1989 1 - CAST

Charlie Cutler's development andsuccessful industrial application ofDynamic Matrix Control technologyover the past seventeen years are amplereasons for his receiving the AlChE CASTComputer Industrial Practice Award.Charlie Cutler has drawn on histwenty-six years of process industryexperience to refine DMC into acomprehensive process control toolconsisting of a multivariable dynamicmodel identifier and a true constrainedmultivariable controller. While manytypes of model based controllers havebeen proposed and published in theacademic and industrial communitiesIDMC has been successfully applied to awide range of real industrial controlproblems. These DMC applications haveresulted in substantial economicbenefits for the operating companies.These economic benefits result fromoperating their processes at multipleconstraints through the use of DMC.The DMC algorithm removes the slacknormally associated with processoperation under any other controltechnique. Charlie Cutler's successwith DMC has played a major role increating a heightened awareness in theprocess industries of the economicbenefits associated with advancedprocess control. This awareness haslead to increased funding of processcontrol research and developmentwhich benefits both the academic andindustrial communities.Dr. S. Vankataraman is theRecipient ofthe 1988 CASTDivision Ted PetersonStudent Paper AwardThe Ted Peterson Student PaperAward is given to recognize anoutstanding published work,performed by a student, in theapplication of computing and systemstechnology to chemical engineering.This award, supported by ChemShareand IBM, consists of $500 and a plaque.The Award will be presented onNovember 30, 1988 at the CASTDivision Award Dinner.The winner for 1988 is Dr. S.Vankataraman, formerly a graduatestudent at Clarkson University, whonow works for ASPEN Tech in Boston.The award is "for his paper entitled,"Exploiting the Gibbs-DuhemEquation in Separation Calculations."The award nomination statement ofqualifications reads as follows:Dr. S. Vankataraman is acknowledgedfor his paper entitled "Exploiting theGibbs-Duhem Equation in SeparationCalculations", which was coauthoredby himselfand his supervisor ProfessorAngelo Lucia. The paper represents thefirst ofthree papers Dr. Vankataramanproduced while pursuing his Ph.D.Degree in Chemical Engineering. Thiscitation has been chosen because itrepresents a comprehensive treatmentofthe foundational ideas on which boththe Ph.D. Thesis and the other twopublications are based. Dr.Vankataraman was completelyresponsible for the development of boththe conceptual ideas and the associatedcomputer software surrounding the useof the Gibbs-Duhem equation in aNewton-like method for solvingseparation process models. The mainideas of the paper are rigorous andfundamentally sound from athermodynamic and equation solvingperspective. At the same time, they areof significant practical importance.They represent a monumental step intheoretical and practical aspects ofequation solving techniques as theyapply to the computer and systemstechnology and chemical engineering.1989 CAST Division AwardsSolicitation of NominationsPlease use the form on the two pages atthe end of this issue to submit yournomination for the 1989 Computing inChemical Engineering Award, 1989Computing Practice Award, and 1989Ted Peterson Student Paper Award.Eight copies of the nominations for theComputing in Chemical Engineeringand Computing Practice Awards, andfour copies of the nomination for theTed Peterson Award, should be sent byApril 3, 1989 to Professor G.V. (Rex)Reklaitis, School of ChemicalEngineering, Purdue University, WestLafayette, IN 47907 (317) 494-4089.SimuSolv*: A ComputerProgram For BuildingMathematical Modelsby Kay E. Kuenker and Gary E. Blau,Global Ag Math Modeling & AnalysisAgricultural Chemical ProcessResearch, Dow Chemical Company,Midland, Michigan10

IntroductionDeveloping mathematical models of ~:'~'~~':""'~"~.}-"'""""-i~~--[5~Jphysical and chemical systems is a Lfundamental part of learning howprocesses work. Building such modelsis iterative, as shown in Figure 1.Data are collected and models areproposed to correlate and explain thedata. When necessary, more data arecollected and additional hypothesesare made until a validated model isobtained. One of the characteristics ofthese mathematical models is theirinherent nonlinearity in the modelparameters. As a result, the use ofstandard statistical tools such aslinear regression analysis l are notvalid for the parameter estimation or"fitting" portion of the model buildingexercise. Although the theoreticalaspects of the nonlinear parameterestimation problem had beendocumented', user-friendly softwarewas not available within the DowChemical Company to perform thisimportant task. Consequently, thebuilding of nonlinear models forreaction kinetics, pharmacokinetics, orecokinetic systems became the domainof "professional" model builders, thatis, scientists and engineers with astrong programming background(frequently referred to as computerjocks), a working knowledge ofnonlinear statistical principles, andsome knowledge of the physicalsystems involved.In the late seventies, it becameapparent to Dow management that asignificant impact on the profitabilityof Dow processes and products could berealized by better understanding howthey worked. This stimulated the needto build mathematical models byresearchers throughout theorganization and not just by the'(chosen few". Funds were provided toa corporate organization to develop astate-of-the-art piece of software thatcould be used by the nonprofessionalmodeling community in Dow. Themission was to allow a user with aminimal working knowledge of-- ....~Pot.""'l.',.IOp

useful for problems with only a fewadjustable parameters to optimize orestimate. The second is thegeneralized reduced gradient method(GRG2) developed by Lasdon, et al. in1978 9 , which is effective for largerproblems. The latter method has beenenhanced by the addition of the directdecoupled method (DDM), one of themost efficient and robust sensitivityanalysis procedures available lO • DDMuses gradients of the objective functionalready calculated by GRG2 and hencedoes not add greatly to thecomputational burden ll Details aboutspecific integration and optimizationalgorithms are discussed in Blau et al'.In the future versions of SimuSolv, aMarquardt method and SequentialQuadratic Programming methods areanticipated.IThe Language of SimuSolvThe language of SimuSolv is really acombination of two languages havingunique functions. The first is ACSL",The Advanced Continuous SimulationLanguage", which is used to write themodel definition program, and thesecond is the Run-Time CommandLanguage which is used incommunicating with the executive.These languages are high-level in thesense that they allow the user toaccomplish many types of complicatedcomputational tasks with a minimalamount of computer programming.Whenever possible, there are defaultsprovided that are generallyappropriate. The user can concentrateon the modeling task itself, ratherthan spending unnecessary time onthe computational methodology. Theterms used in the language have beenchosen to be meaningful andmnemonic in order to facilitate thelearning process. Although thelanguage ofSimuSolv is designed to beeasy to learn and use, the power andversatility of the underlyingcomputational and numerical analysismethods have not been compromised toachieve that end. For the moresophisticated user, error bounds,integration methods and step size, andother characteristics of thecomputations are user controlled ifdesired. Macro facilities are a part ofthe language and FORTRANsubroutines can be incorporated intothe program to tailor it to specialneeds. Before SimuSolv can beinvoked, a model definition programmust be written in ACSL. The programgenerally consists of the followingthree distinct segments:INITIAL: The code in this segment isexecuted at the beginning of eachsimulation. Initial values are assignedto variables and preliminarycalculations are performed.DYNAMIC: This segment defines thedynamic or time varying aspects of themodel. All the differential equationsare placed here along with otherstatements that are required duringthe course ofa simulation.TERMINAL: The code in this segment isexecuted only at the end of asimulation.In addition to a model definitionprogram, a fun-time command file isneeded. This file contains anyexperimental data to be analyzed suchas for parameter estimation. The dataare entered into the file in a familiartable format with the column headingsbeing model variable names. Thesecolumn headings are used for bothparameter estimation and graphicalcomparison of calculated and observedvalues.There are two phases in the use ofSimuSolv. In the first phase the modelof the physical system, written in theACSL language, is translated intoFORTRAN, compiled, linked withSimuSolv subroutines and anysubroutines the user wishes to include,creating an executable computermodule with which the user interacts.In the second phase, the user entersinto a dialogue with SimuSolv, duringwhich he or she controls the activitiesof the program. The user may ask forsimulation, parameter estimation,optimization, graphs or tables ofresults, and so on. Provision is madefor hardcopy documentation.IIHow Has SimuSolv Been Used?Chemists and engineers use SimuSolvextensively to develop themathematical models required for theeffective design, debottlenecking andoptimization of chemical processes. Itis employed in research to model thekinetics of reacting systems, inengineering to model reactors andother unit operations, and inproduction to help optimize entireprocesses. Models of unit operationshave been incorporated into steadystatesimulation programs such asASPEN' and PROCESS", makingSimuSolv a valuable complement tothese large programs. The dynamicnature of the models makes them veryuseful in studying start-up and controlstrategies for coupled unit operations.The SimuSolv program is ideallysuited to modeling the operation of achemical reactor, the key unitoperation in any chemical processl3.Batch or well-mixed flow reactors canbe simulated in a straightforwardmanner by sets ofordinary differentialequations. Fixed-bed reactors, forwhich both dynamic and spatial effectsmust be considered, are simulated bysets of partial differential equations.These equations are decomposed inSimuSolv into arrays of ordinarydifferential equations for easy,efficient handling. A reactor modelmust contain a verified model of thereaction kinetics. SimuSolv is usedeffectively to build such kineticmodels. SimuSol v has beenincorporated as an integral part of theiterative procedure, which was shownin Figure 1. Starting with availabledata and some knowledge of thechemical reactions, one or more kineticmodels are postulated. These12

conceptual models are translated intoa model definition program andSimuSolv is then used to comparecalculated and experimental results.The models and the data are studied todetermine where the data or kineticmodels are inadequate. Quitefrequently, additional experimentsmust be designed to distinguishbetween the models or to get goodestimates of model parameters. Theparameter estimation features ofSimuSolv are invoked to obtain thebest set of kinetic parameters for eachproposed modeI and, finally, thestatistical output from SimuSolv isexamined to help decide which modelis the best one. This procedure leads toa kinetic model that can beincorporated with confidence into anengineering model of a reactor. Toillustrate the capabilities of SimuSolvas well as the concepts ofmodeling anddesign of experiments for modeldiscrimination and parameterestimation, several examples will bepresented.III Parameter Estimation WithSimuSolvConsider the following chemicalmodel:whereEONH3MEADEATEAEO+NH3EO+MEAEO+DEAK~ MEA~DEA~ TEA= ethylene oxideammonia= mono-ethanolamine= di-ethanolaminetri-ethanolamineand the corresponding kinetic modeldMEAJdt = K/'EO*NH3-K 2 *EO*MEAdDEA/dt = K 2 *EO*MEA-K 3 *DEA*EOdTEA/dt = K 3 *EO*DEAEO = EOIC-(MEA-MEAlC!-2IDEA-DEAlC!-3ITEA-TEAIC!NH3Time MEA DEA TEAENDNH3IC-(MEA-MEAIC!-IDEA-DEAlC!-(TEA-TEAIC!The following Table shows theavailable set of sequentialconcentrationdata, as a function oftime, for components MEA, DEA, andTEA:DATA0.0 0.0 0.0 0.00.1 0.39 0.09 0.010.2 0.46 0.20 0.030.3 0.43 0.31 0.100.4 0.37 0.37 0.150.8 0.34 0.39 0.191.0 0.32 0.40 0.221.2 0.29 0.40 0.251.4 0.28 0.40 0.261.6 0.27 0.41 0.281.8 0.27 0.42 0.282.0 0.25 0.42 0.29Questions that need to be answeredare:1. What are the best values for therate constants K[, K2, andK3?2. What are the standard deviationsor uncertainties in these estimatedrate constants?3. How well do the calculated curvesfit the data?The first step to be taken in order toanswer these questions is to translatethe kinetic model into a SimuSolvmodel definition program in the ACSLlanguage. Every model definitionprogram follows the same format,beginning with the word PROGRAM andending with the word END. In betweenare the three optional segments,INITIAL, defining the initial conditions,DYNAMIC, containing the time varyingportions of the model, and TERMINAL,which performs calculations afterintegration has been completed. Amodel definition program of the aboveprocess is shown below.The dollar ($) sign allows one to placemore than one statement on a line,comments are placed between singlequotes, and the ellipses C..), permit thecontinuation of one line to thesucceeding one.The second step is toinvoke SimuSolvand get into the run-time mode. Toperform a simulation, i.e., solve thedifferential equations, a STARTcommand is used. A graphical displayof the results of the simulation isobtained with the command PLOT MEADEATEA.Figure 2 shows that the valuescalculated by the model follow thebasic shape of the data. However, therate constants Kl, K2, and K3 are notat their optimal values to yield thebest fit. SimuSolv allows the user tointeract with the model to study theeffect ofchanging the values ofKl, K2,and K3. This interaction allows theuser to more fully understand thesensitivity of the model to parameterchanges. Once the best "eye-fit" todata is obtained, SimuSolv can be usedto estimate the optimum values for theparameters Kl, K2, and K3·SimuSolv uses the method ofmaximum likelihood to performparameter estimation2. This methodmaximizes the probability that aparticular set of parameter estimates,e.g., Kl*, K2*, K 3*, generated theexperimental data. The generation ofmaximum likelihood estimates is aniterative process. Starting with initialguesses of the parameters supplied bythe user, nonlinear optimizationtechniques are used to generatesuccessive values of the parameterswhich are selected to maximize alikelihood function. This likelihoodfunction is the calculated probabilityof our having measured a given set ofdata given the current set ofparameters and the correct model.Another attractive feature of themethod of maximum likelihood is theability to estimate the parameters of astatistical model ofexperimental errorvariability along with the parametersof the mathematical model. This13

statistical model is used to properlyweight the data extracting thegreatest information content fromthose data points that have thesmallest variability. One restriction,however, is that the form of the errormodel must be assumed a priority. InSimuSolv, these assumptions take thefollowing form:1. The experimental errors arenormally distributed.2. The measured values areindependent of each other (thevalidity of this assumption isquestionable even though theexperimentalist generally strivesto make it true for his or hermeasurements).3. The measured values areuncorrelated.The final assumption is unique toSimuSolv and requires someexplanation. The variability of themeasured values is given by therelationship,where..2 ..2r V i(S )0') = Wl) . ,toI - Jis the variance of the ithresponse for the jth datapoint tj (for simplicity, itwill be assumed that timeis the only independentvariable).is the predicted values ofthe ith response for the jthdata pointis a statistical parameteris another statisticalparameter called theheteroscedastisityparameter.This functional form simply relates thevariability of the measurement to themagnitude of the measured values.Two limiting cases are of interest.When yi= 0, oil = UJi/ so thevariability is constant over the entiredata region. When yi = 2, thevariability is directly proportional tothe measured values. This latter casecorresponds to the situation where therelative errors in the measurementsare constant over the experimentalregion. In SimuSolv, the parametersUJij and Yi are estimated along with theparameters ofthe mathematical model(KI, K 2, and K3 in this example). ThisPROGRAMINITIALVariableConstantConstantConstantConstantConstantConstantCINTERVALENDDYNAMICDERIVATIVETime = 0.0K, = 3.0, K 2 = 2.0, K, = 1.0NH3IC = 1.0, EOIC = 3.0MEAIC = 0.0DEAIC = 0.0TEAIC = 0.0TSTOP = 2.0CINT = TSTOP/50.Write differential equations'$'Define Run variable'$'Initial Parameter Estimates'$'Starting Material'$'Initial values for'$'State variables'$'Length ofexperiment'$'Communication interval'DMEADTDDEADTDTEADT= K 1 *EO*NH3 - K 2 *EO*MEA= K 2 '" EO'" MEA - K 3>to EO'" DEA= K 3 *EO*DEA'Integrate Differential Equations'MEADEATEA= INTEG(DMEADT,MEAICJ= INTEG(DDEADT,DEAICJ= INTEG(DTEADT,TEAIC)ENDEONH3ENDTERMT(TIME .GE. TSTOP)ENDTERMINALEND'Write Mass Balances'$'Test to Determine End oflntegratiou'= EOIC - (MEA - MEAIC)- 2*lDEA - DEAIC) - 3*(TEA - TEAIC)= NH3IC-(MEA-MEAIC) - lDEA-DEAICJ-(TEA-TEAIC)14

