Chemical Engineering Journal - SMGRC
Downloaded from rChemical Engineering Journal 143 (2008) 282–292Contents lists available at ScienceDirectChemical Engineering Journaljournal homepage: www.elsevier.com/locate/cejNonlinear predictive control of a polymerization reactor based on piecewiselinear Wiener modelG. Shafiee, M.M. Arefi , M.R. Jahed-Motlagh, A.A. JalaliIran University of Science and Technology, Tehran 16846, Irana r t i c l ei n f oArticle history:Received 18 February 2008Received in revised form 23 April 2008Accepted 5 May 2008Keywords:Nonlinear model predictive control (NMPC)Piecewise linear (PWL) Wiener modelGeneralized multiple-level noise (GMN)Quadratic programming (QP)Polymerization reactorMulti-input multi-output (MIMO)a b s t r a c tIn this paper, a nonlinear model predictive control (NMPC) based on a piecewise linear Wiener model isapplied to a polymerization reactor. The static nonlinear part of the applied Wiener model is approximatedusing the piecewise linear functions and its dynamic linear element is identified using a state-spacedescription. Due to the nonlinear gain of model, for gathering data, a generalized multiple-level noise(GMN) test has been used. This test demonstrates the response of the system to a range of amplitudechanges. The predictive control based on this model retains all the interested properties of the classicallinear MPC. This approach leads to a quadratic programming problem due to the canonical structure ofthe nonlinear gain. The control scheme has been applied to a polymerization reactor as a MIMO process.Results show that the used Wiener model is able to identify the nonlinear processes effectively. Thenonlinear predictive control based on this model is compared to the linear MPC. The parameters of bothlinear and nonlinear model predictive controllers are tuned and the performances of both methods arecompared. It is shown that the nonlinear controller has a better performance, having short settling timeand without any overshoot compared to its linear one. Moreover, this controller has a good performanceand rejects unmeasured disturbances effectively. 2008 Elsevier B.V. All rights reserved.1. IntroductionThere are very few design techniques that can be proven to stabilize processes in the presence of nonlinearities and constraints.Model predictive control (MPC) is one of these techniques [1]. MPCrefers to a class of computer control algorithms that control thefuture behavior of a plant through the use of an explicit processmodel. At each control interval the MPC algorithm computes anopen-loop sequence of manipulated variable adjustments in orderto optimize future plant behavior. The first input in the optimalsequence is injected into the plant, and the entire optimization isrepeated at subsequent control intervals [1]. Regarding desirableproperties of MPC, these controllers are applied quickly in a widerange of different industries; such that by the year 1999 more than4500 applications of these controllers have been reported whichuse linear model, while about 80% of these applications are in petrochemical industries [2,3]. By now, the application of MPC based onlinear dynamic models covers a wide range of applications and thelinear MPC theory can be considered quite mature [1]. Nevertheless,many manufacturing processes are inherently nonlinear and there Corresponding author. Tel.: 98 21 77240492; fax: 98 21 77240490.E-mail address: arefi@iust.ac.ir (M.M. Arefi).1385-8947/ – see front matter 2008 Elsevier B.V. All rights reserved.doi:10.1016/j.cej.2008.05.013are cases where nonlinear effects are significant and can not beignored. These include at least two broad categories of applications[1]:1- Regulator control problems where the process is highly nonlinear and subject to large frequent disturbances (pH control,etc.).2- Servo control problems where the operating points change frequently and span a wide range of nonlinear process dynamics(polymer manufacturing, ammonia synthesis, etc.).Under these conditions, linear models are often not sufficientenough to describe the process dynamics adequately and therefore nonlinear models should be used. Nonlinear model predictivecontrol (NMPC) is a good development of linear MPC to nonlinearworld that is presented as a very good scheme for this type of problems. NMPC is conceptually similar to its linear counterpart exceptthat nonlinear dynamic models are used for process prediction andoptimization [4].Nonlinear systems modeling can be performed in three different ways. The first method is the use of different models for variousoperating points of the system. The second approach is using fundamental equations (e.g. mass and energy conservation equations)which in most cases are difficult to use due to process complexity.
