SIMULATION AND OPTIMIZATION OF A STYRENE MONOMER REACTOR USING A NEURAL NETWORK HYBRID MODEL

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1 opyrght 00 IFA 5th Trennal World ongress, Barcelona, Span SIMULATION AND OTIMIZATION OF A STYRENE MONOMER REATOR USING A NEURAL NETWORK HYBRID MODEL Heen Lm, Mngu Kang, Mnho hang, Jeongseok Lee*, Sunwon ark Dept. o hem. Eng., KAIST *SR Research Insttute, LG hem. Abstract: The modelng and optmzaton o a dehydrogenaton reactor n the ndustral styrene monomer plant has been proposed n ths study. Because ths reactor consumes large amount o expensve hgh-pressure steam to produce the styrene monomer (usually more than two thrd o total energy costs), the mnmzaton o the operatng cost s hghly desrable to maxmze the prot. However, t s not easy to develop the accurate mathematcal model because o the lack o nternal or ntermedate measurements o the ndustral reactor, and also the lack o expermental results o the catalyst deactvaton. To overcome these dcultes, we propose an alternatve model o the styrene monomer reactor usng a hybrd model n whch the mathematcal model s combned wth neural networks. Maor reacton mechansm s descrbed n the mathematcal model, and the deactvaton eect s modelled n neural network usng the real operaton data. Usng ths model, the senstvty analyss and the optmzaton o the ndustral plant have been perormed. The proposed optmal operaton enlarges the prot o the plant very much. opyrght 00 IFA Keywords: reactor modelng, parameter optmzaton, parameter estmaton, process smulators.. INTRODUTION Most o the ndustral styrene monomer plants carry out the adabatc dehydrogenaton reacton n the multple reactors n seres. Even though these adabatc reactors need more expensve equpment cost and operatng cost, they have been used n many companes because o ther hgh converson o ethylbenzene and the small usage o catalyst pellets (Lm, et al., 00b; Wett, 98). The optmzaton o a styrene monomer(sm) reactor s hghly valuable because t can be used n optmzng the current operaton, whch has hgh cost due to the large amount o expensve hgh-pressure steam (Haung, 98). But, only a ew researchers had studed about ndustral styrene monomer reactors because they have dcult geometres and unknown reacton mechansms (Sundaram, et al., 99). There had been many eorts to mprove the productvty o styrene monomer producton plants. But most o them had been ocused on mprovement o the recovery secton or the other equpments except the reactor and development o new dehydrogenaton catalyst (avan and Trro, 995). Reacton models had been ormulated (lough and Ramrez, 976; Scheel and rowe, 969; Hrano, 986) and characterstcs o the styrene monomer plug-low reactor had been studed (Scheel and rowe, 969; Abdalla, et al., 976). Only Savorett (Savorett, et al., 999) proposed the non-adabatc radal-low reactor model. Through these researches, t had been possble to represent the styrene monomer reactor mathematcally. Although the mathematcal model o the radal low reactor gave a reasonable predcton rom the ndustral pont o vew (Lm, et al., 000), the nature o the process presented some dcultes n the predcton o catalyst actvty. The lack o nternal or ntermedate measurements o the reactor represented a lmtaton or the detaled model valdaton. In addton, the lack o experments o catalyst actvty represented the uncertanty o parameters n mathematcal equatons. Thus the mathematcal model was not enough to represent changes o catalyst actvtes. In ths study, alternatve models o the styrene monomer reactor have been proposed usng a hybrd model n whch the mathematcal model s combned wth neural networks. The mathematcal knetc model or an adabatc radal-low styrene monomer reactor has been used as a rst prncple model. A neural network model has been developed or the