GRAPHICAL SIMULATION OU~'PUTEthanolamine RooelloD Kinetics", 7:: C o :=0".,::,.:-,'=':.:0:;.'-=w,,'='h:...;::E'~.:::.'~'=o=.o=':::,~' =0::'':.;'_'--.r---,,•~:, "'0' +-_.. . '. -_·e-·····t,,·_ e-. __ ._-:,.~_ ••• _.~•.-.---~ i,'~j : :.l : ~~i·_=~::::'4',• : :' t '0: -6- 1.. 1 .. ~::::=~~~1 ._. _, ... . .~ : : i :8"'0 -- -- .. --~---- -- ; . ~-- .~------ .f • ~ iii / 1 ' • ,°0,0 ... ~.4 ' B i iII. 1.2 1.1'1Figure 2: Graphical Output ofComparison of Reaction Kineticswith Experimental Data~.llGRAPHICAL OUTPUT AFTER OPTIMIZATIONElhanol~mlne Reaction KIneticsCOlllparialon wllh Experlmenlal Data,,, : ,~O.•_- _ ••••••_--j--- -- :~f--•6.-.----1----(';I--.-.. --.--. : ~ : . : :, "~~. i:~ r·······-·---T--·-----· Q .- ••••••••-. ·~····--------···~I~ : :E)~. : : , ,~ --.'..171 ····--·----·r····--·····1"'--··--------":.·czt····mlm mi··mmj .0.0 U.4 ,I II ; ? ,I "Figure 3: Graphical Output AfterParameter Estimation. Comparison ofReaction Kinetics with Experimental Data.assures that the data are properlyweighted in a statistical sense. Theuser can control the weighting, but aproperly designed experiment withreplication is preferred. To obtain thelikelihood estimates in the exampleproblem, the following commands areissued:VARY K,K,K 3FIT MEA DEA TEAOPTIMIZEThe resulting plot is shown in Figure 3.When SimuSolv has found thelikelihood estimators, it does one finalsimulation to prepare a set ofcalculated values for graphing, andthen it reports the results of theestimation process (see Table 1). Thereis a lot of statistical information in thereport, but this discussion will focus onjust a few of the items. UnderPARAMETER ESTIMATES, the initial andfinal values of the rate constants arepresented with an improvement(increase) in the Likelihood Function.STANDARD DEVIATION gives a measureof the uncertainty in the estimates ofthe rate constants. The OBSERVED andPREDICTED values of the threevariables are self explanatory.RESIDUAL PLOTs are bar graphsshowing in an approximate mannerhow the RESIDUALS are distributed.Other parts of the report are discussedin more detail in the SimuSolvIntroductory GuideI4.SimuSolv also can help us answerquestions of uncertainty or quality ofthe parameter estimates. Contourplotting features allow one tographically view the confidenceregions for the estimated parameters.There are three methods forcalculating these regions: (l) Linear,which is based on a Taylor seriesapproximation, (2) Nonlinear, whichuses the F-statistic 15 , and (3) Exact,which is based on Bayes' TheoremI 6.These confidence regions can beobtained by using the MAP command inSimuSolv (Figure 4).In addition to confidence regions, twoandthree-dimensional graphics of theobjective function may be obtained byusing various subcommands of theMAP command. The surface plot of the. Log Likelihood Function is shown inFigure 5.IV Nonlinear Experimental Designwith SimuSolvDuring the model building process, itis frequently possible to postulateseveral physically meaningfulmechanistic models to represent theprocess under study. The problem is todiscriminate or choose between thesepostulated models using statisticalanalysis until the most suitable one isfound. At times, however, theinformation content of the data is solow that it is impossihle todiscriminate between models.Therefore, additional experimentsneed to be designed. SimuSolv is avery useful tool for this purpose. It canbe used to determine where in anoperating region the postulated modelspredict different results from oneanother. Such points are the mostuseful for discriminating rival models.The following example will illustratethese concepts.Consider the following gaseousreaction for the reduction of nitricoxide I ?:NO + H2A+B15

NONLINEAR CONFIDENCE INTERVALSCon Lour PloLConfidence Lovel2.7,--------'-'==-'-'-------,2.6 ..~ 2.52.4 .2.3 +---+---+----1---+----13.8 3.9 •.0 4.1 4.2 ,1.3PERSPECTIVE PLOT OF OBJECTIVE FUNCTION• > 60'Surface Plol120 ,---------,-------~" o - 100 ,.- u~ 00­u.~ ~O·po20 L;2~~3=:::4=:fi==~~""---~---..------==l;;~-- -:;-:;::=JKI 0 7 I 2 K2Figure 4. Nonlinear Confidence In,ervals forK1 and K2 generated by the command MAP NONLIN.parameter estimation capabilities ofSimuSolv can be used to estimate theparameters efor each of the postulatedmodels. Table 2 shows an isothermalanalysis of the data. The pointestimates for the parameters arepresented at the differenttemperatures; they suggest that Model3 be selected over models 1 and 3 sincethe adsorption constants are positivefor Model 3. Table 3 shows anonisothermal analysis of the data. Itis now no longer possible to rejectmodels based on the signs of theparameters and statistical criteriasuch as likelihood ratios or residualanalysis, which are normally used todiscriminate the models. Thelikelihood ratios presented in Table 3fail to discriminate Model 1 fromModel 2 but suggest that Model 3 ispreferred.Figure 5. Three Dimensional Surface Plot ofthe Objective Function Used in Parameter Estimation.Generated with the Command: MAP FUNCTION 3-D.The three-dimensional graphicscapabilities of SimuSolv were used tocompare all three models over theentire experimental region. Tocompare the models, a differencefunction was set up and plotted over anextrapolated experimental region.The difference between models 1 and 3is shown in Figure 6 and Figure 7 by acontour plot and surface plot,respectively. Examining the figureskx 10Temp.•4g-mole/(min) KA.atm- 1 K B .atm-1°c(g-catalyst)LogLikelihoodMODEL 1375 2.45E-3 ± 2.18E-4 3.60E5 ± 3.36E7 5.45E5 ± 5.09E7 4.59400 3.20E-3 ± 8.31 E-4 935.0 ± 1.78E4 732. 142E4 -923425 8.41 E-3 ± 1.59E-4 2.64E5 ± 5.96E7 5.04E5 ± 1.14E8 -10.80MODEL 2375 1.60E-3 ± 1.13E-4 4.28E5 ± 6.24E7 6.49E5 ± 9.43E7 4.59400 4.10E-3 ± 1.49E-3 1130.0 ± 2.48E4 880 ± 1.94E4 -9.20425 4.55E-3 ± 5.38E-4 8.96E4 ± 1.23E7 1.56E5 ± 2.15E7 -10.84MODEL 3375 5.13E-4 ± 1.25E-4 13.4 ± 4.33 18.8 ± 4.52 5.08400 4.66E-4 ± 8. 05E-5 48.8 ± 20.6 47.0 ± 19.0 -4.20425 1.02E-3 ± 3.70E-4 23.0 ± 14.9 33.7 ± 17.8 -9.7Table 2: Isothermal Data AnalySIS17

ParaMmetersModel 1 Model 2 Model 3k' 4.75E-3 ± 3.04E-3 4.7E-41.47E-3 4.13E- 8.65E-5E 19.6 ± 1.30 19.6±1.27 19.8± 1.03KA 130 ± 350 207 ± 793 40.6± 17KB 199±498 314±1154 46.9±17Lo,Likeli- -31.4 -30.7 -24.6hoodFunctionTable 3: Nonlsothermal Data AnalysIson the contour plot it is evident thatthe models are the most different inthe upper right corner where thepartial pressure ofA is 0.5 and that ofB is 0.5. Looking at the surface plotshows the same thing, however, thevertical axis illustrates that there isreally very little difference at allbetween Modell and Model 3. Figure8 shows the surface plot of thedifference between models 1 and 2 fornonisothermal data analysis withtemperature versus partial pressure ofA. Again the greatest differencebetween the models appears to bewhere the partial pressure of A is 0.5and the temperature is high. Thus, tocorrectly discriminate among models,additional experiments should bedesigned and run where the models arethe most different.VDesign of Experiments ForParameter EstimationOnce the model discrimination processhas been completed and a suitablemodel found, it is necessary to find thebest parameter estimates for thatmodel. SimuSolv can be used todetermine the maximum likelihoodestimators of the parameters andquantify the uncertainty in theestimates. There are three methods inSimuSolv to approximate theconfidence regions around theadjustable parameters: (1) Linear, (2)Nonlinear, and (3) Exact. The LinearMethod gives an approximation of theconfidence region by truncating at thesecond term of a Taylor seriesexpansion of the function around thelikelihood estimators. The secondmethod uses the nonlinear form of themodel, but employs the F-statistic todetermine the confidence limits, whichis valid for linear models only. TheExact method is based on Bayes'Theorem, which uses priorprobabilities ofthe parameters and thelikelihoods of those parameters". Ifthe confidence regions areunacceptable, the modeler is forced tocollect additional experimental data toreduce or minimize the uncertainty inthese parameter estimates. SimuSolvbecomes a very useful tool for locatingexperimental conditions that willreduce this uncertainty since it ispossible to simulate the confidenceregions over the experimental regionsbefore the experiments are conducted.This concept is best illustrated byexample.Consider the following reactionscheme:Estimate k i and k2 from the followingsequential concentration time dataand determine the uncertainty in theparameter estimates:TimeConcentration1.0 70.22.0 81.94.0 76.47.0 74.124.0 50.748.0 37.455.0 20.572.0 18.30.5Contour Plot03'Con lour ond Surfoce Plol0.40.3m0.20.10' 00600·00'5~ 0·00300·00150'0000-0,00'50.00.00.10.2 0 . .3A0.40.50·3B 0·20:00. 0Figure 6: Contour Plot of the Objective Function:The Difference Between Modell and Model 3.Figure 7: Surface Plot of the Objective Function:The Difference Between Modell and Model 3.18

Can lour and Surface Plol100oTo solve this problem using SimuSolvproceed in the same manner as theother illustrations, building the ACSLmodel definition program, andperforming simulations and parameterestimation, thereby obtaining themaximum likelihood estimators for kiand k2. The graphical result of this isshown in Figure 9, which shows thatthe fit of the data is very reasonable.But the question must be asked, Howgood are those parameter estimates?or, stated in another way, What is theconfidence region around ki and k2?The confidence regions calculatedusing the Linearization method areshown in Figure 10. This ellipticalregion suggests that a high degree ofconfidence can be placed in the factthat ki and k2 are at their optimum.However, if the confidence regions aregenerated using the exact method, atotally different result is obtained asillustrated in Figure 11. In this figure,note that k2 is bounded above andbelow by 0.028 and 0.016, respectively.Although ki is bounded below by 1.75,it appears to be unbounded above,implying that the area of theconfidence region is infinite. Theexperimental data provide anexplanation for this phenomenon. Byreferring to the fit of data from Figure9, note that k2 is determined from 7300 0. 0Figure 8: Surface Plot of Objective Function: The Difference Between Model1 and Model 2 with Nonlsothermal Data Analysisdata points whereas the uptake rate kiis determined by one point only.Therefore, it suggests additionalexperiments in the region of 0 to 1. 0hours. For example, by adding oneadditional data point taken at time =1.0 hours and re-fitting the data, theconfidence region was greatlyimproved, as shown in Figure 12.VI Optimization With SimuSolvIn the previous examples, the use ofSimuSolv for nonlinear parameterestimation and experimental design inthe building of mechanistic models hasbeen emphasized, However, once suchmodels have been built and validated,SimuSolv can be used to optimize theirperformance. SimuSolv contains thenonlinear parameter optimization codeGRG2 developed by Lasdon'. Anyobjective function and nonlinearconstraints can be defined in the modeldefinition program. Then thecommands MAXIMIZE or MINIMIZE areused in the run-time environment tocall this objective function. The VARYcommand demonstrated earlier is usedto identify those parameters to beoptimized. If the parameters aresubject only to univariate constraints,a direct search method is alsoavailable. As with any nonlinearoptimization procedure, the quality ofthe initial estimates supplied by theuser are the primary factors incontrolling the performance of theoptimization procedures both in termsof efficiency and convergence to anoptimum. One particularly attractivefeature of doing optimization withSimuSolv is the ability to use the MAPcommand and other graphical featuresto display the behavior of the objectivefunction in the vicinity of the optimumfor any pair of optimization variables.This can frequently help the user avoidfalse optima or saddle points.ConclusionSimuSolv is a very useful tool forbuilding and optimizing modelscharacterized by systems ofdifferential equations. It is being usedextensively in academic, industrial,and governmental agencies byscientists and engineers who have aneed to build and use mathematicalmodels but who are not professionalmodelers. SimuSolv is available forboth the IBM and DEC Mainframes aswell as MICROVAX and SUNworkstations. The package is beingdeveloped and enhanced by DowChemical Company's EngineeringResearch Laboratory. Some19

fl·°T ....,._C~O~.~.O~'c.I.~O!!!n~oC'r-'"~O~d~·,T·~Il~h""O~.~IO~:--I•.;g CONTCJI.R PLOT~.,----------------,ro:'-I~_-::iil::::-j:----::t:... ···············1:::, ' , ,, j! , i,: : i, , ,j i ' !,' ,1iJ !l : : ••••••••• ',: •••••••••••••••:,~•••••••••••••••.u ••••••••••: ••••g ···············:········..••••..r················[·····.. ···~····f······o·~o----+.,,;----t;----i..;,----i;o---1"TIU(Figure 9: Graphical Output from ParameterEstimation. Comparison of SimulationResults to Experimental Data.CONTOUR PLOTg, r-------------:;-----~~"~~+-----.....---....,.-----;ir_---~I0.0 ~O 4,0 6,0 8.0KlFigure 10: Confidence Regions for k , andk 2 Calculated by Linear Approximation.Contour ,PlolConfidcncc Level0.032 ~------------------.,0.028'IN Cl '\0.024~ill0,020.' 0, •\:...-.'-:'/,.~ ....,~ O+----,---.,----r---I0.0~O4,0KlG.OFigure 11: Confidence Regions for k , andk 2 Calculated Using the Exact Method.anticipated enhancements are theabilities to handle systems of algebraicand differential equationssimultaneously, to design experimentsfor both model discrimination andparameter estimation, and to readilyhandle systems of partial differentialequations. The product is beingmarketed in the United States andCanada by Mitchell and GauthierAssociates, 73 Junction Square Drive,Concord, MA, 01742, (617) 369-5115,0.0168.0 0.012and in Europe by Rapid Data, Inc.,Brighton, England. Theseorganizations may be contacted byindividuals interested in licensing theproduct.Bibliography1. Draper, N. and H, Smith, AppliedRegression Analysis, John Wiley & Sons,New York, 1981.2 3 4 SKlFigure 12: Confidence Regions for k, and k2Calculated Using the Exact Method with OneAdditional Data Point at time = 0.5 hours.2. Bard, Y., Nonlinear ParameterEstimation, Academic Press,New York,1974.3. Blau, G.E., and G.L. Agin, Application ofDACSL to the Design and Analysis ofChemical Reaction Systems, AIChESymposium Series, No. 78 on ComputerAided Design and Process Analysis, 1982.4. Slau, G.E. and K.E. Kuenker, The Use ofSimuSolv to Build Models of BiologicalSystems, Proceedings of the IEEE20

•5.6.7.8.9.Conference on Medicine in the BiologicalSociety, Boston, MA.1987.Neely, W.B., G.E. Blau, and G.L. Agio, TheUse of SimuSolv to Analyze FishBioconcentration Data, Chemometricsand Intelligent Laboratory Systems, Vol1,1987.Blau, G.E. and W.B. Neely,Pharmacokinetics in Risk Assessment,Drinking Water & Health, Vol 18,National Academic Press, Washington,D.C., 1987.Hindemarsh, A.C., LSODE and TSODI, TwoNew Initial Values O.D.E. Solvers, ACMSignam Newsletter, Vo115, 1980.Spendley, N., G.R. Hext, and F.R.Himsworth, Technometrics, Vol 4, pp.441,1962.Lasdon, L.S., H.D. Warren and M. Rather,GRG2 Users Guide, Department ofComputer and Information Services.Cleveland State University, Cleveland, OH,1978.10. Dunker, A., The Decoupled-DirectMethod for Calculating SensitivityCoefficients in Chemical Kinetics JChern. Physics, VolS!, 1984. ' .11. McCroskey, P.M., Direct DecoupledSensitivity Analysis of DifferentialSystems with Application to NonlinearParameter Estimation, Master's Thesis,Carnegie·Mellon, 1985.12. Mitchell and Gauthier AssociatesAdvanced Continuous Simulatio~Language, Concord, MA,1986.13. Blau, G.E., K.E. Kuenker, G.L. Agin, andE.C. Steiner, Chemical Reactor Designand Analysis with SimuSolv*Proceedings of the 7th Internationa;Symposium on Large Chemical PlantsBrugge, Belgium, 1988.'14. Steiner, E.C., a.E. Blau, and G.L. Agio,SimuSolv Introductory Guide, DowChemical Company, 1776 Building,Midland, MI 48674, 1987.15. Reilly, P.M., G.E. Blau, The Use ofStatistical Methods to BuildMathematical Models of ChemicalReacting Systems, Can. J. Chern. Engr.,Vol 52, 1974.16. Reilly, P.M., The Numerical Comput·ation of Posterior Distributions inBayesian Statistical Inference, J. of theRoyal Statistical Society, Series C., Vol 25,1976.17. Ayen, R.J. and M.S. Peters, Ind. Eng.Chern. Process Design Develop., Vol. 1,p.204, Kittrel et. al., AIChE Journal, Vol.11,6(19621.***'Trademark ofThe Dow Chemical CompanyRegistered Trademark ofMitchell &Gauthier, Inc.• Trademark of ASPEN Technologies, Inc.*'*'Trademark ofSimulation Sciences, Inc.*****************************Computer-Assisted Processand Control Engineeringby Authors: W. Westerberg, HenryChIen, James M. Douglas, Bruce A.Finlayson, Roland Keunings, ManfredMorari, Jeffrey J. Siirola, and WilliamSillimanWe would like to thank Robert Simonat the National Research Council forallowing us to have permission topublish Chapter 8 from Frontiers inChemical Engineering: ResearchNeeds and Opportunities (Copyright1988 by the National Academy ofSciences). All figures, except Figures8.2 and 8,4, have been left out.AbstractComputers and computationalmethods have advanced to the pointwhere they are beginning to havesignificant impact on the way in whichchemical engineers can approachproblems in design, control, andoperations. The computer's ability tohandle more complex mathematicsand to permit the exhaustive solutionof detailed models will allow chemicalengineers to model process physics andchemistry from the molecular scale tothe plant scale, to construct modelsthat incorporate all relevantphenomena ofa process, and to design,control, and optimize more on the basisof computed theoretical predictionsand less on empiricism. This chapterexplores the implications of thesechanges for process design, control,operations, and engineeringinformation management. Futuredevelopments and opportunities inprocess sensors are also covered.Imagine a room 50 feet long and 30feet wide, filled with cabinetscontaining 18,000 vacuum tubesinterconnected by many kilometers ofcopper wire. This was the ENIAC(Figure 8.1), a 3D-ton behemoth thatwas the world's first electroniccomputer. Now imagine that ENIAChad been given the task of solving a setof simultaneous linear equations thatembodied 30,000 independentvariables. With a team of peopleworking around the clock to recordintermediate solutions and feed themback into the computer (ENIAC had alimited amount of memory) ENIACwould still be chugging aw;y morethan 40 years later to solve theproblem! A supercomputer usingmodern algorithms could solve theproblem in an hour or so.The speed and capability of the moderncomputer have tremendousimplications for the practice ofchemical engineering. In the futurecomputer programs incorporatin~artificial intelligence or expert~ystems will help engineers designImproved chemical processes moreefficiently. Complex computationsbased on fundamental engineeringknowledge will allow engineers tode.sign reactors that can virtuallyehmmate undesired by-productsmaking processes less complex andles~ polluting. New sensors, many ofwhICh will be miniature analyticallaboratories tied to miniatureelectronics, will allow rapid andaccurate measurements for controlthat are currently impossible. Newchemical products that today arediscovered predominantly throughlaboratory work - for instancereinforced plastics that are as stron~as steel and weigh less than aluminumor drugs with miraculous properties ­may be discovered in the future bycomputer calculations based on models21