Downloaded from rG. Shafiee et al. / Chemical Engineering Journal 143 (2008) 282–292NomenclatureAccpDDajEjfgth HIIfkdkfkfski kpktktc ktcoheat-transfer area of the reactorspecific heat capacity of reactor contentspolydispersityDamkohler number for species jactivation energy for reaction jinitiator efficiencygel effect factorheat-transfer coefficientheat of reactioninitiator concentration in the reactorinitiator feed concentrationdissociation rate constant for initiatorrate constant for chain transfer to monomerrate constant for chain transfer to solventfrequency factor for reaction i (i d, f, p, t)propagation rate constantoverall termination rate constantrate constant for combination terminationzero conversion frequency factor for combinationtermination reactionktdrate constant for disproportionation ktdozero conversion frequency factor for disproportionation reactionktooverall termination rate constant at zero conversion ktofrequency factor for overall termination rate constant at zero conversionMmonomer concentration in the reactorMfmonomer feed concentrationMfomonomer feed concentration for scaling purposesonlymolecular weight for initiatorMWiMWmmolecular weight for monomermolecular weight for solventMWsPconcentration of live polymerqvolumetric flow rateRuniversal gas constantSsolvent concentrationttimeT, Tcreactor temperature, jacket temperaturefeed temperatureTfuvector of manipulated variablesvkwhite noise sequence, measurementVvolume of reactorfree volumeVfVfm , Vfp , Vfs free volume contribution of monomer, polymer,solvent, respectivelyVifvolume fraction of initiator in feedvolume fraction of monomer in feedVmfwwhite noise input, processWdimensionless live polymer concentrationxidimensionless reactor state onomer concentration)manipulated variable (dimensionless jacket temx2cperature)x3fmanipulated variable (dimensionless inlet initiatorconcentration)Greek lettersˇdimensionless heat-transfer coefficient283 m , p , s volume fraction of monomer, polymer, solvent,respectively 0 , 1 , 2 zeroth, first, and second MWD moments number-average chain length density of reacting medium s , i , m densities of solvent, initiator, and monomer,respectively dimensionless timeSubscriptsD, fdissociation, transfer to monomerppropagationtc , tdtermination: combination, disproportionation1, 2, 3, 4 monomer, temperature, initiator, solvent5, 6, 7moments: zeroth, first, secondThe third and the best approach is the use of empirical models thatconvert the available input–output data to an input–output relationwhich can be used for the prediction of the future behavior of thesystem.There are several approaches to nonlinear system identificationbased on empirical models. One way is to use theoretically soundnonlinear functions and to develop identification schemes for thesemodels. Identification using Volterra series, neural networks andnonlinear ARMAX models belong to this methodology. The advantage of this approach is the ability to obtain a global model of theunderlying system. The main difficulty of the approach is the highcost in identification tests and computation. Another approach isto combine linear dynamic models with static or memoryless nonlinear functions. These types of models are called block-orientednonlinear models. There are several advantages when using blockoriented models: (1) low cost in identification tests; (2) low cost inidentification and control computations and (3) it is easy to comprehend and to incorporate a priori process knowledge [5].The class of block-oriented nonlinear models includes complexmodels which are composed of linear dynamic systems and nonlinear static elements. Wiener and Hammerstein models are the mostknown and the most widely implemented members of this class.Wiener and Hammerstein models have found numerous industrialapplications for system modeling, control, fault detection and isolation. Wiener and Hammerstein models reveal the capability ofdescribing a wide class of different systems and apart from industrial examples, there are many other applications in biology andmedicine [6].In particular, Wiener models have a special structure