate. An MVG could ha\'e a high service fac-tor, but potentially the limits could be set so it actually generates mini-mal to zero benefits. If an MVC variable is a limit to plant throughputor is a major contributor to plant profitability, it is especially importantto operate this particular controller properly and as designed.

One common problem that reduces an MVC's effectiveness ispinching variable limits. If not corrected, the problem can becomesignificant. An operator might tighten up on a particular litnit for ahost of reasons. Often it is to compensate for a temporary upset orprocess change. The limits may or may not get reset once the tempo-rary situation passes.

Perhaps there is a problem with the controller itself A problemwith model mismarch or controller tuning can cause the controller tobe less stable when operated closer to true plant constraints or v^ thin

Rutining this column's MVC effectively provides a largebenefit.

certain operating regions. Anotlier reason might be insufficient opera-tor training and experience on the MVCs. Because of this, operatorsmay not know where to best set the limits or they may be uncomforr-able resetting limits. Finally, some operators may naturally be moreconservative than others and adjust the limits to keep the processwithin their comfort zxjne. Regardless of the reason, limit ranges onsome controllers tend to narrow over time, possibly reducing control-ler effectiveness.

Cioing through all the limits frequently ensures that they are set atthe proper values. Ideally, the board operator would examine the limitsat the start of his or her shift. But operators are normally \'ery busy atthe start of their shift; examining a large number of manipitlated andcontrolled variable limits is typically not their top priority. Engineersand specialists responsible for maintaining the MVCs can help. Butagain, they can only devote a limited amount of time to this aaivityand are not always available.

The Equistar Gorpus Ghristi plant includes an olefins unit thatproduces ethylene as the primary product. Advanced controls andreal-time optimization is foirly new to this planr, with an AI'G/real-time optimization project having been completed in 2003. Advancedcontrols in the form of MVC's were commissioned on the crackingfiimaces, fiacdonation and refrigeration units. A composite linear pro-

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SPECIALREPORT PROCESS AND PLANT OPTIMIZATION

T A B L E 1 Deethanizer column MVC variables

Deethanizer MVC controlledvariables

Deethanizer MVC manipulatedvariables

Overhead propylene composition

BotToms ethane+ethylene composition

Reflux drum level

Column bottoms level

Column differential pressure

Overhead product flow controller output

Reflux flowrate

Overhead product fiowrate

Bottoms flowrate

Column pressure

Tray 47 temperature

gram (CLP) is used to maximi7.e fiirnace feed rare subject to furnace,tractionation and refrigeration constraints. !n total, the olefins utiitincludes 177 manipulated variables and 569 controlled variables. Thisis a large number of operating limits to keep track of.

Deethanizer co lumn example . The deethanizer feed (Fig. 1)is primarily a mixture of C2S and C3S liquids from upsrream towers.The overhead C^s product goes to the acetylene reactors and then totlieCj splitter column. The bottoms C s and heavier product goes to adepropanizer column. Key component analyzers are available for boththe overhead and bottom streams.

The deethanizer normally operates in a stable manner. At highcolumn feed rates, top section Hooding can be a plant throughput con-straint. The deethanizer MVC controlled and niLinipulated •ariables arelisted in Table 1. This controller recei\'es three targets from the real-timeoptimizer: overhead composition, bottoms composition and columnpressure. Because this column can be a plant throughput constraintand the optimization targets vary with market and plant conditions,running this columns MVC effectively provides a large benefit.

'Operational score' concept. Each MVC controlled andmanipulated variable has an upper and lower limit. First, an evalua-tion is made to determine if the variable limits that have a significantnegative impact on plant profitability are set too conser\-atively. Eachof these limits will recei 'e a calculated operational score. If a limit is setwide enougli, it will achieve a high score. If a limit is set very narrow,it will achieve a low score.

For example, on the deethanizer column, column differentialpressure is a controlled variable that has a high impact on plant profit-ability. In this case, the only limit of interest is the differential pressure

upper limit since the low limit is irrelevant. So, If the operator sets thedifilerenrial ptessure upper limit dose to the tower fltwd limit, a highscore is achieved since it allows the plant to maximize rates. On theother hand, if the operator is conser\-ative and sets the upper limit atsome lower value, a lower score is achieved since this constraint, if hit,will prevent the plant from processing additional feed.

Each MVC variable limit receives a score between 0% (worst)to 100% (best), based on a comparison between the operator setlimit and guideline limits. Operations and process control personnelnormally determine these guideline limits jointly, and they normallydefine a wide range of operaring room for the MVC. They must bewithin the controllers engineering limits for consistenc)'.

The two guideline limits assigned to each MVC variable limit aredefined as the 100% score limit and the 0% score limits. For a variablehigh limit, the calculation is:

Score =\ 1 -UM \00-UUMIT

= 100%

For a variable low limit, the calculation is:

Score = 1 -LLIMIT-UM_\QQ

LIM_Q-UM_\OO)100%

where: ULIMITis the MVC operator upper limitLIJMlTis the MVC operator lower limitLIM_\O0 is die limit at which a 100% score is achievedLIM_O is the limit at which a 0% score is achieved

To avoid negative scores and scores above 100%, the scores areclamped at 0% and 100%. If a variable is out of service, it is assigneda score of 0%. Also, if a sub-controller or main controller is out ofser\-ice, all associated variables are assigned a score of 0%.

Using the deethanizer column differential pressure high-limitexample, flooding starts to become significant at a differential pres-sure of 6.0 psi. So the 100% score limit is set at 5-5 psi. This valuerepresents the ideal high limit for differential ptessure, since it avoidsHooding but still allows a high amount of column feed. The 0% scorelimit is set at 5.0 psi. Tlius, if tlie operator sets the high limit at or above5.5 psi, the score for this limit is 100%. If the operator sets the highlimit at or below 5.0 psi, the score for this limit is 0%.