that predict the detailed behavior ofmolecules.Chemical engineers will lead thisrevolution. They will need to betrained to use advanced computertechniques for process design, processcontrol, and management of processinformation. Advanced engineeringdevelopment will be based more thanever on mathematical modeling andscientific computation. Reliablemodeling at the microscale, theindividual process unit scale, and theplant process scale will improve ourability to scale up processes in a fewlarge steps, possibly bypassing theneed for a pilot plant and saving thetwo or three years required to buildand operate it. Process models capableof predicting dynamic behavior,operability, flexibility, and potentialsafety problems will permit theseaspects of a process to be consideredmore fully earlier in the design stage.Because improved computers canperform the extensive computationsrequired by such models, it will bepossible to test alternative designsmore quickly.A chemical process must be designedto operate under a chosen set ofconditions, each of which must becontrolled within specified limits if theprocess is to operate reliably and yielda product of specified quality.Accurate, complex, computer-solvablemodels of chemical processes willincorporate features of the controlsthat are needed to maintain thedesired process conditions. Suchmodels will be able to predict theeffects of process excursions and thecontrol measures needed to correctthem. Computer management of theprocess operation will rapidly initiatecontrol correction of processexcursions. The development of newtypes of process sensors will beessential to this degree of processcontrol and will eliminate the timeconsumingwithdrawal of processstream samples for analysis.The design of a commercial process cangenerate an almost uncountablenumber of possible solutions forseemingly simple problems. Even adecade's 10,000-fold increase incomputational capability in terms offaster computer speeds and betteralgorithms does not permit a person tosearch among these alternatives, norwould such a search make strategicsense. Give engineers a designproblem for which the best solution isobvious from today's technology, andthey will quickly write down thecorrect solution without searching. Ifthe solution is not obvious, they willoften home in on the informationneeded and perform the computationsthat will expose the right solutionquickly and with minimal effort. Byusing intuition and experience, theyeliminate the need for testing everypossible alternative. We need tounderstand how to use computertechnology in much the same way, tosolve complex problems where many ofthe decisions are based on qualitativeinformation and insights that developas the problem is attacked.Encoding this activity in the computerinvolves a type of modeling in whichthe capabilities of the designer and histools, the alternative procedures bywhich complex problem solving can beperformed, and effective methods ofinformation management are allincorporated. Advances in artificialintelligence, expert systems, andinformation management willrevolutionize the automation of thisactivity, giving us computers that candisplay encyclopedic recall of relevantinformation and nearly humanreasoning capabilities. A HAL 9000 of2001: A Space Odyssey fame mayindeed exist in the future.Computer-generated visualinformation, for example, threedimensionalportrayal of proposed newdesigns, will be commonplace in thefuture. Communication will be innatural language, using both picturesand voice. This setting, which willaddress the need for new chemicalprocesses and products by harnessingalmost unimaginable computingpower, will provide significant newresearch opportunities in chemicalengineering.Using The Computer's PotentialEach decade over the last 35 years hasseen the processing speed of newlydesigned computers increase by afactor of about 100 owing to advancesin the design of electronicmicrocircuits and other computerhardware. On top of this has beenanother 100-fold increase per decade incomputer speed, thanks to moreefficient methods of carrying outcomputations (algorithms). It is notwidely appreciated that newalgorithms have been as valuable ashardware design in improvingcomputer performance. With thecombination of improved computerhardware and better algorithms,effective computer speeds have morethan doubled on average each year.The availability of computingresources is also increasing rapidly.The actual and projected availabilityof high-speed supercomputers is shownin Table 8.1. The projection for 1990 ­at least 700 computers ofCray I classrepresents35 times more availablecomputing power than that availablein 1980. While continued substantialinvestment in supercomputers isneeded, support for better ways ofusing them should not be neglected. Itis conceivable that a new algorithmcould effectively increase the power ofsupercomputing for a specific problemby a factor of 35 overnight. During thelast two decades, many developmentsin numerical analysis have had aprofound impact on scientificcomputation.Clearly computer technology hasimproved rapidly; there is little reasonto doubt that it will continue to do so.The problem is now and will continue22

-IPROCESS DESIGN IN THE TWENTY·FIRST CENTURYIt is April 2008. A process engineer for a medium-size chemical company is designing a process for a new series ofproducts her company is developing. By talking to her computer and pointing to the screen, she indicates that theprocess needs a reactor and some separators, as well as recycle capability. The computer, using an artificial intelligenceprogram tied to mathematical models, determines how the reactor temperature and pressure will influence the productyield and how the yield in turn will influence the amount ofprocess stream recycle. The computer chooses the optimaltypes ofseparators and indicates how they should be sequenced for different process streams and products ofthe series.After 20 minutes on a tera[lop machine (equivalent to 1 week of computing on a CRAY I of the 1980s), the computerindicates that the general process design is completed, having given partial information during the 20-minutecomputation.The graphical output from the computer shows the process [lowsheet, with several separation units and projectedequipment and operating costs. It also [lags information that is uncertain because it had to use thermodynamic dataextrapolated from measured values. At the engineer's request, the computer shows several alternative [lowsheets it hadconsidered, indicates their projected costs, and tells why it eliminated each of them. Some of the flowsheets wereeliminated because ofhigh cost, others because they were considered unsafe, others because the startup procedures wouldbe difficult, and still others because they were based on uncertain extrapolation ofexperimental data.After perusal of these process options, the engineer asks the computer to select five designs for further study, and thecomputer produces a paper copy ofthe [lowsheet and design parameters for each. She then tells the computer to preparemore rigorous designs from each of the five [lowsheets. The computer questions the third design because it involvesextensive extrapolation ofknown thermodynamic data. However, since the engineer feels that this may be one ofthe betterdesigns, she asks the computer to notify the laboratory management computer, which is connected to a laboratorytechnician's terminal, to begin experimental determination ofthe missing thermodynamic data so that the design can beprepared. The computer reminds the engineer that precise control ofprocess conditions will be essential for another ofthe designs and that a new sensor may be necessary. The engineer consults the artificial intelligence program, whichtells her that either oftwo sensors being developed in another company department may fill her need. The engineer usesher computer to schedule a meeting with the manager ofsensor development.Two years later, the process is producing one of the new products. The time from design to operation was short enoughthat the company was able to capture a new market, and it plans to extend the business with related products of theseries. The design engineer has developed a real-time dynamic simulation of the process and is running it on thecompany's parallel supercomputer to test strategies for producing the related products from the process equipment. Shequeries another computer to search the historical records ofplant operation. She wants to know how well the dynamicmodel mirrors the actual plant operation and when the process parameters were last adjusted to give a better fit betweenthe plant and the model. She feeds this information to her computer, which devises a comprehensive plan for schedulingproduction among four of the new products, with provision for revising the schedule as inventories and sale of theproducts change. A new business has been created.to be the lack of people trained to applycomputer technology to scientific andengineering tasks. The improvementssuggested in Chapter 7 in terms of ourability to design and control betterchemical products and processes willbe made by chemical engineers whounderstand computers - not bycomputer scientists or by softwareengineers. The countries thatunderstand this distinction will leadthe world in chemical technology.YearNumber of CRAY I- ClassSupercomputers1980 211085 1421990 700-1,000Table 8.1: Actual and EstimatedSupercomputing ResourcesAvailable to Researchers in theUnited States, 1980-1990Mathematical Models ofFundamental PhenomenaChemical engineers have traditionallyused mathematical models tocharacterize the physical and chemicalinteractions occurring in chemicalprocesses. Many ofthese models eitherhave been entirely empirical or haverelied on crude approximations of thebasic physics or chemistry of theprocess. This is because a typicalchemical process comprises anassemblage of interacting flows,23

transports, and chemical reactions.Accurate analysis and prediction of thebehavior of such a complex systemrequire detailed portrayal of thephysics of transport and the chemistryof reactions, which calls for complexequations that do not yield totraditional mathematics. Nonlinearpartial differential and integralequations in two and sometimes threespatial variables must be solved forregions with complicated shapes thatoften have at least some free boundary.The more accurate the model, the moremathematically complex it becomes,but it cannot be more complex thanallowed for by the available methodsfor solving its equations.Before the advent of modern computeraidedmathematics, mostmathematical models of real chemicalprocesses were so idealized that theyhad severely limited utility - beingreduced to one dimension and a fewvariables, or linearized, or limited tosimplified variability of parameters.The increased a vailability ofsupercomputers along with progress incomputational mathematics andnumerical functional analysis isrevolutionizing the way in whichchemical engineers approach thetheory and engineering of chemicalprocesses. The means are at hand tomodel process physics and chemistryfrom the molecular scale to the plantscale; to construct models thatincorporate all relevant phenomena ofa process; and to design, control, andoptimize more on the basis of computedtheoretical predictions and less onempiricism. Chemical engineers,using advanced computationalmethods and supercomputers, can nowreadily identify the importantphenomena in a complex chemicalprocess over the entire range ofapplicable conditions by exhaustivesolution of detailed models. Thebenefits of investing in less empirical,more fundamental mathematicalmodels are becoming clear:• The capability to constructmathematical models that morefully incorporate the basicchemistry and physics of a systemprovides a mechanism forassessing understanding offundamental phenomena in asystem by comparing predictionsmade by the model withexperimental data.• Better models can replacelaboratory or field tests that aredifficult or costly to perform oridentify crucial experiments thatshould be carried out. In eithercase, they will significantlyenhance the scope and productivityof chemical engineeringresearchers in academia andindustry.• In process design, it is frequentlydiscovered that many of the basicdata needed to understand aprocess are lacking. Because mostcurrent mathematical models arenot sufficiently accurate to permitdirect scale-up of the process fromlaboratory data to full plant size, apilot plant must be constructed.As models are improved, it maybecome possible to evaluate designdecisions with more confidence,and bypass the pilot plant stage.Process technologies for which the useof more comprehensive mathematicalmodels can result in majorimprovements include those forbiochemical reaction processing; highperformancepolymers, plastics,composites, and ceramics; chemicalreaction processing (e.g., reactioninjection molding, reaction coating,chemical vapor deposition);microelectronic circuits; optical fibersand disks; magnetic memory systems;high-speed coating; photovoltaic andsemiconductor materials; coalgasification; enhanced petroleumrecovery; solution mining; andhazardous waste disposal. To date themost extensive use of supercomputermodeling has been in space ageweapons technologies, whereobjectives, economics, and time framesdiffer from those in the chemicalprocess industries. It is clearly in thenational interest to stimulate the moreextensive use of advancedcomputational methods andsupercomputers in other industriescritical to our worldwide competitiveposition. A program to encourage thegreater dissemination of advancedcomputational techniques andhardware will offer challenges andopportunities to computationalmathematicians and numericalanalysts, to engineering scientists, toapplications and software experts infirms that develop and manufacturesupercomputers, and, above all, toperceptive leaders in high-technologyprocess industries.The following sections describe inmore detail a number of areas inchemical engineering in which theability to develop and apply detailedmathematical models should yieldsubstantial rewards.Hydrodynamic SystemsMuch of the current computationalmodeling research in chemicalengineering is concerned with thebehavior offlowing fluids. The generalsystem of equations that describe fluidmechanics, called the Navier-Stokesequations, has been known for morethan 100 years, but for complexphenomena the equations areexceedingly difficult to solve. Onlyrecently have methods been devised totreat such phenomena as shock wavesand turbulence. Further difficultiesarise when disparate temporal andspatial scales are present and whenchemical reactions occur in the fluid.Solutions of the Navier-Stokesequations can be smooth and steady, orthey can exhibit regular oscillations oreven chaos. In some cases the fluidflow is enclosed by a rigid boundarywith a complex shape, as in theextrusion of polymers; in others theflow is effectively unbounded and thesolution must extend to infinity, as inatmospheric systems; and in still24

HypercubesOne thing that has not changed since the earliest days of the computer age is this: the fastest available computer isnever fast enough to solve the most difficult problem of the day. Because of the limitations of current computers,simplifying assumptions about most physical systems must be made for the problem to be solved by available computertechnology. For example, in petroleum reservoir simulation, available computer power dictates how many grid pointscan be used to describe the reservoir and how complex the thermodynamic model that describes the system can be. Acomputer capable of solving these complex systems without resorting to simplifying assumptions would have to beorders ofmagnitude faster than those available today.Computer manufacturers have traditionally boosted performance by using faster components to build computers. Butthe overall architecture ofcomputers has not changed since the 1940s, and the use of faster components is a strategythat is now running into diminishing returns. New computer architectures, based on parallel processing of subelementsofthe same problem, will be needed to achieve significant advances in speed to solve the complex problems oftoday and tomorrow.One example ofsuch a new approach is the hypercube architecture, in which many processors are linked together as ateam to solve a single problem. A well-integrated team ofcheap processing units can potentially out perform the mostsophisticated single-processor machine. In addition, since each processor can have its own dedicated memory, the totalsystem can have both more memory than current supercomputers and more memory in use at any given moment.Standard programs must be broken into smaller pieces to run on a hypercube. Each processor is assigned theresponsibility for calculations for a specific piece ofa problem. For example, in petroleum reservoir simulation, eachprocessor might be assigned a different section ofthe reservoir to model. In modeling a complex chemical plant, eachprocessor might be assigned a different piece ofequipment. As each processor proceeds, it informs the other processorsofits results, so that all the other processors can incorporate the information into their respective portions ofthe overallcalculation.Hypercubes and other new computer architectures (e.g., systems based on simulations of neural networks) representexciting new tools for chemical engineers. A wide variety ofapplications central to the concerns ofchemical engineers(e.g., fluid dynamics and heat flow) have already been converted to run on these architectures. The new computerdesigns promise to move the field ofchemical engineering substantially away from its dependence on simplified modelstoward computer simulations and calculations that more closely represent the incredible complexity ofthe real world.others, such as the flow of blood invessels, the boundary is deformable.Solution of the Navier-Stokesequations for systems of technologicalinterest remains an exceedinglychallenging task; supercomputers areneeded to treat those systems that canbe solved.PolymerProcessingThe development of polymers andpolymer composites will benefitgreatly from the availability of bettercomputers and better algorithms. Theinherent properties of a polymer aregoverned by the chemical structure ofits molecules, but the properties of afinished polymer product are affectedby the interactions among thesemolecules, which are stronglyinfluenced by the way in which thematerial has been processed. While itis now possible to predict certainproperties of polymers from theirmolecular structure, the ability topredict the effect of polymer processingsteps on polymer properties is justbeing developed. Ideally it would bedesirable to model all steps from theformation of the polymer through itsprocessing and then predict the finalproperties of the material fromstructure-property relationships.Although such modeling is aformidable problem, it is becomingfeasible with the advent ofsupercomputers and improvedalgorithms.Petroleum ProductionComputation is widely used inpetroleum exploration and productionby exploration geophysicists,petroleum engineers, and chemicalengineers. As more sophisticatedtechniques are developed for locatingand recovering petroleum,mathematical modeling is playing anever- expanding role.25