that facilitates their application to NMPC. These models consist of a lineardynamic element which is followed by a static nonlinearity andcan represent many of the nonlinearities commonly encounteredin industrial processes [7]. Due to the static nature of the nonlinearities, they can be removed from the control problem. This factgeneralizes the well-known gain-scheduling concept for nonlinear control. Due to the presence of some potential computationaldifficulties, an implicit inversion of the nonlinear static gain is necessary [7]. Application of these models in NMPC has been addressedin several papers [7–15]. For example in Refs. [8,9], a static nonlinear term is used to model the inverse of the nonlinearity of theplant and is selected as a polynomial with proper degree. Besidesin Refs. [7,10,11], the nonlinear term and its inverse are modeledusing piecewise linear (PWL) method. In Ref. [12], a nonlinearcombination of Laguerre models followed by a single-layer neural network is introduced as an efficient nonlinear identificationmethod used in MPC applications. The nonlinear predictive con-
Downloaded from rderG. Shafiee et al. / Chemical Engineering Journal 143 (2008) 282–292trol based on Wiener-Neural model is presented in Ref. [13], wherethe static nonlinear part is modeled using neural network. In allof these works the paradigmatic applications have been pH neutralization and continuous stirred tank reactor (CSTR) processes. InRef. [14], the nonlinear predictive control based on a Wiener-Neuralmodel is applied to a plug-flow tubular reactor, where the process is simulated in HYSYS environment. In Ref. [15], a distillationcolumn simulation model is used as a benchmark to demonstratethe benefits of a Wiener model based identification and controlmethodology. The results verify the capability of this method inidentification of a nonlinear ill-conditioned plant compared withthe other existing linear techniques.In addition to the above-mentioned processes, a polymerizationreactor is a process that bears a highly nonlinear behavior. Polymerization reactors are difficult to control effectively due to their highlynonlinear behavior and multi-input multi-output structure.Lack of online measurements and input constraints are twoimportant problems which are sometimes neglected in academicstudies of a polymerization reactor control [16,17]. Most nonlinear control techniques proposed for polymerization reactors arebased on feedback linearization or MPC [17–21]. In Ref. [17], amultivariable extension of the feedback linearization (FBL) MPCcontrol strategy for the free-radical polymerization of methylmethacrylate in a CSTR has been presented and its results arecompared to NMPC. A constrained MPC of a polymerization reactor is presented in Ref. [18], where the process is controlled asthree inputs–three outputs. In Ref. [19], a multi-input multi-output(MIMO) Wiener model of a polymerization reactor is identifiedand the model is used in an MPC scheme. The quality of theproposed controller is also compared with that of linear MPC.This algorithm is based on the past inputs multivariable outputerror state-space (PI-MOESP) method for the estimation of systemmatrices of the linear part [22], and Tchebychev polynomials forthe nonlinear part [19]. In Ref. [20], an adaptive MPC is appliedto methyl methacrylate (MMA) polymerization reactor. The control of a solution copolymerization reactor using MPC algorithmbased on multiple piecewise linear models is presented in Ref.[21].In this paper, a nonlinear model predictive control based ona piecewise linear Wiener model is applied to a polymerizationreactor. The static nonlinear element of this Wiener model isapproximated using the piecewise linear functions and its dynamiclinear element is modeled using a state-space description. PWLfunctions have been proved to be a very powerful tool for modelingand analyzing nonlinear systems [23,24]. A generalized multiplelevel noise (GMN) test [5] is used for getting data in order to identifythe model. The presented control scheme has been applied to apolymerization reactor, and its results have