The calculation is performed within the distributed control system

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SPECIALREPORT PROCESS AND PLANT OPTIMIZATION

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(DCS). The graph is built within the DCS's native window displaysystem and each axis represents one MVC \'ariable limit. Its score isplotted on a 0-100% scale. The control langiuige program updatesthe operational score and the graph is updated in re.il time.

A DCS screen capture of the deethanizer graphs is shown in Fig.2. Tbe top two graphs display current scores for the controlled andmanipulated variables, respectively. Note that not all the limits weredeemed significant or problematic enough to warrant scoring. Thebottom two graphs display the patterns as they typically appear undernormal operation and serve as a basis for comparison. Also includedon the display is an a\-enige score fbr each graph, "lliis average providesa numerical representation of the difference between current and nor-mal operating scores. The capability to assign weight feaors to eachindividual score to reflect the relative importance of the individuallimits W;LS included in the control language program, but not tised inthis particular example.

I here is a degree of flexibility in the setup of these graphs to deter-mine what the normal operation pattern will look like. This is a ilinc-tion of the 0% and 100% score \-alues chosen and the typical seningsof the controller variable limits. These parameters define the lookor pattern of the graphs. The graphs can be "full" and symmetricalor can be partially full and skewed. If the operators normally set thecontroller limits close to or at their 100% score \'alues, the graphs willconsistently appear fiiU. These graphs are the easiest to recognize andremember.

A qttick glance at these graphs and one can determine if a controlleris being operated properly and effectively (a full graph), or if there isan abnormal situation that warnuits flirther investigation (a skewedgraph). On the other hand, if some of the limits of a controller are typi-cally set some distance away fi'om their 100% score values, the graphpattern will be normally skewed. Obviously, a pattern of this form ismore difficult to recognize and remember. This is where a comparisonchart is helpRil.

A study of the patterns in Fig. 2 shows that a ntimber of controlledvariables such as the reflux drum level high limit, bottoms C^s lowlimit and column differential pressure high limit need to be adjusted.A few manipulated variables need to be adjusted as well, particuliirlyboth refltix limits and the pressure high limit. Fig. 3 shows the samegraphs as Fig. 2, but the pressure manipulated \'ariable is now off! Notethat the scores for the pressure limits are 0%.

The amount of information that can be itiduded on a graph dis-

play is a matter of preference. Fig. 4 includes the current limit valueson the top graphs, and the normal and 100% score limit values on thebottom graphs (actual values are not shown due to confidentiiility).Having this infortnarion saves the opetator from having to rely anhis or her memory or notes to know where to set the limits. Whilethis is a useflil feature, it distraas somewhat from the pure patternrecognition concept. The numbers clutter the display; one tends toconcentrate on numbers rather than patterns. A good compromiseis to inirially bring up the display with no values and include a targeton tlie display to toggle the \-alues on and off Fig. 4 also includes twotargets that, when selected, take the operator directly to the MVCoperating displays.

Reganiiess of the choice of p;irameters that txltimately define what agraph pattern will look like under normal operation, each MVC^ gniphwill have a certain and fairiy consistent pattern. Over rime, operatorsand those tliat support the controllers will recognize these patterns andbe able to quickly tell when they don't appear normal.

Benefits. Operating an MVC with wider limits can provide a signif-icant payoff Again, using the deethanizer column differential pressureconstraint as iin example, if the plant is running at maximum rates andcolumn DP is the throtighput limit, the benefit from running at anupper limit of 5.5 psi vs. 5.0 psi is approximately $400,000 annually

Operational score monitoring may not be needed for all control-lers and variables. In Equistars case, the unit throughput constraintchanges regulariy, and depends on feed type, ambient conditions,furnace operation and other factors. It is not tinusual fbr the plant to beup against three constraints simulmneotisly. These potential through-ptit constraints are spread out over various controllers. So certainly,the.se Qjntrollers would benefit most from score monitoring.

Another area in wliich operational scoring provides benefits is inoperator training—particularly with operators learning to operatethe DCS. Although new board operators will be less familiar withthe MVCs, the ability to view and compare patterns should helpprovide guidance on how to operate the controllers effectively andconsistently.

Final considerat ions . Although there is no limit to the numberof controller variables one can track or the number of graphs one canbuild, the general concept is to focus on critical variables only. Thisminimizes pattern clutter and enables quicker, easier and more fre-quent inspections of the patterns. The graph displays are not intendedas replacements for the standard MVC operating graphics.

The a v e r ^ or individtial operadonal scores can be included in theplant's process data historian to allow tracking over rime and by shift.Consistendy low scores over time may point to controller jierfbrmanceissues. Consistent differences in scores from shift-to-shift may point toa need for additional operator training.

Operational scoring is not the ultimate indicarion of bow well APCcontrollers perform. Ic is, however, a simple tool that can help a plantcontinue to achieve high returns from its APC applicarions. HP

Ed C h a n g is a principal process control engineer lor EquisiarChemicals. LP, in Corpus Christi. Texas. Prior to joining Equistar, heworked for Ceianese AG, Setpoint, Inc., and Aspen Technology, Inc.Mr. Chang has 16 years of advanced process control and real-timeoptimization experience, and has commissioned controls at refiner-

ies, petrochemical and chemical facilities in the US and overseas, Mr, Chang holds aBE degree in chemical engineering and computer science from Vanderbilt University,and is a registered professional engineer in Texas,

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