Once regions that may containpetroleum are located, local geologicalfeatures must be sought that mighthave trapped the hydrocarbons. Thediscovery process is based on a kind ofseismic prospecting in which geologicmaps are constructed from reflectedseismic signals generated byexplosions or vibrations at the surfaceof the earth. These signals arereflected or refracted in varyingdegrees by different rock strata andare recorded by a set of receivers.Thus, the problem of interpretingsignals can be likened to that ofanalyzing light beams reflected by anarray of variously curved plates ofglass of different reflectivitiesseparated by liquids of differentrefractive indexes. The inversemathematical problem of determiningthe earth structure and the propertiesof the strata from the recorded signalsis extremely difficult (Figure 8.2).After a hydrocarbon reservoir has beenlocated, the flow of oil, water, gas, andpossibly injected chemicals in thereservoir must be modeled. Thischallenge is particularly appropriatefor chemical engineers working withpetroleum engineers because of theimportant role played by molecularlevel interactions between oil,subsurface water, and rock. Models forfluid flow in porous media comprisecoupled systems of nonlinear partialdifferential equations for conservationof mass and energy, equations of state,and other constraining relationships.These models are usually defined onirregular domains with complexboundary conditions. Their numericalsolution, with attendant difficultiessuch as choice of discretizationmethods and grid orientation, is achallenging intellectual problem.Once wells have been drilled into theformation, the local properties of thereservoir rocks and fluids can bedetermined. To construct a realisticmodel of the reservoir, its propertiesover its total extent - not just at thewell sites - must be known. One wayof estimating these properties is tomatch production histories at the wellswith those predicted by the reservoirmodel. This is a classic ill-posedinverse problem that is very difficult tosolve.When or ifthe reservoir is successfullysimulated, the engineer can turn tooptimizing petroleum recovery, andtheoretical ideas can be applied tomodels for various enhanced recoverymethods to select optimal proceduresand schedules (see Chapter 7).Combustion SystemsCombustion is one of the oldest andmost basic chemical processes (Figure8.3). Its accurate mathematicalmodeling can help avoid explosionsand catastrophic fires, promote moreefficient fuel use, minimize pollutantformation, and design systems for theincineration of toxic materials (seeChapter 8). For example, modeling theinitiation and propagation of fires,explosions, and detonations requiresthe ability to model combustionphenomena. Models of the internalcombustion engine can shed light onthe influence of combustion chambershape or valve and spark plugplacement on engine performance.Models at the molecular level canprovide a fundamental understandingof how fuels are burned and howgaseous and particulate pollutants areformed. This can lead to ways toimprove the design of combustionsystems.Mathematical models of combustionmust incorporate intricate fluidmechanics coupled with the kinetics ofmany chemical reactions among amultitude of compounds and freeradicals. They must also consider thatthose reactions are. taking place inturbulent flows inside chambers withcomplex shapes. Because completemodels of real combustors,incorporating accurate treatment ofboth fluid mechanics and chemistry,are still too large for presentcomputers, the challenge is toconstruct simpler, yet still valid,models by using critical insight intothe important chemical and physicalphenomena found in combustors.Chemical engineers have the mix ofexpertise necessary to accomplish this.Environmental SystemsThe environment can be likened to agiant chemical reactor. Gases andparticles are emitted into theatmosphere by industrial and otherman-made processes, as well as by avariety of natural processes such asphotosynthesis, vulcanism, wildfires,and decay processes. These gases andparticles can undergo chemicalreactions, and they or their reactionproducts can be transported by thewind, mixed by atmosphericturbulence, and absorbed by waterdroplets. Ultimately, they eitherremain in the atmosphere indefinitelyor reach the earth's surface. Forexample, the hazes of pollutedatmospheres consist of submicronaerosols of inorganic and organiccompounds, which are formed bychemical reaction, homogeneousnucleation, or condensation of gases(Figure 8.4).Models of atmospheric phenomena aresimilar to those of combustion andinvolve the coupling of exceedinglycomplex chemistry and physics withthree-dimensional hydrodynamics.The distribution and transport ofchemicals introduced intogroundwater also involve a coupling ofchemical reactions and transportsthrough porous solid media. Thedevelopment of groundwater models iscritical to understanding the effects ofland disposal of toxic waste (seeChapter 7).26

Process DesignThe primary goal of process design isto identify the optimal equipmentunits, the optimal connections betweenthem, and tbe optimal conditions foroperating tbem to deliver desiredproduct yields at the lowest cost, usingsafe process paths, and with minimaladverse impact to the environment.Design is a complex problem thatinvolves not only the quantitativecomputing depicted in the previoussection, but also the effective handlingof massive amounts ofinformation andqualitative reasoning.Computer-Assisted Design of NewProcessesDesigns for new processes proceedthrough at least three stages:• Conceptual design thegeneration (process synthesis) andtheir translation into an initialdesign. This stage incl udespreliminary cost estimates toassess the potential profitability ofthe process, as well as analyses ofprocess safety and environmentalconsiderations.• Final design - a rigorous set ofdesign calculations to specify allthe significant details ofa process.• Detailed design - preparation ofengineering drawings andequipment lists needed forconstruction.The key step in the conceptual designof a new chemical manufacturingprocess is generating the processf10wsheet (Figure 8.5). All otherelements of computer-aided design(e.g., process simulation, design ofcontrol systems, and plant-wideintegration of processes) come intoplay after the f10wsheet has beenestablished. In current practice, thepressure to enter the market quicklyoften allows for the exploration ofonlya few of the process alternatives thatshould be considered. To be fair totoday's designers, it is possible togenerate a very large number ofalternative process paths at theconceptual stage of design, and yetexperience indicates that less than onepercent of the ideas for new designsbecome commercial. Thus, thechallenge in computer-aided processsynthesis is to develop systematicprocedures for the generation andquick screening of many processalternatives. The goal is to simplifythe synthesis/analysis activity inconceptual design and give thedesigner confidence that the initialuniverse of potential process pathscontained all the pathways withreasonable chances for commercialsuccess. The advances in computeraidedprocess synthesis that arepossible over the next decade aredramatic. They include both anincreasing level of sophistication (e.g.,the synthesis of heat exchangernetworks, sequences of separationprocesses, networks of reactors, andprocess control systems) andcomputational procedures that shouldmake possible the identification of themost viable process option in arelatively short amount oftime.As the designer moves from conceptualdesign toward final design, he or shemust analyze a number of alternativesfor the final design. The developmentof large, computer-aided design·programs (so-called processsimulators) such as FLOWTRAN,PROCESS, DESIGN 2000, and ASPEN (orother equivalent programs used invarious companies) has significantlyautomated the detailed computationsneeded to analyze these variousprocess designs. The availability ofprocess simulators has probably beenthe most important development in thedesign of petrochemical plants in thepast 20 years, cutting design timesdrastically and resulting in ·betterdesigned plants.Although the available simulatorshave done much to achieve superiordesign of petrochemical processes,there is considerable room forimprovement. For example, bettermodels are needed for complex reactorsand for solids processing operationssuch as crystallization, filtration, anddrying. Thermodynamic models areneeded for polar compounds. Moreover,the current process simulators arelimited to steady-state operations andare capable of analyzing only isolatedparts of a chemical plant at any giventime. This compartmentalization isdue to the limitations on computermemory that prevailed when theseprograms were first developed. Thismemory limitation resulted in acomputational strategy that dividedthe plant into "boxes" and simulatedstatic conditions within each box,iteratively merging the results tosimulate the entire plant. Withtoday's supercomputers, it is possibleto simulate the dynamics of the entirechemical plant. This opens the way fordramatic advances in modeling andanalysis ofalternative process designs,because the chemical reactions thatoccur in manufacturing processes areusually nonlinear and interdependent,and random disturbances in theprocess can propagate quickly andthreaten the operation of the entireplant. To nullify the effects of suchdisturbances, the designer must knowthe dynamics of the entire plant, sothat control failure in anyone unitdoes not radiate quickly to other units.It is now within our reach to integratethis sophisticated level of design andanalysis on a plant-wide scale(including design and performancemodeling of plant-wide controlsystems) into the computational toolsused to analyze and optimize theperformance of individual processes inthe plant.In the detailed design stage for achemical manufacturing process, achosen process design must beconverted into a list of equipmentitems to be purchased and a set ofblueprints to guide their assembly.The design is presented as a detailedprocess flow diagram (PFDl, from which27

QuitDesign file: TT:"PLFIGaHandle FUesOperationsTEAR TEST: P&L F1gSSolution 1s 512.519.539Solution 1s 512,535,540or other equivalents5558+51r--r--5iS51.5 514SE519:-l~5752 ~TA 53 5. ~TAv, v,~TA 511 ~TA 512L.".. J524525 .TA 52.'-l>- 522 v,-520SiB521 52953Sv~ -"510513~e..-523~ 7 534 ~TA 535~527 f'I 53LF_ ~TA ~TA531V" '-W 538 v , III,L ~541539 --t>-Q- 540 542532-"-v 533Figure 8.2: Example of a flowsheet generated by computer-aided process synthesis, as would be seen on the screenof a computer terminal. The column on the left side ofthe figure shows options available in the particular designprogram being run. Courtesy, Peter Piela, Carnegie-Mellon Universityis constructed a list of all needed itemsof equipment, and piping andinstrumentation diagrams (PIDs) thatshow the equipment and itsinterconnections. The next task is toestablish the physical layout for theentire plant. Advanced computerbaseddrafting tools aid in all theseactivities.Computer-Assisted ProcessRetrofittingThe preceding section focused largelyon the design of new plants. However,these procedures can also be adapted tothe retrofitting of existing plants.Retrofitting is generally undertaken toincrease the capacity of a plant; tomake use of a new technology such asan improved catalyst, a new materialof construction, or a new unitoperation; or to respond to a significantchange in the cost of energy or rawmaterials. A fair amount ofretrofitting in the chemical processindustries in recent years has beenundertaken to improve energyefficiency through plant-wide energyintegration - retrofitting of about 50processes to incorporate modern heatexcbangernetwork synthesis conceptshas reduced energy requirements inthe chemical industry by 30 to 50percent. Retrofitting will continue toplaya major role in the design ofchemical plants as new procedures forcomputer-aided synthesis ofseparation systems are applied andresearch on process synthesis begins toyield large savings by helping existingpetrochemical plants produce the samemix of prod ucts through moreeconomical chemical reactionpathways.We need to develop a systematicapproach to analyzing the impact ofmaking changes in the connectionsbetween process units or in the size of28

fi·units that are undertaken to improveoperating costs, plant flexibility, orsafety.Research Opportunities in ProcessDesignThe overall goal of process designresearch is to develop a systematicprocedure, probably in the form of aninteractive computer program, thatcontains design heuristics andinterchangeable approximate andrigorous models that can lead anengineer from an initial concept to afinal design as quickly as possible.The final design must includeconsiderations of economics,controllability, safety, andenvironmental protection. We need toextend the conceptual and final designprocedures that have been developedfor petrochemical processes toprocesses for producing polymers,biochemicals, and electronic devices.We also must develop systematicsynthesis/analysis procedures forstudying batch processes analogous tothe procedures that have beendeveloped for studying continuouspetrochemical processes.There are some aspects of processdesign in which decisions are basedprimarily on past experience ratherthan on quantitative performancemodels. Problems of this type includethe selection ofconstruction materials,the selection of appropriate models forevaluating the physical properties ofhomogeneous and heterogeneousmixtures of components, and theselection of safety systems. Advancesin expert systems technology andinformation management will have aprofound impact on expressing thesolutions to these problems.In summary, systematic proceduresmust be developed for the following:• generation ofprocess alternatives;• quick screening of processalternatives using both rule·ofthumband short-cut calculations;• inclusion of controllability, safety,and'environmental factors in theinitial design;• more detailed screening of a smallnumber of promising designalternatives;• design of the process andmanagement of its construction;• use and extension of expertsystems concepts to handle aspectsof design that deal with a mixtureof qualitative and quantitativeinformation; and• retrofitting ofexisting plants.Process Operations and ControlProcess operations and control have atremendous impact on the profitabilityofa manufacturing operation. In somecases, they can determine theeconomic viability of a manufacturingfacility. For example, DuPont'sProcess Control Technology Panel hasestimated that if DuPont were toextend the degree of computer processcontrol that has been achieved at a fewof its plants across the entirecorporation, it would save as much ashalf a billion dollars a year inmanufacturing costs. If DuPont'snumbers are representative, the entirechemical industry could save billionsof dollars each year through morewidespread application of the bestavailable process control. This couldbe the single most effective step thatthe U.S. chemical process industrycould take to improve its globalcompetiti ve position inmanufacturing.Why are such savings suddenlypossible? Because of the explosivedevelopments in computer technology,research on operations and control isno longer constrained by lack ofcomputing power. In particular, thetraditional boundaries betweendesign, control, optimization,simulation, and operation aredisappearing. Control is becoming apart of process design; simulation andoptimization are becoming componentsofcontrol design.Research opportunities in processoperations and control lie in threeareas:• collection of information throughprocess measurements;• interpretation and communicationof information by use of processmodels; and• utilization of information throughcontrol algorithms and controlstrategies for both normal andabnormal operation.MeasurementsThe essence of process control is totake appropriate and quick correctiveaction based on measured informationabout the behavior of the process. Theconcept of the process is contained in aprocess model, and measurements areused to evaluate the degree to whichthe process conditions deviate fromthose of the model. When a mismatchoccurs between actual processconditions and those postulated by themodel, a control and operatingstrategy is invoked to correct theprocess conditions. A criticalinterrelationship exists betweenmeasurements and operating/controlstrategy, one that is too oftenneglected. The control strategydepends on what information is or cartbe available, even while it dictateswhich measurements are needed. Thisis perhaps best illustrated in a numberof manufacturing processes in cuttingedge.technologies, where control andoperating strategy is circumscribed bythe lack of appropriate sensors formany critical process variables.Conventional estimation techniquesare used that infer the values ofunmeasured variables from measuredvariables, but these provide imperfectguidance for process control.29

Even something as empirical asprocess measurement cannot bedivorced from the need for good processmodeling. In the absence of a goodmodel, it may not be known whatvariables affect process operation orproduct quality and should thereforebe measured or estimated.Process measurements are subject toerrors. Random (stochastic)disturbances are ubiquitous, and grossor systematic errors can be caused bymalfunctioning sensors orinstruments. The detection andelimination of these errors areessential if the data are to be used forprocess operations and control. Thesuccess of this screening depends onthe measurements themselves, thefailure data available, and the processcontrol strategy. At the present time,diagnostic programs are not applied tomost sensor failure data. Thedetection and remediation ofsignificant errors in measurements forprocess control pose interestingresearch opportunities.Interpretation ofProcess InformationThe quality ofan operation and controlstrategy depends on the quality of themodel on which it is based (Figure 8.6).We are only beginning to understandthis relationship quantitatively, evenin the relatively simple context of thefeedback control of linear systems.Even ifit is assumed that the structureof the process model is correct, we donot yet know how to translateuncertainties in model parametersinto uncertainty in the performance ofthe control system. A more difficultproblem is to assess the effect of anincorrect model structure, such f:lS awrong set of basic equations, on theperformance of the control strategy onwhich it is based. Understanding theeffect of model-process mismatch oncontrol system performance provides acritical research opportunity.In the context of operations andcontrol, simulations can be used to testnew process strategies as well as totrain operating personnel to controlthe process and to respond to.emergencies. The increasing use ofsimulation in process control requiresthat the cost of dynamic simulation bebrought down. This could be done bytaking advantage ofnew developmentsin computers, such as new userinterfaces, computer architectures,and languages, and by developingfaster numerical integrationalgorithms for ordinary differentialequations.Alarm management also requiresresearch. Modern chemical plantsusually have audible and visualannunciators to warn operators whenkey variables deviate from acceptableor safe values. A process upset in aplant that has several interconnectedunits with many feedback controls canset off multiple alarms, and theconsequences of misinterpreting thealarms can range from inefficientprocess operation to outright disaster.When the alarm sounds, the operatormust decide quickly what action totake. A hybridization of expertsystems and process control systemscan assist the operator in interpretingprocess status after an abnormalevent. The need for better handling ofabnormal events makes research inartificial intelligence of greatimportance to the chemical industry.Integration of Process Design withControlMost continuous plants are nowdesigned for steady-state operationwith little regard for the ease (ordifficulty) with which the steady statecan be maintained through control.Such a plant can be difficult to controlonce it deviates from the steady state.Design and control have traditionallybeen treated separately for thefollowing reasons:• The problems in each area aloneare exceedingly complex.• The interactions between design,control, and optimization arepoorly understood.• The computational requirementsofan integrated approach to designand control have been beyond thecapability of available hardwareand software.For example, a chemical plant mightbe designed to achieve high efficiencyby integrating the operation of manyindividual process units across theplant (e.g., by using waste heat fromone unit as an energy source inanother unit). However, the tightcoupling of process units generallymakes the entire plant more difficultto control. Therefore, this is a factorthat must be considered at the designstage. No methodology currentlyexists for including this considerationin plant design; its developmentconstitutes a significant researchopportunity.The supercomputer power that isbecoming available will provide theopportunity to combine process andcontrol system design - includingoptimization - into one large problemthat can be solved in a way thataccounts for their interactions. Thesuccess of such a consolidation willdepend on the development ofapproximate compatible models and oftechniques to relate model quality toperformance.Robust and Adaptive Control.Control systems are designed frommathematical models that aregenerally imperfect descriptions of thereal process. It is essential that controlsystems operate satisfactorily over awide range of process conditions.30