been compared to linearMPC.The paper is organized as follows: In Section 2 a Wiener modelwith a piecewise linear representation for the nonlinear gain is presented and then the NMPC based on this Wiener model is described.In Section 3, the presented control scheme has been applied to apolymerization reactor, and simulation results are compared to linear MPC. Finally, some concluding remarks are discussed in Section4.Fig. 1. The Wiener model.procedure of the Wiener model identification and inverse modelevaluation is stated. Finally, the NMPC based on this Wiener modelis presented.2.1. Piecewise linear Wiener modelAmong the nonlinear black box models, the block-oriented models are efficient structures in nonlinear modeling [14]. These modelsconsist of a series connection of a linear dynamic and static nonlinear element.A Wiener model consists of a dynamic linear block (H1) in cascade with a static nonlinearity at the output (H2), as shown in Fig. 1.Here v(k) Rmo is an intermediate signal that does not necessarilyhave a physical meaning. On the other hand, in the Hammersteinmodel the static input nonlinearity precedes the linear block.In certain respects, Hammerstein models are very similar tothe linear models on which they are based. For example, if u(k)is a piecewise constant input sequence [e.g. pulses, steps, pseudorandom binary sequences (PRBS), etc.], for any static nonlinearitythe intermediate variable sequence will also be a piecewise constant sequence with the same general character (specifically, withtransitions at the same instants as u(k), but assuming different values). Hammerstein models have been considered as alternatives tolinear models in a number of chemical process applications [25].In particular, while Hammerstein and Wiener models exhibitexactly the same steady-state behavior, the differences in theirtransient responses can be quite significant. As a specific example,the general character of the step response can change with the signand/or magnitude of the input step, unlike the case of the Hammerstein model, where this general character is determined entirely bythe linear part [25]. Because of this behavior and the capability ofmodeling complex nonlinear dynamics by Wiener models led us tothe selection of this model structure. In this paper, the possibilitiesand the advantages of the use of a specific Wiener approximationto represent the model of the process are analyzed.Let us assume that the system to be controlled can be describedby the following discrete-time, nonlinear, state-space model [7,11]:x(k 1) f (x(k), u(k))(1)y(k) g(x(k)) d(k))(2)where f : Rn Rmi Rn and g : Rn Rmo are twice continuouslydifferentiable functions, x Rn is a vector of n state variables, u Rmiis a vector of mi process inputs or manipulated variables, d Rmo isa vector of mo additive disturbance variables, y Rmo is a vector ofmo process outputs and k is the sample time.There are several options to describe the linear dynamic blockin Wiener models. For example, some of the used representationsinclude convolution models (step or impulse responses), autoregressive moving average with exogenous input (ARMAX) models,autoregressive with exogenous input (ARX) models, state-spacemodels, etc. [9]. In this work, a state-space model is used as follows:2. The nonlinear model predictive control based onpiecewise linear Wiener modelx(k 1) Ax(k) Bu(k)v(k) Cx(k) Du(k)In this section, nonlinear predictive control based on a piecewiselinear Wiener model is introduced. For identification of this model,an efficient test signal for gathering dynamic data is necessary. Todo this, some test signals are presented. By using obtained data, thewhere A, B, C, D are the system matrices with proper dimensions.For the static nonlinear element (H2), the continuous PWL functions are used. PWL functions have been proved to be a verypowerful tool for modeling and analyzing nonlinear systems [24].(3)
Downloaded from rG. Shafiee et al. / Chemical Engineering Journal 143 (2008) 282–292285number of levels on this test is equal or greater than the degree ofthe nonlinear polynomial which must be identified. Moreover, theaverage switching time of the test can be obtained from Tsw T/3,where T is 98% of the process settling time.Fig. 2. The piecewise linear Wiener model.2.3. Wiener model identification and inverse model evaluationIt can be proved that any nonlinear function f (f : Rmo Rmo ), canuniquely be represented as [24]:f (v) C T (v)where the vector (4)T[ 0T , 1T , . . . , mo T ]is the elements of theTT ] is the parameter vector associatedbasis and C [C0T , C1T , . . . , Cmoiwith the vector function .In this work, the function is f H2 : D D, being D Rmo , as shownin Fig. 2. The domain and the image of the PWL function share thesame dimension in our application. Moreover, if we assume that thefunction f of the system is invertible (this is a reasonable assumptionfor a large set of process models), it is possible to define the inversefunction as f 1 , such that v f 1 (f (v)). This function is also uniqueand PWL [10].2.2. Input signal designSome important factors which must be considered in designingthe identification test for nonlinear systems are [5]:a)b)c)d)e)Duration of the test signal.Amplitude and shape of the test signal.The spectrum of the test signal (the average switching time).Correlation of the test signal in each channel.The number of manipulated variables in each test.Traditionally, PRBS are used as the inputs to a system in order toproduce representative sets of data to be analyzed. In theory, a PRBSexcites the range of dynamics present in a system so that a dynamicmodel can be produced which contains these dynamics. This is notsufficient, however, for fitting a Wiener model. Since these models have nonlinear gains, an input signal must be used which alsodemonstrates the response of the system to a range of amplitudechanges [9]. A signal that satisfies these criteria is a GMN [5] or amodified PRBS signal [9] which, in addition to random frequency,also exhibits random amplitude changes.In addition to above, one disadvantage of using a PRBS signalis that its spectrum has dips around some frequencies, which willresult in low signal-to-noise ratios in these frequency ranges. A better way to generate binary signals with low-pass character is theso-called generalized binary sequence (GBN) [5]. Another advantage with GBN is that the signal length is flexible. The GBN also hasa minimum crest factor [5].Since in nonlinear systems the test time depends mainly on thenumber of parameters in the model and the level of noise andunmeasured disturbances, it is recommended longer test time incomparison with linear systems [5]. This is typically consideredabout 16–25 times of the settling time of the process. The other factors may be included by choosing one of the following test signals[5]:Different Wiener model identification approaches can be foundin relevant literature [7–15,19,22,26]. A general classification ofthese approaches is the following:1) The N-L approach.2) The L-N approach.3) The simultaneous approach.In this paper, the L-N approach is used for identification ofWiener model, because it is straightforward and ensures an accurate description of the static nonlinearity. In this method, first thelinear block is identified using a correlation technique. After that,the intermediate signal v is generated from the input signal andfinally the static nonlinearity is estimated.For identification of Wiener model parameters, the linear partis modeled using a state-space description and the nonlinear staticgain is identified using dynamic and steady-state input–outputdata. To identify the linear dynamic part and the static PWL function, the N4SID (Numerical Algorithms for Subspace State SpaceSystem Identification) algorithm and the PWL Toolbox [27] basedon the least square method were used, respectively.In order to implement the NMPC scheme that is described inthe next section, a good representation of the inverse of the nonlinearity is necessary. To identify it, the following approaches areavailable [7,28]:a) Algorithmic approach.b) Direct inverse computation.c) Direct identification.Since problems of small dimension are dealt with here anduseful data for the