A Revolution In Process ControlUntil recently, the chemical process industries have largely tried to solve difficult control problems by using simulation,case studies, and ad hoc on-line adjustment of controller knobs - a procedure that can require personal intuition andcontinuous human supervision ofthe controls. These industries did not use modern control techniques because availablecontrol theory did not address practical issues. The control theory that was dominant in the 1960s and 1970s assumedthat the designer had a linear mathematical model ofthe process to be controlled as well as a substantial body of fixedknowledge on likely disturbances and the inherent "noise" in the measurements made by process sensors. Such a linearprocess control theory is built on fatal shortcomings: there are no truly linear processes and there are no fixed, knownspectra of information on disturbances and measurement noise. Controllers designed on the basis of linear processtheory will fail in the real world ofnonlinearity and uncertainty.New research advances in control theory that are bringing it closer to practical problems are promising dramatic newdevelopments and attracting widespread industrial interest. One of these advances is the development of "robust"systems. A robust control system is a stable, closed-loop system that can operate successfully even ifthe model on whichit is based does not adequately describe the plant. A second advance is the use ofpowerful semi-empirical formalisms incontrol problems, particularly where the range ofpossible process variables is constrained.The development of "internal model control," a design technique that bridges traditional and robust techniques fordesigning control systems, has provided the framework for unifying and extending these advances. It is now available incommercially available design software.The combination ofthese advances is revolutionizing process control, spawning unprecedented research activity in bothacademia and industry.Thus, the control algorithm mustprovide for control of the process evenwhen the dynamic behavior of theprocess differs significantly from thatpredicted by the model. A controlsystem with this characteristic issometimes called robust. In fact, atraditional disregard for the mode Ierror problem is one of the mainreasons for the frequently cited gapbetween theory and practice in processcontrol. Industry needs algorithmsthat are robust rather than ones that"get that last half percentperformance." Control strategies thatwork all the time within reasonablelimits are better than those that workoptimally some of the time but thatfrequently require reversion tomanual control.Because a process often changes overtime, its model parameters must becontinually updated; in extreme cases,the basic model must be reformulated.An adaptive system is a control systemthat automatically adjusts itscontroller settings or even its structureto accommodate changes in the processor its environment. The problem ofmodel-plant mismatch is of crucialimportance in the design of adaptivecontrollers for processes since it is thatvery mismatch that drives changes inthe controller parameters. Theengineering theory and methodologyfor designing reliable adaptivecontrollers for chemical processes arein the earliest stages ofdevelopment.Finally, there are always processoperations in which neither classicalnor modern control is effective. Suchoperations may require qualitativedecision making or the use of pastknowledge. Artificial intelligencetechniques offer promise for controlsystem design in these cases.Batch Process EngineeringThe production of fine and specialtychemicals, which are usually made bybatch processes, is becomingincreasingly important andcompetitive (Figure 8.7). The efficientoperation of multiproduct andmultipurpose batch plants offers avariety of challenging researchproblems for chemical engineers. Mostindustrial batch chemical operationsare now scheduled by intuitive, ad hocmethods that consist of modestvariations around historical operatingpatterns and that make little or no useof computer technology. It is nowwidely recognized that the schedulingproblems associated with batchprocesses are immensely complex and,in fact, are among the most difficultcombinatorial problems known.Limited progress has been made inusing mathematical models in thesimplest types of batch processscheduling. Current algorithms aretoo computationally demanding andcomplex for industrial use. Animportant intellectual challenge is togenerate a unified field of batchprocess engineering theory and to putit into a practical context by using casestudies.Linear control theory will be of limiteduse for operational transitions fromone batch regime to the next and for31

the control of batch plants. Too manyof the processes are unstable andexhibit nonlinear behavior, such asmultiple steady states or limit cycles.Such problems often arise in the batchproduction ofpolymers. The feasibilityof precisely controlling many batchprocesses will depend on thedevelopment of an appropriatenonlinear control theory with a highlevel ofrobustness.While startup and shutdown occurrelatively infrequently in largecontinuous plants, they are inherent inbatch plant operation. Most startupand shutdown procedures, whetherdevised empirically or theoretically,are designed to follow a recipe ofactions with no feedback. Thus, ifupsets occur, there is often no way tochange the startup or shutdown intime to avoid unwanted processexcursions. Procedures are needed thatincorporate feedback and adaptivetechniques to the problem of plantstartup and shutdown.Process SensorsIf we had a completely accurate modelof a process and accuratemeasurements of process disturbancesat their inception, then correctiveaction could be taken directly withoutthe need to measure the outputstreams from the process after thedisturbance has propagated through it.But because we generally do not haveadequate models, the output streamsof processes must be measured for thepurpose offeedback control.The sensor is the "fingertip" of theprocess control system. The principalchallenge in process sensing is thedevelopment of analytical sensors,particularly for determining processstream composition. Such sensorseliminate the need to withdrawsamples to determine process andproduct parameters, a practice thatshould be minimized because ofinherent problems (e.g., samples ofreactive intermediates may be toxic orotherwise dangerous, or theintervention represented bywithdrawing a sample may affectprocess operation). Since it isimportant for process control not todisturb the normal operation of theprocess, sensors are needed that canoperate in the environment of theprocess stream. The key to meetingthis challenge is a fundamentalunderstanding of the physical andchemical interactions at the sensorenvironmentinterface and, inparticular, the transport and kineticprocesses that occur there.Future Sensor DevelopmentsThe techniques used in the chemicalprocessing of electronic microcircuits(see Chapter 5) are being adapted tothe microfabrication of two- and threedimensionalstructures for solid-statesensors. These techniques will permitthe integration of transducers,optoelectronics, signal-conditioningand data-processing devices, andmicromechanical devices intoextremely small packages. Reducedsize offers advantages in thermaluniformity and response speed; shockand acceleration resistance; andreduced weight, volume, power, andcost.Solid-state sensors may be developedthat will be responsive to a broadrange of acoustic inputs,electromagnetic radiation, ionizingradiation, and electrochemical stimuli.Response elements may be tailored forhigh selectivity among ions, freeradicals, or specific compounds.Alternatively, elements .with lowselectivity are also useful becauseinformation from an array of suchsensors, each with a different butknown broad response, can beprocessed to provide quantitativeanalysis of a complex mixture.Complex mixtures also lendthemselves to chromatographicanalysis. It has been shown that gaschromatographic data can be analyzedon a silicon chip, although with someloss in recognition reliability.Combinations of gas or liquidchromatography or capillaryelectrophoresis of microsamples withmass spectrometry may be developedto provide superior performance."IJ-_[YCOUPLfR•• 0' ..9"f1i'.,I"~ l::SFigure 8.4: Configurations for severaldifferent kinds of optical fiber sensingsystems are shown. The commonfactor in all these systems is the use ofan optical fiber as an integral elementin the system, either to carry light toand from discrete sensors (oftenreferred to as optrodes), or as sensitiveelements themselves (intrinsic fibersensors). Courtesy, AT&T BellLaboratories.The development of biological sensorsis taking place at a rapid pace.Biological sensors analyze chemicalmixtures using biological reagents ofexquisite specificity - for example,enzymes, immunoproteins, monoclonalantibodies, and recombinant nucleicacids. Such sensors may permit theanalysis of fast reactions of species invery dilute media. Multicomponentbiological sensors may be able toperform complex analyses that involvemultiple reactions, with automaticregeneration of the biochemicalreagents or removal of interferingspecies.Unfortunately, current biologicalsensors are extremely delicate. Evenwhen the biological reagents areimmobilized on a solid carrier, suchsensors require careful constructionand frequent recalibration, are notalways amenable to automation orunattended operation, and sometimes32

have inconsistent dynamic responseand limited life. Although biologicalreagents are ideally suited for someapplications, particularly those inrelatively mild environments, theymay not survive the harsherconditions often found in processindustries, Here again,miniaturization of the biologicalsenSor and its direct integration intoan optoelectronic transducer,potentiometric electrode, or membraneare promising approaches. With thecurrent worldwide interest inbiotechnology, major innovations inbiological sensors can be anticipated.Further advances in optoelectronicswill allow the development ofinstrumentation with no electricalcomponents in the sensor. Thesedevices will operate by transmittingprobe light from a remote source to theprocess sensor with an optical fiberlight guide. In the sensor, the lightsignal will be altered by the sensedenvironment (e.g., by absorption ofcertain wavelengths, fluorescence, orscattering) and will thus be "encoded"with information. The encoded signalis transmitted through the opticalfiber to a transducer that produces anelectronic signal. The advantages ofsuch systems include inherent safety,low signal attenuation, and the abilityto multiplex signals in the opticalfiber. Such instrumentation canincorporate additional chemical,biological, and electronic componentsand is likely to playa major role inmany future sensor systems.Future sensors and their associateddata processing elements will needcapabilities beyond those required forthe simple measurement of processvariables, such as periodic selfcalibrationagainst known standards,automatic compensation forenvironmental or other interferences,signal conditioning includinglinearization or other variablecalculation, and fault recognition anddiagnosis. Some of these capabilitiesare now available to a limited extent.Others will become available with thecontinued development of integratedsensors and da ta processinginstrumentation, Arrays of sensorshave already been mentioned inconnection with complex mixtureanalysis, but they may also be used toprovide redundancy, fault detection,and data reconciliation.Research OpportunitiesThe availability of high-quality, real­.time information on the conditions andcomposition of the process stream willpermit engineers to develop acompletely new generation of processcontrol strategies. The physical,chemical, and biological phenomena atthe sensor/process interface must beunderstood and translated into sensortechnology. Chemical engineers arewell positioned to contribute to thedevelopment of improved processsensors in a variety ofways, including• Work in interdisciplinary collaborationswith electronic engineers,biologists, analytical chemists, andothers to elucidate the biological,chemical, and physical interactionsto be measured;• Application of fundamentalprinciples of reaction engineeringand transport phenomena to thedesign ofsensor surfaces;• Development of new processcontrol systems and operationstrategies in response to improvedcapabilities for measurement; and• Determination of the implicationsfor process design of wholly newtypes ofprocess sensors.Process Engineering InformationManagementIn the next decade, competition amongindustrialized countries will beinfluenced by the way in whichinformation and knowledge aremanaged in industry. The challenge isto be the first to find relevantinformation, to recognize the keyelements of that information, and toapply those elements in themanufacture ofdesired products.Computer technology will continue toprovide new generations of hardwareand software for fast informationprocessing and low-cost storage andretrieval. The use of computers forinformation management and decisionmaking will be essential, andadvanced capabilities in userinterfaces and networking will bringnew dimensions to this application.For example, current on-line literaturesearch systems allow data sharingamong many users, significantlyincreasing individual productivity.However, this technology is generallyonly used to manage well-organizeddata; basic research is needed to applyit to engineering data, which are not aswell organized.A process engineer will need to be ableto store and access relevant datarapidly in order to carry out processdevelopment and design in less timeand to solve problems arising from newand complex design requirements (e.g.,designing for multiple objectives ofprofitability, safety, reliability, andcontrollability). To provide for rapiddata storage, access, and transfer, newgenerations of computer hardware(bulk storage, network, and workstations) and software (data bases) willbe needed. Some specific researchchallenges for chemical engineers,working together with computerscientists and information specialists,follow.• Most chemical manufacturingprocesses in the future will bemonitored and control1ed bycomputer. Process data will becollected continuously and storedeither locally or in a central database. Research is needed to developmutually compatible, efficientalgorithms for storing andretrieving process data. Inaddition, computerized procedures33

will be needed for sifting throughvoluminous process data forinformation to use in processimprovement and in thegeneration ofnew processes... Methods must be developed forstoring judgments, assumptions,and logical information used in thedesign and development ofprocesses and models... Procedures and methods will beneeded for retrieving andoperating on other types ofimprecise data... Efficient transfer of informationamong engineers and designerswill depend on how easily thesedata can be accessed. The specialneeds ofchemical engineers in thisarea - the particular ways inwhich they generate, manage, anduse information - merit study.Implications ofResearch FrontiersThe speed and capability of the moderncomputer, as well as the developingsophistication ofchemical engineeringdesign and process control tools, havetremendous implications for thepractice of chemical engineering.Chemical engineers of the future willconceptualize and solve problems inentirely new ways. There are twohottlenecks to the application of thesepowerful resources, though. First,there are not enough active researchgroups at the frontiers of computerassistedprocess design and control. Alarger effort is needed in order for thefield to keep pace with the expandingpower of availahle computers. Second,many chemical engineeringdepartments lack the computationalresources needed to fully integrateadvances in design and control intotheir curricula. For the full potentialof the computer to be realized inimproved design of chemical productsand the improved design and operationof processes to produce them, chemicalengineers must be broadly versed inadvanced computer technology. Thiscan only happen if they have access tostate-of-the-art computational toolsthroughout their educational careers,not in an isolated course or two.Making this broad access andutilization of the computer ineducation possible will requiresubstantial government, academic,and industrial funding to provide bothhardware and software. In some cases,groups may need remote access tonetworks of supercomputers. In othercases, dedicated array processors andother computational hardware may berequired in the chemical engineeringdepartment. If this country is tomaintain a leadership role in chemicaltechnology, critical needs for bothresearch support and facilitiesacquisition must be addressed. Thestatus of funding in this fie ld and aspecific initiative to achieve the goalsoutlined above are discussed inChapter 10 and Appendix A.*****************************Thanks from Washingtonby Henry McGee, Washington DCAlChEMeeting Program ChairmanWith many fine sessions from the CASTDivision, the Washington DC meetingwas a great Success. Meetingattendance was among the top two orthree in all of AIChE history. Themeeting had a number of innovationsin both technical content and inorganization. Although most of thistechnical content came from theestablished groups and Divisions inthe usual way, some special speakersand sessions either originated from theMeeting Program Chairman and hisCommittee, or else were midwifed bythe MPC. One of these latter types ofsessions was on Global ClimateChange. Interestingly, this sessionwas broadcast live by the Voice ofAmerica. This particular example ofpublic attention was unique at ourWashington affair and is certainly notcommon in AIChE history.The Supersessions were conceived asteaching exercises wherein new andexciting avenues for the potentialexercise of chemical engineeringexpertise might be presented to us.The speakers were, in general, notbaptized chemical engineers, and theformat for their presentations wasmeant to be attractive and compelling.The Biotech Supersession wascertainly one of the most compellingand best attended of these; it attractedan audience of perhaps 700 to 800people. In this session, we learnedwhat chemical engineers might dowith unusual marine organismscollected from Australia's GreatBarrier Reef. And we learned thatrecombinant DNA has a proper placein chemical engineering departments.All of this is definitely not dull.Perhaps CAST will want to state sucha Supersession at a forthcomingmeeting. I hope so. In any event,thanks again for your sessions, whichcontrihuted so much to the success ofthe Washington DC meeting.34

The AIChE ManagementDivisionby M. Tayyabkhan, Chairman,ManagementDivisionGreetings to the members of CASTDivision. I welcome this opportunityto tell you a little bit about the boldplans and valiant attempts in theManagement Division that we hopewill turn into an exciting program formembers ofour division. It is a specialpriviIe;;e to talk to you, since, I havebeen a member of the CAST divisionever since it was formed.The Management Division is youngerand smaller than CAST; it was formedin 1975 and has about 800 members.Our members are predominantlyaffiliated with industry. Jim Mathisrecently carried out a very interestingsurvey and produced a very clearpicture of the profile and the interestsof our members. Ifany ofyou would beinterested in such information, pleasecontact me at (609) 924-9174.As a Division, we are concerned withthree levels of management, whichare:(1) Top management(2) Middle management(3) Professionals, including recentlypromoted first-level supervisors,who aspire to be in managementWe have members from each of theselevels. We also have contacts withindividuals in each of these levels whoare not members ofAIChE.Top management and our contactswith them are, by and large, resourcesfor us and for AIChE. Periodically ourDivision organizes, in closecooperation with the ExecutiveDirector of AIChE, special one-daymeetings where the top managementfrom industry discusses with theExecutive committee of AIChE issues ofinterest to chemical engineeringprofessionals. In the recent past thesediscussions have included subjectssuch as supply and demand ofchemicalengineers, impact of corporate takeovers,opportunities for chemicalengineers in non~conventionalindustries, and so forth. In thiscontext we are always looking forpersons with ideas and contacts. Ifyouhave any, please come forward.Middle management from industrydoes not take as much interest in theinstitute as we would like them to.Many of them may have felt that theirneeds were not met by the activities ofAIChE and have directed theirattention elsewhere. We believe thatwe need to develop mechanisms thatwould get them involved with us.To this end, we plan to help localsections of AIChE organize speciallunches, say two times a year, formiddle management in the chemicalprocess industry in several cities of thecountry. We hope to assign a memberof the Division to be a helper to thelocal section in that city. The luncheswould be attended by the invitedpersons in middle management,officers ofthe local section, and severalmembers from the ManagementDivision who live in the city. Theselunches would offer individuals in themiddle management of one companyan opportunity to meet individualsfrom other companies in similarpositions. This may prove to be anenticement for middle managers toattend.In an attempt to meet the needs of thenewly appointed managers andaspiring managers, we areencouraging members of our Divisionto make presentations on managementskills at local sections. We plan toprovide a few speakers withinteresting talks on managementsubjects to the Speakers Bureau that isrun by AIChE. At the same time, wehope to develop programs at nationalmeetings that deal with first-levelmanagement skills and withorganizational and behavioralmatters.Let me now turn my attention to ourprogramming activities. The CASTDivision is extremely strong in thisarea, and we can learn from you. Mostofour programming committees wouldbenefit from joint programming withother divisions, because there isnatural overlap in some areas ofinterest. I invite the CAST Division toconsider joint programming with us.For your information, I am listing ourprogramming committees and theirchairmen. Please feel free to contactthem ifyou have suggestions.Chairman of area 5 (ManagementDiv): Al Wechsler (617) 864-57705(a) Management of ResearchChmn: Tom Hanley(904) 487-61445(b) Project ManagementChmn: Hebab Quazi(818) 300-68985(c)Management Sciences:Irene Farkas-Conn(312) 842-63885(d) Plant and ManuIacturingManagement:Bob Gilbert(713) 474-85005(e) Corporate ManagementChmn: Jim Mathis(201) 522-87645(f)Environmental and Legalno chairman5(g) Personnel Management:Marvin Livingood(502) 459-888850) Private Practice and ConsultingChmn: John Grant5(j)Technology TransferChmn: Mike Tayyabkhan(609) 924-91745(k) Project Analysis and Economics:Jim Weaver(813) 378-128735

Several of us in the ManagementDivision, specially on the board andthe executive committee, are quiteactive in use of computing andelectronic mail. In this area, I believe,our divisions have an opportunity towork with each other. One of thechallenges we face is that the academicmembers of AIChE have access to anduse BITNET, whereas the industrialmembers use one of two or threepopular commercial systems. We musthave good communications betweenour industrial and academic membersand bridge the gap between thedifferent e-mail systems in an efficientand cost effective manner. Peter Ronyand I are planning to experiment withsolutions to this problem and wewelcome suggestions and otherparticipants.In closing, I want to thank you andyour forever experimenting editor forletting me address you. Please contactme with your suggestions andquestions (Telephone (609) 924-9174;Telex 4933567 MTTUI; MClmail ID348-9468; USmail 62 Erdman Ave.,Princeton NJ 08540), or better still,join our Division and show up at OUfmeetings and sessions.A Commitment to theBetterment ofthe ProcessIndustriesby Vincent S. Verneuil, Jr.Simulation Sciences Inc. is proud tosponsor the Computing in ChemicalEngineering Award given annually bythe Computing and SystemsTechnology Division. Sponsorship ofthis award, which gives recognition foroutstanding contribution in,.theapplications of computer and systemstechnology, is one important way thatSimSci· lives up to its MissionStatement commitment to "contribute to the overall bettermentand productivity of the processindustries."Last year's recipient, Professor JamesM. Douglas, exemplifies how oneindividual can make an outstandingcontribution to our industry throughcreative, innovative thinking. Hissystems approach to process designprovides us with new insight andmethodology for creating andevaluating process alternatives. Wehope that the recognition givenProfessor Douglas will not onlyencourage him to continue his work,but will inspire the rest of us to tryharder to make our industry a littlebetter.We are indeed fortunate to participatein the dynamic changes taking place inour industry at this time. With theadvent of faster and more economicalcomputers, distributed processing, andnew methods, data and expert systemsfor designing and optimizing processplant performance, our ability todevelop more powerful, yet easier touse, computer software and put it inthe hands of the practicing engineerhas never been greater.Over the years, SimSci's commitmentto the betterment of the processindustries has included grants toselected universities for research, andthe free use ofour PROCESS· simulator.Today, approximately 200 universitiesworldwide use PROCESS as a teachingtool in their undergraduate designcourses. We believe that studentexposure to commercial simulationtools within the context of theirthermo, design and optimizationcourses is excellent preparation fortheir industry careers.During SimSci's 22-year history, wehave continuously developed andexpanded our simulation capability inresponse to advances in technologyand the needs of our customers. Eachyear we have added new capabilitiesand released new, improved versions ofour programs. And, as needs change,we have developed entirely newgenerations of our programs to keepcurrent with new technologies.With the introduction this year ofPRO/ll·, our fourth generationsimulator, the convenience,accessibility and ease of use of oursimulator has been greatly increased.PRO/ll input/output graphics andflowsheet plots, along with casecompare features, aid productivity andflowsheet understanding. Built on anew structure, database and graphicsenvironment, it is designed to operateefficiently in today's computingenvironment, and to provide aplatform for an integrated processengineering system.Simulation Sciences Inc. is proud to bea part of the process industries and tomake our small contribution to theadvancement of computerized processengineering. There are manyoutstanding contributions made to ourindustry daily that go with little or norecognition outside of a small localaudience. We are happy to sponsor oneaward, and hope that it helps all of usrenew our commitment to thebetterment ofour industry.SimSci is a service mark of SimulationSciences Inc. PROCESS and PRO/ll aretrademarks of Simulation SciencesInc.Meetings and ConferencesThe following items summarizeinformation in the hands of the Editorby January 26, 1989. From this pointforward, the deadlines for the twoissues of CAST Communications ­called the Winter and Summer issueswillbe approximately several weeksafter the Fall and Spring AIChEmeetings, respectively. These reviseddeadlines will give CAST divisionmembers who are active in CASTprogramming activities sufficient timeafter AIChE meetings to send lastminuteinformation to both thePublications Board Chairman (JeffSiirola) and the Editor of thenewsletter (Peter Rony). We preferthat all communications with us be36

done in electronic form, either with MSDOS formatted diskettes or else withmessages sent electronically overBITNET. The most up-to-date listing ofproposed sessions and Calls for Paperswill be maintained electronically bythe Publications Board Chairman.To repeat comments made in the"About This Issue" editorial: "Thisissue will be one of three to bepublished this year: Volume 12, No.1,the Fall 1988 issue, delayed toaccommodate the Calls for Papers forthe 1989 San Francisco AIChE meeting;Volume 12, No.2, also known as the"Winter 1989" issue, a short issuecontaining Part 2 of the Frontiers inChemical Engineering: ResearchNeeds and Opportunities article,meeting announcements, and Calls forPapers, and delayed several monthsfrom the normal publication schedulefor the "Winter" issue: and Volume 12,No.3, also known as the "Summer1989" issue, which will be published ontime in August 1989 and will containthe Award Address (including copies ofhis illustrations) by Professor J. D.Seader, "Computers and ChemicalEngineering Education," at the CASTDivision banquet at the WashingtonD.C. AIChE meeting. This revisedschedule has been developed with thecooperation of both the Publicationsand the Programming Boardchairmen. By the end of the summer,we hope to have all of the CASTprogramming area chairmen linkedelectronically by BITNET. A listing oftentative programs, first calls, finalcalls, and a program schedule for thenext meeting will be updated everyseveral months and made available toall CAST Division members (who haveaccess to BITNET) on the LSUCHE CRANDfile server at the Department ofChemical Engineering, LouisianaState University. We are still hopingthat AIChE headquarters in New Yorkwill be able to gain access to a localBITNET node.Please send CAST Division sessioninformation, Calls for Papers, meeting,and short course announcements to meimmediately by February 1, 1989 forinclusion in the HWinter 1989" issue ofCAST Communications.Peter R. Rony,Editor, CAST CommunicationsHouston AIChE Meeting(April 2-6. 1989)The programming booklet for the 1989Spring National Meeting andPetrochemical Expo '89 at the GeorgeR. Brown Convention Center inHouston, Texas has already beenreceived by all CAST Divisionmembers.Area lOa Sessions1. Applications of ExpertSystems in Process Engineering.Babu Joseph (Chairman), Department ofChemical Engineering, Washington University,St. Louis, MO 63130, (314)889-6076 and HeinzA. Preisig (Vice Chairman), Department ofChemical Engineering, Texas AM University,College Station,TX 77843-3122,(409)845-0386.2. Computer-Aided ProcessModeling and Simulation, Lorenz T.Biegler (Chairman), Department of ChemicalEngineering, Carnegie Mellon University,Pittsburgh, PA 15213, (412)268-2232 and RalphW. Pike (Vice Chairman), Department ofChemical Engineering, Louisiana StateUniversity. Baton Rouge, LA 70803, (504)388­6910.3. Bioprocess Design andSimulation: Issues and Tools.Michael M. Domach (Chairmanl, Department ofChemical Engineering, Carnegie MellonUniversity, Pittsburgh, PA 15213, {412l268­2246 and Randy Field (Vice Chairman), AspenTechnology, Inc., 251 Vassar Street, Cambridge,MA02139,(617)497-901 O.4-5. Retrofit Design Techniqueand Applications I and II, RajeevGautam (Co-Chairmanl, Union CarbideCorporation, P.O. Box 8361, South Charleston,WV 25303, {304l747-3710 and H. Dennis Spriggs(Co-Chairman), Linnhoff March Inc., P.O. Box2306, Leesburg, VA 22075, (703)777-1118_For further details concerning ArealOa sessions and scheduling, pleasecontact Michael F. Doherty (Area lOaChairman), Department of ChemicalEngineering, U niversity ofMassachusetts, Amherst, MA 01003,(413)545-2359.Area lOb Sessions1. Expert Systems in ProcessControl2. ControlOperationsofArea 10c SessionsSeparation1. Innovative Uses ofSpreadsheets in EngineeringCalculations I and II. Kris Kaushik (Co­Chairman), Shell Oil Company, P.O,Box 2099,Houston, TX 77252-2099, (713) 241-2098 andKeshava Halemane (Co-Chairman), Universityof Maryland, Department of ChemicalEngineering, College Park, MD 20742, (30l)454-5098.2. Plant-Wide Use of Computersin Process Operations. Ed Rosen (Co­Chairman), Monsanto Co. F2WK, 800 N.Lindbergh, St. Louis, MO 63167, (314) 694-6412and Irven Rinard (Co-Chairman), City College ofNew York, 138th St, and Convert Ave., NewYork, NY 10031,(212)690-4135_3. Personal Computers inPlanning and Scheduling. MikeTayyabkhan (Co·Chairman), TayyabkhanConsultants, 62 Erdman Avenue, Princeton, NY,08540 and Ed Bodington (Co-Chairman),Chesapeake Decision Science, P.O, Box 275, SanAnselmo, CA 94960.4. On-Line Fault Administration.Mark Kramer (Co-Chairman), Dept, of ChemicalEng" Room 66-542, Massachusetts Inst. ofTech.,Cambridge, MA 02139, (617) 253-6508 and V.Venkatsubramanian (Co-Chairman), Chem.Eng. & Applied Chemistry, ColumbiaUniversity, New York, NY 20027, (212) 280­2561.37

For further details concerning Area10c sessions and scheduling, pleasecontact Ignacio Grossmann,Department of Chemical Engineering,Carnegie Mellon University,Pittsburgh, PA 15213, (412) 268-2228.European Symposium onComputer Applications inChemical Industry,Erlangen, Federal RepublicofGermany(April 23-26, 1989)The conference topic will be subdividedinto four subject groups:• Expert Systems and Data BankManagement Systems• Computer Integrated Production• Process Synthesis and Design• New Developments in ComputingThe symposium subjects will betreated in plenary papers, oral (about40), and poster contributions, plus aroundtable discussion. About 30minutes, including discussion, will beavailable for every oral contribution,while the time foreseen for the displayof posters will be one day. The finaldecision on oral or poster presentationswill reside with the ScientificSymposium Committee.All manuscripts will be printed andpublished in one volume and beavailable for distribution to allsymposium participants at thebeginning of the meeting. A number offacilities will be available at thesymposium for the demonstration ofprograms and data banks. Interestedpersons are invited to inform theorganizers about their participation byOctober 1988. Further details will beworked out on the basis of requestsreceived and will be communicated atthe beginning of 1989. The symposiumlanguage will be English.The symposium is organized byDECHEMA Deutsche Gesellschaft furChemisches Apparatewesen,Chemische Technik undBiotechnologie e.V., Frankfurt amMain in collaboration with members ofthe Dechema-FachausschussAnwendung elektronischerRechengerate in der ChemischenTechnik, and members on the WorkingPart on use of Computers in ChemicalEngineering of the EuropeanFederation ofChemical Engineering.Correspondence and Secretariat:DECHEMA, Attention Mrs. L. Schubel,P. O. Box 97 01 46, D-6000 Frankfurtam Main 97, West Germany. Phone(069) 75 64-235/209.Short CourseAdvanced Process Control,McMaster University(May 15·19, 1989)An intensive short course on DigitalComputer. Techniques for processidentification and control. This shortcourse is intended for persons whohave some background in the basicelements of process control and whoare interested in learning aboutprocess identification and moreadvanced digital control methods, or inupdating their knowledge in theseareas. The course is built around aseries of identification and controllerdesign workshops, using a userfriendly,interactive, graphics-basedsoftware package running on VAX's orIBM PC's. For details, contact Dr. P. A.Taylor, Department of ChemicalEngineering, McMaster University,Hamilton, Ontario, Canada, L8S 4L7(416) 525-9140, ext. 4952. FAX (416)522-0058.Short CourseComputer Methods forProcess Design andOptimization, EngineeringDesign Research Center,Carnegie Mellon University,Pittsburgh(May 22-26, 1989)This course deals with recentdevelopments in the areas of processsynthesis, retrofit design, operabilityand batch processing using computermethods based on nonlinear andmixed-integer programming andexpert systems. The format of thecourse includes lectures, sampleproblems, demonstrations and handsonworkshops. The instructors of thecourse are Professors Larry Biegler,Ignacio Grossmann and ArthurWesterberg. For information pleasecall (412)268-2207.1989 American ControlConference, The PittsburghHilton and Towers(June 21-23,1989)The American Automatic ControlCouncil will hold the eighth AmericanControl Conference IACC) on June 15­17,1988 at the Pittsburgh Hilton andTowers, Pittsburgh, Pennsylvania.The conference will bring togetherpeople working in the fields of control,automation, and related areas fromthe American Institute of Aeronauticsand Astronautics IAIAA), AmericanInstitute of Chemical Engineers(AIChE), American Society ofMechanical Engineers IASME),Association of Iron and SteelEngineers IAISEl, Institute ofElectricaland Electronic Engineers (IEEE),Instrument Society of America lISA),and the Society of ComputerSimulation (SCS).Both contributed and invited papersare included in the program. The ACCwill cover a range of topics relevant totheory and practical implementation38

of control and industrial automationand to university education incontrols. Topics ofinterest include butare not limited to linear and nonlinearsystems, identification and estimation,signal processing, multivariablesystems, large scale systems, roboticsand manufacturing systems, guidanceand control, sensors, simulation,adaptive control, optimal control,expert systems, and controlapplications.The organizing committee intends toarrange workshops to be held inconjunction with the 1989 ACC.Interested individuals should contactthe Special Events Chairman, MichaelK. Masten, (214) 343-7695, or theGeneral Chairman.(NOTE: Though the October 14, 1988deadline has passed, the followinginformation should be of interest tothose individuals who wish to attendthe ACC.)CAST Area lOb is planning to sponsoror cosponsor a number of invitedsessions at the 1989 American ControlConference. Chairs for theseprospective sessions have beenappointed and are now activelyorganizing them. A list of sessiontitles follows. If you are interested inpresenting a paper in an invitedsession, you should communicate withthe appropriate chair or co-chairdirectly. One page abstracts of allprospective papers must be availableto session organizers by October 14.Proposed AIChE Invited Sessions:1. Advances in Process Control(possibly two sessions)Evangelos ZafiriouDepartment ofChern. & Nuclear Eng.University ofMarylandCollege Park, MD 20742(301) 454-5098Jorge MandlerAir Products & ChemicalsBox 538Allentown, PA 18105(215) 481-34132. Robust ControlYamanArkunSchool ofChemical EngineeringGeorgia inst. ofTechnologyAtlanta, GA 30332-0100(404) 894-2865Manfred MorariDepartment ofChemical EngineeringCalifornia lnst. ofTechnologyPasadena, CA 91125(818) 356-41863. Control of DistributedParameter SystemsVasilios ManousiouthakisDepartment ofChemical EngineeringUCLALos Angeles, CA 90024(213) 825-93854. Applications of ArtificialIntelligence to Process ControlAli CinarDepartment ofChemical Eng.Illinois Inst. ofTechnologyChicago, IL 60616(312) 567-3042Bradley HoltDepartment ofChemical Eng.University ofWashingtonSeattle, WA 98195(206) 543-05545. Control of Chemical ReactorsChristos GeorgakisPMC Research CenterLehigh UniversityBethlehem, PA 18015(215) 758-47816. Decentralized ControlDominique BonvinTechnisch-Chemisches-LaborETH-ZentrumCH-8092 Zurich/Switzerland0112563113YamanArkunSchool ofChemical EngineeringGeorgia Inst. ofTechnologyAtlanta, GA 30332-0100(404) 894-28657. Recent Advances in AdaptiveProcess ControlSpyros SvoronosDepartment ofChemical EngineeringUniversity ofFloridaGainesville, FL 32611(904) 392-9101Duncan MellichampDept ofChemical & Nuclear Eng.University ofCaliforniaSanta Barbara, CA 93106(805) 961-28218. Advanced Distillation ColumnControl (Jointly Sponsored wi ISA)Sten Bay JorgensenDepartment ofChemical EngineeringTechnical University ofDenmarkDK 2800 Lyngby, Denmark(02) 88 32 88Tom McAvoyDepartment of Chemical & NuclearEng.University of MarylandCollege Park, MD 20742(301) 454-45939. Control of BiochemicalProcesses (Joint wi ISA)Satish ParulekarDept. ofChemical EngineeringIllinois Institute ofTechnologyChicago, IL 60616(312) 567-3044Karen McDonaldDept. ofChemical EngineeringUniversity ofCaliforniaDavis, CA 95616(916) 752-040039