identification process are available, the directidentification approach has been chosen in this paper. In thismethod, after identification of linear model, the output sequencesof this LTI system will be computed. With this sequence, a primaryidentification of the nonlinear part of the Wiener model can beestimated. Now, for identification of inverse of nonlinearity, theinputs and outputs of this sequence are changed and using this newsequence, the inverse of nonlinear element is identified for controlpurposes.2.4. Nonlinear model predictive controlThe control problem to be solved is to compute a sequenceof inputs u(k), {k 1,. . .,M}, that will minimize the followingdynamic objective:J P j 1 y(k j) r Qj M 1 u(k j) Rj(5)j 0subject to model equations and to inequality constraints:a) Stair Test.b) Filtered white uniform noise.c) GMN.yl y(k j) yu j 1, . . . , P 1ul u(k j) uu j 1, . . . , M 1In this work, the GMN test has been used for data collection.This type of test is a multi-level extension of GBN. In this test theamplitude and the number of pulses must be selected suitably. Thewhere P is the prediction horizon, M is the control horizon, u is themanipulated variable, y is the output variable and r is the desiredset point. The relative importance of the objective function contributions is controlled by setting the time dependent weighting(6)
Downloaded from rderG. Shafiee et al. / Chemical Engineering Journal 143 (2008) 282–292matrices Qj and Rj . Beyond the control horizon, the control signalis assumed to be constant ( u(k j) 0, j M,. . .,P). Once u(k) iscomputed, following the receding horizon principle, only the firstelement of the optimal control sequence is used as the current control value. Then the horizon will shift one step forward in time andthe whole procedure is repeated.In this work, since the PWL function f is assumed to be invertible; the inverse of nonlinear part of the Wiener model is used totranslate set points, output variables and their constraints to thelinear model. Finally, the Wiener NMPC (WNMPC) can be posed asa quadratic programming (QP) problem:Tmin J min{(v̂(k) r (k)) Q(v̂(k) r (k)) u(k) R u(k)}u(k)Tu(k)(10)Ṡ q(S S)V f(11)q1 0 0 [(kf M ktd P kfs S)] P ktc P 2V2(12)q[(kf M ktd P kfs S)(2 2 ) ktc P]P 1 1 V(1 )(13)q[(k M ktd P kfs S)( 3 3 2 4 ) ktc P( 2)]P 2 2 fV(1 )2(14)(7) where v̂(k) is the predicted output for the linear model, u(k) is thevector of manipulating variables and r* (k) f 1 (r(k)) is a transformation of the set point r(k) to the linear part. Also, the relativeimportance of the objective function contributions is controlled bysetting the weighting matrices Q and R.3. Case study: polymerization rectorIn this section, the presented control scheme is applied to apolymerization reactor as a MIMO process. Polymerization reactors are difficult to control effectively due to their highly nonlinearbehavior and MIMO structure [17]. In this section, identificationand predictive control of this process is presented.3.1. Process descriptionThe process under consideration is solution polymerization ofmethyl methacrylate (PMMA) in a jacketed CSTR. As shown inFig. 3, three streams—monomer, initiator and solvent are feed intothe CSTR system. The reactor is equipped with a cooling jacket toremove extra heat generated during the exothermic polymerization. The exit stream contains polymer product, unreacted polymer,initiator and solvent, and is send downstream for separation[18].The model equations are [16]:qṀ (Mf M) kp MPVq(T T ) V fq(I I) kd IV fwheresubject tovl v̂(k) vuul u(k) uuṪ İ H cp(8) kp MP hAc(T Tc )V cpkp Mkp M kf M kfs S kt P P 2fkd IkI(15)(16)kt ktc ktd(17)The ith moment of the dead polymer molecular weight distribution (MWD) is represented by i (i 0, 1, 2) and M isthe monomer concentration, T is the reactor temperature, I isthe initiator concentration, S is the solvent concentration, Mfis the monomer feed concentration, Tf is the feed temperature, If is the initiator feed concentration, Sf is the solventfeed concentration and Tc is the coolant temperature. Otherprocess parameters are defined in notation part. The rate constants