fl··10. Control of Metals Processing(Jointly Sponsored w/ AISE)William TomcaninAlcoa LaboratoriesAluminum Company ofAmericaAlcoa Center, PA 15069(412) 337-2942For further information, pleasecontact:Professor Duncan Mellichamp (AIChESociety Review Chairman),Department of Chemical Engineering,University of California, SantaBarbara, CA 93106, (805) 961-3411;Professor Marija Ilic (ProgramChairman), LEES, Bldg. 10-059,Massachusetts Institute ofTechnology, Cambridge,Massachusetts 02139, (617) 253-4682;Professor H. Vincent Poor (GeneralChairman), Coordinated ScienceLaboratory, University of Illinois atUrbana-Champaign, 1101 WestSpringfield Avenue, Urbana, Il 61801,(217) 333-6449.Foundations ofComputer­Aided Process Design(FOCAPD-89) Snowmass, CO(July 9-14, 1989)Jeffrey J. Siirola (Chairman),Eastman Kodak Company, P.O. Box1972, Kingsport, TN 37662, (615) 229­3069 and Ignacio E. Grossmann (Co­Vice Chairman), Department ofChemical Engineering, CarnegieMellon University, Pittsburgh, PA15213, (412) 268- 2228.The following is the tentative programfor the meeting.Nine sessions are planned. Conferencethemes include: Design Theory andMethodology, Artificial Intelligence,New Design Environments, ProcessSynthesis, Mathematical Techniques,Process Simulation and Analysis,Applications of Supercomputers,Chemical Product Design, and FutureOutlook. See the specialannouncement elsewhere in this issue.Session 1:Keynote AddressCritical Issues in Design Theoryand Methodology. Michael J.Wozny, Rensselear.Session 2:Artificial Intelligence in Design.Chairman: John C. Hale, DuPont.Artificial Intelligence andSymbolic Computing in ProcessEngineering Design. GeorgeStephanopoulos, MIT.Computer-Aided Process DesignSystems with Knowledge Base:Present and Future. KiyoyukiSuzuki, Intelligent Technology Inc.A Blackboard-based Architecturefor VLSI CAD Tool Integration.Stephen W. Director, Carnegie Mellon.Session 3:Approaches to Chemical ProcessSynthesis. Chairman: David W. T.Rippin, ETH.A Hierarchical Decision Procedurefor the Conceptual Design ofChemical Processes. James M.Douglas, University of Massachusetts.The Impact of MINLP Algorithms inProcess Synthesis. Ignacio E.Grossmann, Carnegie Mellon.Session 4:Systems Analysis Tools forChemical Process Design.Chairman: Henry M. Gerhardt,Amoco Chemical.Nonlinear Systems Analysis inProcess Design. Warren D. Seider,University of Pennsylvania.Strategies for SimultaneousSolution and Optimization ofDifferential-Algebraic Systems.Lorenz T. Biegler, Carnegie Mellon.Recent Developments in Methodsfor Finding All Solutions toGeneral Systems of NonlinearEquations. J. D. Seader, UniversityofUtah.Session 5:Additional Chemical ProcessDesign Issues. Chairman: H. DennisSpriggs, Linnhoff-MarchRecent Developmen ts inSystematic Methods for RetrofitProcess Design. Truls Gundersen,Norsk Hydro.Progress a.nd Issues in Computer­Aided Batch Design. G. V. Reklaitis,Purdue.Session 6:Process Design Environments.Chairman: Edward M. Rosen,Monsanto.A State-of-the-Art ProcessSynthesis and AnalysisEnvironment. Don Vredeveld, UnionCarbide.Integrated Process/Plant Design.Malcolm L. Preston, ICI.Application of AdvancedArchitectures to PracticalChemical Engineering DesignProblems. Gregory J. McRae,Carnegie Mellon.Session 7:Product Design. Chairman: JosephD. Wright, Xerox Research Centre ofCanada.Designing Molecules PossessingDesired Physical Property Values.Kevin G. Joback, MIT.40

Molecular Modeling for the Designof Pharmaceuticals. RobertLangridge, University of California ­San Francisco.Computing in CAR Product Design.Kenneth H. Huebner, Ford.Session 8:New Challenges in Process Design.Chairman; John D. Perkins, ImperialCollege.Robust Solution of FlowsheetingEquation Systems. Ross E. Swaney,University ofWisconsin.Design of Multipurpose BatchPlants with Multiple ProductionRoutes. Iftekhar A. Karimi,Northwestern.Systematic Synthesis of SeparationScbemes for Azeotropic Mixtures.Michael F. Doherty, University ofMassachusetts.Simultaneous Process Synthesisand Control of Chemical Systems:Disturbance Rejection.Christodoulos A. Floudas, Princeton.Design and Synthesis Methods forPolymer Processes. Michael F.Malone, University ofMassachusetts.Computer-Aided Analysis andDesign in Polymer Processing.Andrew N. Hrymak, McMasterUniversity.Session 9:The Future of Product and ProcessDesign. Chairman; Jeffrey J. Siirola,Eastman Kodak.Future Directions in Design andDesign Environments. Arthur W.Westerberg, Carnegie Mellon.Future Demands in ChemicalProduct and Process Engineering.L. E. Scriven, University ofMinnesota.Process Design - What Next? RogerW. H. Sargent, Imperial College.Philadelphia AIChE Meeting(August 20-23, 1989)1989 InternationalConference on ParallelProcessing, PennsylvaniaState University(August 8·12, 1989)Pakers at this conference will describerecent advances on all aspects ofparallel/distributed processing. Thesemay include parallel/distributed logiccircuits; impact of VLSI to parallelprocessor architecture; variousconcurrent-, parallel-, paper-, line-, ormultiple-processor architectures;processor~memoryinterconnections;computer networks; distributeddatabases; reliability and faulttolerance; modeling and simulationtechniq ues; performancemeasurements; operating systems;languages; compilers; algorithms; orvarious application studies.The deadline for submitting papers ishas passed, but CAST division membersmay be interested in attending theconference. For further details,contact; Dr. Peter M. Kogge, Mail Stop0302, IBM Corporation, Route 17C,Owego, NY 13827, (607) 751- 2291.Please limit the length of your paper toabout 20 double· spaced typed pages.Computer IntegratedProcess Engineering(CIPE '89), Leeds, England(September 25·28, 1989)A major aim of the CIPE '89 conferenceis to review the field of advancedcomputer based technologies, withemphasis on integration, and toexamine the probiems of bringingtogether well-established applicationspackages to create the integratedprocess systems design environment.A second aim is to explore newerdevelopments in hardware and'software that specifically addressissues relating to integrated design.This call for papers invitescontributions in the three linked fieldsof enabling technology, integration,and applications;Enabling Technologies• Mathematical and numericalmethods• Software engineering• Graphics• Databases• Parallel processing• Hardware architectures• Man-machine interfacesIntegration• CAD/CAE integration• Management information systems• Integration ofdesign/control/operation• Computer-aided projectmanagement• Decision support systems• Computer integrated manufacture(CIM)Applications• Plant·wide systems• Discrete/continuous systems• Real-time applications• Flexible production systems• Operator training41

Papers will be presented in sessionsopened by a keynote speakerconducted by a chairman/rapporteur.It is intended that discussion of thepapers will be an integral element ofthe meeting, and adequate time forthis will be included in the program.All papers will be refereed.Call for Papers - Timetable forAuthorsOctober I, 1988 Submission of 500­word abstract in EnglishDecember I, 1988 Notification toauthors ofacceptance ofabstractMarch I, 1989for refereeingJuly I, 1989paperSubmission of paperFinal submission ofPreprints of all papers will bedistributed to conference participantsapproximately two weeks before thecongress. Final proceedings, includingupdated papers, comments of sessionchairmen, and summaries ofdiscussions will be published in theIChemE's Symposium Series and willbe mailed to participants one monthafter the conference.The conference is aimed at industrialand academic engineers and scientistsengaged in computer-aided design,production, and development. Theconference organization reserves theoption to limit the number ofparticipants to 180 on a first-come,first-served basis. Early registration istherefore recommended. A few placeswill be reserved for junior researchersat a reduced registration fee.Please direct correspondence to: CIPE'89, Attn. Prof. C. McGreavy, TheUniversity of Leeds, Leeds LS2 9JT,England. Telephone: (0532) 332401.Telex: 556473 UNILDS G. Fax: 0532332032.San Francisco AIChE Meeting(November 5-10, 1989)See the Calls-for-Papers section of thisnewsletter. Note that the Calls are notcomplete. If you wish to present apaper in any of the sessions for which aCalls for Papers does not appear,please contact the session chairman.For further details, see "About ThisIssue."IFAC World Congress,Tallinn(1990)Sub IPC4: Control of ChemicalProcesses and Processes for NaturalProducts Like Food, Wood, andAgricultureChairman: Mogens KummelGiven below, with organizers listed inparentheses, are the planned sessions.Full papers need to be submitted byJune 15, 1989. Interested authorsshould contact Professor Thomas J.McAvoy, University of Maryland,Department of Chemical and NuclearEngineering, College Park, Maryland20742-2111, (301) 454-2431.Planned Sessions (first organizer is thesession chairman):1. Expert Control of ChemicalProcesses (McAvoy and Koivo)2. Adaptive Control of ChemicalProcesses (Wittenmark andBachmann)3. Robust Control of ChemicalProcesses (Kantor and Skogestad)4. Fault Detection and Safety(Bachmann and Takamatsu)5. Batch Process Control (Rippin andNajim)6. Plant-wide Production Control(Uronen and Pyzik)7. Statistical Process Control(MacGregor and Morris)8. Biotechnological Process Control(Halme and Staniskis)9. Automatic Control Applications inAgriculture (Hashimoto)New Orleans AIChEMeeting(March 18-22, 1990)Prospective session participants areencouraged to observe the followingdeadlines:September I, 1989: Submit anabstract of the proposed presentationto the session chairman.October 1, 1989: Authors informed ofselection and session content finalized.January I, 1990: Submit an extendedabstract to be published fordistribution at the meeting.February I, 1990: Final manuscriptsubmitted to the session chairman.Authors are reminded that undercurrent AIChE meeting policy, themeeting booklet will contain onlytitles of the papers presented.However, a book of extended abstractsis distributed to attendees at themeeting. Moreover, authors maybring hardcopies of their papers fordistribution at their session, andhardcopies or microfiche may beordered at or after the meeting.Area lOA:DesignSystems and Process1. Artificial IntelligenceApplications in Process andProduct Design. Babu Joseph(Chairman), Department of ChemicalEngineering, Washington University,St. Louis, MO 63130, (314) 889-6076.2. Effective Platforms for UserInterfaces. Mohinder K. Sood(Chairman), Mobil R&D Corporation,PO Box 1026, Princeton, NJ 08540,(609) 737-4960.42

3. Design for Waste Accountability.Chairman to be confirmed. ContactMichael F. Doherty, Department ofChemical Engineering, University ofMassachusetts, Amherst, MA 01003(413) 545-2359.4. Process Design and Analysis.Peter Douglas (Chairman),Department of Chemical Engineering,University of Waterloo, Waterloo,Ontario, N2L 3G1, Canada, (519) 885­2913.Joint Areas lOA and 10C Sessions1-2. Hazard and OperabilityAnalysis for Process Safety I and II(Joint with Area 10C). VenkatVenkatasubramanian (Chairman),School of Chemicai Engineering,Purdue University, West Lafayette, IN47907, (317) 494-4050.For further information detailsconcerning Area lOA sessions andscheduling, please contact Michael F.Doherty (Area lOA Chairman),Department of Chemical Engineering,University of Massachusetts,Amherst, MA 01003, (413) 545-2359.Area lOB:ControlSystems and Process1. Control of PolymerizationReactors. W. David Smith(Chairman), Polymer ProductsDepartment, E.!. DuPont de Nemours& Company, PO Box 80262,Wilmington, DE 19880-0262, (302)695-1476.2. Industrial Applications ofProcess Control. Jorge A. Mandier(Chairman), Research andEngineering Systems - MIS, AirProducts and Chemicals, Inc.,Allentown, PA 18195, (215) 481-3413.For further information detailsconcerning Area lOB sessions andscheduling, please contact Duncan A.Mellichamp (Area lOB Chairman),Department of Chemical and NudearEngineering, University of California,Santa Barbara, CA 93106, (805) 961­2821.Area 10C:OperationsProcessingComputers inand Information1. Computer Aided Engineering. S.Ganguly (Chairman)2-3. Application of Expert Systemsin Process Operations I and II.Peter Clark (Chairman), School ofChemical Engineering, CornellUniversity, Ithaca, NY 14853, (607)255-8656 and Gary D. Cera (ViceChairman), Mobil Research andDevelopment Corporation, PO Box1026, Princeton, NJ 08540, (609) 737­5299.4. Management and Reconciliationof Plant Data. Mohinder K. Sood(Chairman), Mobil R&D Corporation,PO Box 1026, Princeton, NJ 08540,(609) 737-4960 and A. L. Parker (ViceChairman), Shell Oil Company, POBox 10, Norco, LA 70079, (504) 465­7459.5. Computer Networks. BriceCarnahan (Chairman), Department ofChemical Engineering, University ofMichigan, Ann Arbor, MI 48109, (313)764-3366 and Norman E. Rawson(Vice Chairman), IBM Corporation ­D051, 6901 Rockledge Drive,Bethesda, MD 20817, (301) 571-4445.6. Simulation for ProcessOperations. Heinz A. Preisig(Chairman), Department of ChemicalEngineering, Texas A&M University,College Station, TX 77843-3122, (409)845-0386 and Ravi Nath (ViceChairman), Union CarbideCorporation, 11111 Katy Freeway,Houston, TX 77079, (713) 973-5609.For further information detailsconcerning Area 10C sessions andscheduling, please contact RajeevGautam (Area 10C Chairman), UOPMolecular Seive Department,Tarrytown Technical Center,Tarrytown, NY 10591, (914) 789-3206.Area lOD: Applied Mathematicsand Numerical AnalysisNo sessions are plannedFor further information detailsconcerning Area 10D sessions andscheduling, please contact DoraiswamiRamkrishna (Area 10D Chairman),School of Chemical Engineering,Purdue University, West Lafayette, IN47907, (317) 494-4066.For further information concerningCAST Division sessions and scheduling,contact Jeffrey J. Siirola (AreaProgramming Chairman), ECDResearch Laboratories, EastmanKodak Company, P.O. Box 1972,Kingsport, TN 37662, (615) 229-3069.San Diego AIChE Meeting(August 19·22, 1990)Chicago AIChE Meeting(November 11-16, 1990)The CAST Division is tentativelyplanning the following sessions at theChicago meeting:Area lOA:Design1. Process SynthesisArea lOB:ControlSystems and Process2-3. Design and Analysis I and II4. Batch Process Engineering5. Design Methods for Solid-StateChemical Engineering6. Design for Process InnovationSystems and Process1-2.Recent Advances in ProcessControll and II43

3. Nonlinear Control4. Model Predictive Control5. Artificial Intelligence/NeuralNetworks in Process Control6. Process Control Education in the1990s7. Industrial Challenge Problems inProcess ControlJoint Area lOB and Area 15C1. Modeling and Control ofBiochemicalProcessesArea 10c:OperationsProcessingComputers inand Information1-2.Advances in Optimization I and II3-4. Parallel Computing I and II5. Visualization ofComplex Systems6. Neural NetworksArea 10D: Applied Mathematicsand Numerical Analysis1. Mathematical Analysis ofComplex Systems2. Chaos3. Applied Mathematics andNumerical Analysis4. Applications of UnconventionalMathematicsFor further information concerningCAST Division sessions and scheduling,contact Jeffrey J. Siirola (AreaProgramming Chairman), ECDResearch Laboratories, EastmanKodak Company, P.O. Box 1972,Kingsport, TN 37662, (615) 229-3069.Area lOa1. Process Synthesis. Christodo.ulosA. Floudas (Chairman), Department ofChemical Engineering, PrincetonUniversity, Princeton, NJ 08544,(609) 452-4595.2. Design and Analysis. 1. General.Michael F. Malone (Chairman),Department of Chemical Engineering,University of Massachusetts,Amherst, MA 01003, (413) 545-0838.3. Design and Analysis. II. LargeEnvelope Systems. Krishna R.Kaushik (Chairman), Shell OilCompany, P. O. Box 2099, Houston,TX 77252, (713) 241-2098.4. Batch Process EngineeringHeinz A. Preisig (Co-Chairman),Department of Chemical Engineering,Texas A&M University, CollegeStation, TX 77843, (409) 845-0386 andMichael F. Malone (Co-Chairman),Department of Chemical Engineering,University of Massachusetts,Amherst, MA 01003, (413) 545-0838.5. Design Methods for Solid-StateChemical Engineering. Michael F.Doherty (Chairman), Department ofChemical Engineering, University ofMassachusetts, Amherst, MA 01003,(413) 545-2359.6. Design for Process Innovation.Irven H. Rinard (Chairman),Department of Chemical Engineering,The City College of New York,Convent Avenue at 138th Street, NewYork, NY 10031, (212) 690- 6624.Houston AIChE Meeting(Spring, 1991)Los Angeles AIChE Meeting(Fall, 1991)NEWS AND INFORMATIONComputer Aided Process Operations,Proceedings of the First InternationalConference on Foundations ofComputer-Aided ProcessorOperations, Park City, UT, July 5-10,1987. Edited by G. V. Reklaitis and H.D. Spriggs. 1988 viii + 720 pages,U.S. $ 223.75.The objectives of the conference were:to define and discuss the problems ofplant operations; report on thedevelopment and application ofcomputing and computation tolls andtechniques for solving these problems;and bring together leading academicand industrial representatives topromote increased future andinteraction and cooperation in definingand solving operations problems.44

CALL FOR PAPERSCAST Sections at AIChE Annual Meeting,Houston Texas(April 2-6,1989)The CAST Division sessions at the Houston Meeting havebeen summarized in a previous section in this newsletter.Because the November 1, 1988 deadline for the selection ofauthors has passed, these Calls will not be repeated; readersare referred to Volume 11, No.1 (April 1988) of CASTCommunications for a listing. Based upon the deadlines forthe Washington DC and San Francisco national meetings,the editor has reconstructed the remaining deadlines for theHouston meeting as follows:March 1, 1989: Two copies of the final manuscriptsubmitted to session chairman.March 15, 1989: Session chairman submits manuscripts toNew York in order to be made available at the Houstonmeeting.April 2, 1989: If manuscript not submitted on March 15,1989, bring it to Houston.CAST Sessions at AIChE Annual Meeting,San Francisco(November 5-10,1989)The CAST Division is planning the following sessions at theSan Francisco meeting:Area lOA: Computers in Process Design• Process Design and Analysis I and II• Advances in Process Synthesis I and II• Design of Batch Processes• Process Simulation in the Electronics Industry• Integration ofProcess Design and Control (with lOB)Area lOB: Computers in Process Control• Advances in Process Control I and II• Statistical Process Control• Nonlinear Control• Batch Process Control• Integration of Process Design and Control (with lOA)• Modeling Issues in Process ControlArea 10c: Computers in Operations and InformationProcessing• Scheduling and Planning ofProcess Operations I and II• Statistics and Quality Control I and II• Artificial Intelligence and Expert Systems in ProcessEngineeringArea 10D: Applied Mathematics• Progress in Applied Mathematics I and II• Finite Element Applications in Chemical Engineering• Applications of Bifurcation Theory to ChemicalEngineering SystemsAll of these sessions have been confirmed by the MeetingProgram Chairman, John O'Connell (University ofVirginia).The names, addresses, and telephone numbers ofthe sessionchairpersons are given on the next several pages, as arebrief statements of the topics to receive special emphasis insoliciting manuscripts for these sessions. Prospectivesession participants are encouraged to observe the followingdeadlines:January 15, 1989: Revised session information. From areachairmen to meeting program chairman (MPC), Dr. JohnO'Connell (University ofVirginia).April 15, 1989: 1-2 page abstracts received by CAST cochairmenofsessions.May 2, 1989: Session chairman reviews Proposals toPresent forms, selects speakers. Submits information tomeeting program chairman (MPC). Authors notified ofacceptance soon after this date.June 2, 1989: Final session selection by meeting programchairman, who sends decisions to all programmingchairmen.June 23, 1989: Final program copy sent by sessionchairmen to meeting program chairman.July 23, 1989: Final program copy sent by meeting programchairman to AIChE headquarters.August 15, 1989: Extended abstracts submitted to sessionchairmen.September 1, 1989: Extended abstracts submitted byprogram chairmen to AIChE headquarters.45

October I, 1989: Final manuscripts submitted to sessionchairmen.October 16, 1989: Manuscripts submitted by sessionchairmen to New York in order to be made available for SanFrancisco meeting.November 5,1989: If October 16, 1989 deadline is not met,please bring manuscript to San Francisco in order to beavailable at the meeting.The above listing of deadlines is the most extensive onepublished to date in CAST Communications. You can use itas a guide to probable deadlines for future AIChE meetings inwhich the CAST Division members participate as speakers.Process Design and Analysis 1 and ItThese two sessions will focus on the application oftechniques in process design and analysis to practicalproblems. Contributions are specifically invited in thefollowing areas: large-scale optimization, evaluation ofprocess options, detailed simulation of unit operations,design for safety and environmental impact, graphicalmethods, and visualization in design.ChairmanCo-ChairmanAdvances in Process Synthesis II.Papers are solicited in all areas of chemical processsynthesis, including new approaches for total processflowsheets, non-sharp complex and batch separationsequences, heat integration approaches and applications,and reaction path and reactor networks synthesis in bothgrassroot and retrofit situations.ChairmanChristodoulos A. FloudasDepartment of ChemicalEngineeringPrinceton UniversityPrinceton, NJ 08544-5263(609) 452-4595Design ofBatch Processes.Co-ChairmanRoss E. SwaneyDepartment ofChemicalEngineeringUniversity ofWisconsinMadison, WI 53706(608) 262-3641The session will focus on the general area of batch processdesign. The topics of interest include, but are not limited to,the design of multiproducl:! multipurpose plants; schedulingand/or planning considerations in design; design withuncertain process parameters and/or demands; intermediatestorage design; availability and/or reliability analysis;retrofit design; design with resource/operation constraints;and flexibility in design.K. R. KaushikShell Oil CompanyP. O. Box 2099Houston, TX 77252-2099(713) 241-2098Advances in Process Synthesis 10M. L. PrestonImperial Chemical IndustriesP. O. Box7Winnington, NorthwichChesire, CW8 4DJ, EnglandUK (606) 704118ChairmanIftekhar A. KarimiDepartment ofChemicalEngineeringNorthwestern UniversityEvanston, 110 60208-3120(312) 491-3558Co-ChairmanGirish JoglekarBatch Process Technologies,Inc.P. O. Box 2001West Lafayette, IN 47906(317) 463-6473This session seeks contributions in all areas of processsynthesis. Topics of interest include grassroot and retrofitadvances in: design theory, reaction path synthesis, heatintegration approaches and applications, reactor networks,separation synthesis, and synthesis for operability.ChairmanChristodoulos A. FloudasDepartment ofChemicalEngineeringPrinceton UniversityPrinceton, NJ 08544-5263(609) 452-4595Co-ChairmanDon VredeveldUnion Carbide CorporationPO Box8361South Charleston, WV 25303(304) 747-4829Process Simulation in the Electronics Industry.This session will emphasize the continuing role of chemistryand chemical engineering in the processes used tomanufacture microelectronic devices. Papers are solicitedfrom researchers and practitioners active in microelectronicmanufacturing operations, e.g., manufacture of high-puritysemiconductor materials, crystal growth, methods ofpurification, oxygen control in silicon, wafer fabrication,wafer processing, epitaxy, oxidation, lithography, diffusion,ion implantation, plasma processing, metallization,assembly, and packaging. Papers that address the otheraspects of microelectronics fabrication based on chemicalprocesses are also welcome. Papers on facilities preparationas well as utility supplies (de-ionized water, particulate-free46

air, process gases, and chemicals) servicing microelectronicsindustry are also encouraged. Abstracts are due beforeApril 15, 1989. Those interested in submitting a paper mustsubmit a one- to two-page abstract. Authors' names andaddresses should appear below the underlined title. Indicatethe author who will present the paper with an asterisk.ChairmanM. S.BawaTexas Instruments Inc.PO Box 84, Mail Station 883Sherman, TX 75090(214) 868-7111Integration of Process Design and ControlContributions that report case studies of an integrateddesign of whole plants or parts ofplants, and the associatedcontrol systems, are sought. This may include reports ofanysignificant design modification that was made based oncontrol-related considerations such as controllability,reliability, and safety of a plant, as well as robustness toanticipated disturbances or anticipated changes in theoperating conditions. Theoretical contributions that focuson generic frameworks for handling the integrationproblem, may they be heuristic based or deep-knowledgebased, are welcome.ChairmanHeniz A. PreisigChemical EngineeringDepartmentTexas A&M UniversityCollege Station, TX77483-3122(409) 845-0386Co-ChairmanNarses BaronaEthyl Corporation451 Florida StreetBaton Rouge, LA 70801(504) 359-2268Co-ChairmanAdvances in Process Control I and II.Ahmet N. PalazogluChemical EngineeringDepartmentUniversity ofCaliforniaDavis, CA 95616(916) 752-8774Papers which demonstrate advances in process control or inthe application of process control are invited. Relevanttopics include: multivariable control, nonlinear control,robust control, adaptive and self-tuning control, plantwidecontrol, model-predictive control, expert control systems,and statistical process control. Please send extendedabstracts (500 words) to both co-chairmen. The abstractsubmission deadline is April 15, 1989.ChairmanAli CinarDepartment ofChemicalEngineeringIllinois Institute ofTechnologyChicago,IL 60616(312) 567-3042BITNET: CHECINAR@I1TVAXStatistical Process Control.This session will focus on the development and application ofstatistical process and quality control methods to chemicalengineering problems. Topics for potential papers includeapplications to feedback control, process diagnosis, and faultdetection. Proposals for tutorial papers will also beconsidered.ChairmanP. MaurathProcter and GambleCompany11530 Reed Hartman Hwy.Cincinnati,OH 45241(513) 530-3781Nonlinear Control.Co-ChairmanJamesJ. DownsTennessee Eastman CompanyPO Box 511Kingsport, TN 37662(615) 229-5318Co-ChairmanA. KattersonAlcoaAlcoa Technical CenterAlcoa Center, PA 15069Papers are invited which demonstrate theoretical advancesand novel applications in the following areas: exact andapproximate linearization methods, nonlinear decoupling,observer design for nonlinear systems, nonlinear robustcontrol, nonlinear optimal control, and discrete time controlfor nonlinear systems. Papers describing experimentalcontrol applications are also encouraged. Please submit anextended abstract (of no less than 500 words) to sessionchairman or co~chairman.ChairmanV. ManousiouthakisDepartment ofChemicalEngineeringUniversity of CaliforniaLos Angeles, CA90024-1600(213) 825-2046Co-ChairmanC. KravarisDepartment ofChemicalEngineeringUniversity ofMichiganAnn Arbor, MI 48109-2136(313) 764-167447

Batch Process Control.Papers which demonstrate advances in batch process controltheory or applications are invited. Topics of particularinterest include: on-line optimization, parameter and statevariable estimation, modeling issues,' nonlinear modelbasedcontrol, product property or quality control, andinferential control. Please send abstracts to ProfessorBequette by April 15, 1989. Authors will be notified ofacceptance decision by May 15, 1989. Acceptance forpresentation obligates the authors to send the completepaper to Professor Bequette by October 15, 1989.ChairmanJohn W. HamerResearch Laboratories, B-82Eastman Kodak CompanyRochester, NY 14650(716) 477-3740Co-ChairmanModeling Issues in Process Control.W. BequetteDepartment of ChemicalEngineeringRensselear PolytechnicInstituteTroy, NY 12181(518) 276-6377This session has been organized to overview modelingapproaches that are used in controller design. Relevantissues include the level of modeling detail that is requiredfor effective control, whether linear or nonlinear modelsshould be employed, efficient methods for identifying modelswith a limited amount of process data, the sensitivity of thecontrol performance to the degree of approximation in themodel, and methods for identifying and exploiting theuncertainty in the model. Papers dealing with the issues ofmodel reduction, model-based data reconciliation, and noveluses ofstatistics in analyzing process data are also welcome.Please send an extended abstract (500 words) to both cochairmen.Abstract submission deadline: April 15, 1989.ChairmanCo-Chairmanprocesses; long-range planning for capital investment;integration of planning and scheduling; and industrialexperience with commercial software systems.ChairmanIgnacio E. GrossmannDepartment ofChemicalEngineeringCarnegie Mellon UniversityPittsburgh, PA 15213(412) 268-2228Co-ChairmanPeter ClarkSchool ofChemicalEngineeringCornell UniversityIthaca, NY 14853(607) 255-8656Statistics and Quality Control I and II.ChairmanRichardMahChemical EngineeringDepartmentNorthwestern UniversityEvanston,IL 60201(312) 491-3558Co-ChairmanGary ElauDow Chemical Co.Engineering Research1776 BldgMidland, MI 48640(517) 636-5170Artificial Intelligence and Expert Systems in ProcessEngineeringA general session dealing with the theory and application ofartificial intelligence, knowledge-based and expert systemsto process engineering. Emphasis will be given to papersdescribing applications that have followed through todelivery, and those incorporating new theoretical andmethodological considerations. Papers dealing withknowledge representation, qualitative modeling, undertainreasoning, and the like are also welcomed. Areas ofapplication may include product and process design,operations, and control. Authors of application papersshould state the degree of completion and whether thesystem has been applied successfully.Norman F. JeromeResearch Laboratories, B-82Eastman Kodak CompanyRochester, NY 14650(716) 477-2032James RawlingsDepartment ofChemicalEngineeringUniversity ofTexasAustin, TX 78712-1062(512)471-4417Scheduling and Planning of Process Operations I & IITopics of interest include new approaches and algorithms forthe scheduling and production planning of multiproduct andmultipurpose batch plants and of multiproduct continuousChairmanMark A. KramerDepartment ofChemicalEngineeringRoom 66-542Massachusetts Institute ofTechnologyCambridge, MA 02139(617) 253-6508FAX: (617) 253-8000Co-ChairmanGary D. CeraMobil Research andDevelopment CorporationP. O. Box 1026Princeton, New Jersey 08540(609) 737-5299FAX: (609) 737-532548

Progress in Applied Mathematics I and IIPapers are sought emphasizing applications of recentmathematical concepts and techniques; topics includeanalysis of simple systems displaying complex behavior,chaos, and new techniques for analysis of experimental data(time series, power spectrum) as well as mathematicalmodelling ofchemical engineering processes.ChairmanMike DohertyDepartment ofChemicalEngineeringUniversity ofMassachusettsAmherst, MA 01003(413) 545-2359Co-ChairmanJulio OttinoDepartment of ChemicalEngineeringUniversity ofMassachusettsAmherst, MA 01003(413) 545-0593ChairmanV. BalakotiahDepartment of ChemicalEngineeringUniversity of HoustonHouston, TX 77004Co-ChairmanR. NarayananDepartment ofChemicalEngineeringUniversity of FloridaGainesville, FL 32611(904) 392-9103Finite Element Applications in Chemical EngineeringMany of the newer areas of chemical engineering involvetransport phenomena in micro-systems: crystal growth,chemical vapor deposition, electrochemistry, polymerprocessing, electronic component manufacture. This sessionemphasizes applications in which the role of the finiteelement method is important: problems with free andmoving interfaces, graded meshes, and bifurcations.ChairmanB. A FinlaysonDepartment of ChemicalEngineeringUniversity ofWashingtonSeattle, WA 98195(206) 543-4483Co~ChairmanR. L. SaniDepartment ofChemicalEngineeringUniversity of ColoradoBoulder, CO 80309-0424(303) 492-5517Applications of Bifurcation Theory to ChemicalEngineering SystemsThe theory of branching of solutions of nonlinear equations(bifurcation theory) is of great significance to theunderstanding of steady state (multiplicity) and dynamic(stability) characteristics of a diverse variety of chemicalengineering systems. Papers are solicited emphasizing thecombination of analytical tools with computational methodsin this session.49

IiE120/20FORES/IiHTUse BATCHES,the first simulatordesigned specificallyto help you optimizebatch/semicontinuousoperations.Batch Process Technologies. Inc.1291E Cumberland AvenuePost OlTice Box 2001West Lafayette. IN 47906Telephone: (317) 463-6473BATCHES is to batch/semicontinuous processeswhat flowsheeting systems are to continuous processes.Use BATCHES and enjoy the well documented advantagesof advanced simulation technology to manage yourpharmaceutical, specialty chemical or food processes.BATCHES Can Improve Your Bottom Line By• Increasing Productivity of Existing Processes• Reducing Risk of Implementing Decisions• Helping You Optimize New Process DesignsEvaluate the impact of changes on your process BEFOREcommitting valuable resources. Easily simulate yourchoice of "what if .." scenarios. Whether your problemsinvolve scheduling, product mix determination, recipemodifications, debottlenecking, equipment/resourceconstraints, material flow, product sequencing or capacityplanning, BATCHES has the versatility to accuratelyrepresent your process.Learn more about simulation technology and how otherleading manufacturers have benefited from BATCHES byattending our introductory seminar. Call us to make yourseminar reservation or to obtain more information aboutBATCHES.51

AMERICAN INSTITUTE OF CHEMICAL ENGINEERSA. BACKGROUND DATA1989 AWARD NOMINATION FORM*1.2.Name ofthe Award Today's Date _Name ofNominee Date ofBirth _3. Present Position (exact title)4. Education:InstitutionDegree Received Year Received Field5. Positions Held:Company or Institution Position or Title Dates6. Academic and Professional Honours (include awards, memberships in honorary societies andfraternities, prizes) and date the honor was received.7. Technical and Professional Society Memberships and Offices8. Sponsor's Name and Address___-.,..__-;--:--:-;,--:-,- -.,..,-_,- Sponsor's Signature* A person may be nominated for only one award in a given year.

B. CITATION1. A brief statement, not to exceed 250 words, of why the candidate should receive this award.(Use separate sheet ofpaper.)2. Proposed citation (not more than 25 carefully edited words that reflect specificaccomplishments).C, QUALIFICATIONSEach award has a different set of qualifications. These are described in the awards brochure. Afterreading them, please fill in the following information on the nominee where appropriate. Use aseparate sheet for each item ifnecessary.1. Selected bibliography (include books, patents, and major papers published.)2. Specific identification and evaluation of the accomplishments on which the nomination isbased.3. Ifthe nominee has previously received any award from AIChE or one ofits Divisions, an explicitstatement of new accomplishments or work over and above those cited for the earlier awards(s)must be included.4. Other pertinent information.D. SUPPORTING LETTERS AND DOCUMENTSListofno more than five individuals whose letters are attached.NameAffiliation1.2.3.4.5.Please send the completed form and supplemental sheets by April 3, 1989 to the CAST Division 2ndVice Chairman, Professor G. V. (Rex) Reklaitis, School of Chemical Engineering, Purdue University,West Lafayette, IN 47907 (317) 494-4075..