with the exception of kfs are assumed to follow anArrhenius dependence on temperature. These rate constants are[16,17]: E dkd kd exp RT E pkp kp exp RT E to exp kto ktoRT E fkf kf exp RT(18)(19)(20)(21)The expression for kt is obtained using (20) and the Schmidt-Raycorrelation for the gel effect [16–18]: 0.10575 exp[17.15Vf 0.01715(T 273.2)] 4(9)gt ktVf [0.1856 2.965 10 (T 273.2)] 2.3 10 6 exp[75Vf ]kto Vf [0.1856 2.965 10 4 (T 273.2)](22)The free volume Vf is calculated from the volume fractions ofmonomer, polymer, and solvent in the rector through the followingequations [16–18]: Vfm m Vfp p Vfs sVf 0if 0if 0(23)(24)whereFig. 3. Schematic of solution polymerization of MMA in a CSTR [18].Vfm 0.025 0.001(T 167)(25)Vfp 0.025 0.00048(T 378)(26)Vfs 0.025 0.001(T 181)(27)
Downloaded from rG. Shafiee et al. / Chemical Engineering Journal 143 (2008) 282–292Volume fractions are calculated using the reactor concentrationsand physical property data under the assumption of ideal mixing[17]dx7 x7 [(Daf x1 Exf Das x4 gt Datd WExtd )( 3 3 2 4 )d gt Datc WExtc ( 2)] W (1 )2(38)Dap x1 ExDap Ex x1 Daf Exf x1 gt Dat WExt Das x4(39)MWm M m(28)whereMWi M i(29) s MWs M s(30) p m m s s i i p(31) m i where MWj is the molecular weight of species j, j is the pure component density of species j, and is the density of the fluid in thereactor (which is assumed to be constant). The equation for p isderived from the requirement that the mass fractions in the reactorsum to unity. The initiator concentration in the reactor usually isvery low relative to the other species. As a result, it is reasonable tomake the approximation i 0 [17].The polymerization reactor equations are nondimensionalizedusing the dimensionless variables, as shown in Table 1, to give thefollowing state-space model [16]:dx1 x1f x1 Dap Wx1 Exd dx2 x2 BDapd p Wx1 Ex(32) ˇ(x2 x2c )(33)dx3 x3f x3 Dad x3 Exdd (34)dx4 x4f x4d (35)dx5 x5 [(Daf x1 Exf Das x4 gt Datd WExtd )( W )]d 1 2W gt Datc Extc2(36)dx6 x6 [(Daf x1 Exf Das x4 gt Datd WExtd )(2 2 )d gt Datc WExtc ] W (1 )(37)Table 1Dimensionless variables [16]Dimensionless variableDefinition x3tqVMMfoT Tf EpTf RTfIMfox4SMfotdx5 Mfofx6 1Mfox7 2MfoPMfoMfMfox1x2Wx1fx2cx3fTc Tf EpTf RTfIfMfoDimensionless pk e d pdDapDadDasDatDatcB( H)Mfo cp TfDatdˇhAc cp qDafkp Mfo e pkp e p VMfoqDapkfs Mfo Vq e t p VMktofoq ktcoe to p VMfoqk e td p VMfotdoqk e f p VMfofq287Ex expExj expx21 (x2 / j x21 (x2 /(40)p)p) ,(j d, f, t, tc, td)(41)The parameter values of model equations are given in Table 2.3.1.1. Open-loop resultsThe open-loop behavior of the system has been justifiedusing simulations on steady-state responses. Moreover, using thetransient responses of the state variables, may give a better understanding of the open-loop dynamics, i.e., monomer and initiatorconcentration in the reactor, rector temperature, and three leadingmoments.The feed conditions for a start up and initial values are summarized in Table 3. The start up procedure is as follows:For t 0, the reactor contains only the monomer and solventwhich are at a same temperature with that of the cooling jacketand feed. At this time, in order to reach the desired conversion, theconcentration of the monomer feed and the reactor monomer iskept at the same level.It is assumed that no reaction takes place before the feedenters the reactor. At time zero, monomer, initiator, and solventare applied to the reactor. Immediately, the reaction starts until thesystem reaches its steady state. The open-loop steady-state resultsare listed in Table 4 as the main case of simulation.The open-loop responses, for start up of the CSTR system, areillustrated in Fig. 4. It is observed that the monomer concentrationin the reactor, changes from 3.5 mol/L to a new steady-state valueas 3.14 m
the nonlinear part [19]. In Ref. [20], an adaptive MPC is applied to methyl methacrylate (MMA) polymerization reactor. The con-trol of a solution copolymerization reactor using MPC algorithm based on multiple piecewise linear models is presented in Ref. [21]. In this paper, a nonlinear model predictive control based on
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