2 catalyst deactvaton model because the exact parameters or the deactvaton could not be measured and the deactvaton was observed by the plant data only. Usng ths model, the smulaton and optmzaton have been perormed. Some examples have been tested wth ths smulator and the potental usages o ths program have been nvestgated.. SYSTEM DESRITION. Reactor System: characterstcs reactor, ths hydrocarbon eed s mxed wth superheated steam n the adabatc radal low reactor. Ths steam acts not only as a heatng medum but also as a dluent. Hgh S/O rato and low operatng pressure ncrease the molar converson o ethylbenzene. Molar converson o ethylbenzene s also a uncton o temperature; hgher temperature yelds hgher conversons n nearly a lnear uncton. Thereore, an ncrement n converson can be obtaned by ncreasng the reactor temperature. Snce the reacton o ethylbenzene to styrene s endothermc, t s carred out n multple adabatc radal bed reactors lled wth catalysts. As or load, molar converson o ethylbenzene s an nverse uncton o ths parameter snce hgher velocty means lower resdence tme. Ths n turn reduces molar converson (Krk, et al., 98).. Reacton Mechansm In the styrene monomer reactor, three maor competng reactons are known (Scheel and rowe, 969): reactons (), (), and (). Styrene s produced by dehydrogenaton o ethylbenzene. These reactons are endothermc except (). 6H 5H H 6H 5HH + H () 6H5H H 6H6 + H 4 () 6H5H H + H 6H5H + H () 4 Fg.. Reactor conguraton o current adabatc radal low reactor In addton to these, there are three sde reactons by thermal crackng at hgher temperature. H O + H4 O + H (4) H O + H 4 O + H (5) H O + O O + (6) H Snce the dehydrogenaton o ethylbenzene s a reversble endothermc reacton, hgh styrene yeld s avored by hgh temperature.. MODEL DESRITION Fg.. Smpled reactor model and cross-sectonal conguraton Ths styrene monomer plant has two ethylbenzene dehydrogenaton reactors n seres as shown n Fg.. Between two reactors, there s a heat exchanger. In ths heat exchanger, the product stream rom the rst reactor s reheated up to 60. The converson o ethylbenzene (EB) and the selectvty o ethylbenzene to styrene are aected by reactor operatng pressure, temperature, molar steamto-hydrocarbon rato (S/O rato), and reactor load (=[current ethylbenzene low rate] / [desgn ethylbenzene low rate]). The desred reacton can be enhanced at the low operatng pressure because the man reacton o styrene monomer producton ncreses the number o moles. The hydrocarbon eed to the reactor s the mxture o resh ethylbenzene and recycled unconverted ethylbenzene. ror to enterng the. Frst rncple Model: mathematcal model Reacton model: The knetc model or the reactons s shown n Table. Governng equatons: The adabatc radal low reactor s smpled usng the ollowng ve assumptons. () Quas-steady state operaton () Ideal gas mxture: ths assumpton s vald because the styrene monomer reactor s operated at hgh temperature and low-pressure condton. () Unorm dstrbuton: the parabolc delector nsde o the plant makes the unorm low pattern. (4) No pressure drop and mass or heat duson n the axal drecton: only the pressure drop exsts nsde o catalyst bed n the radal drecton. (5) No reacton except catalyst bed

3 Accordng to the prevous assumptons, the shell mass balance (5), energy balance (6), pressure drop (7) equatons n cylndrcal coordnates are ollowng. Table Reacton Model Reacton amounts n reacton, = k( EB ST H / K ) (7) = k EB (8) = k EB (9) H 4 = k4 H O (0) ET = 5 k5 H O () ME 6 = k H O () 6 O T Reacton amounts o component I,, 6 = ν F () = Reacton rate constant E k = k exp (4) 0 RT m N dg = π rl MW, dr F g = (5) = = m ( H ) πrl dt = = (6) dr F p d out n = (7) dr R R out. Neural Network Model n The neural network model conssts o a set o processng unts called neurons, connected to one another. The neural network n ths study s a eedorward network wth one hdden layer, seven nput varables and one output varable. By adustng parameters n the couplng, between neurons, the network s capable o learnng rom a set o numercal data correspondng to the nput and desred output. (Nascmento, et al., 999) Because o the tme-varyng characterstcs o catalyst actvty, the recurrent neural network s used n ths hybrd model. As the nput varables are, () temperature (T(k-)); () pressure ((k-)), () partal pressure o steam n eed ( STM (k-)); (4) eed lowrate o EB (F EB (k-)); (5) deactvaton actor at tme pont k- (Φ(k-)). Fg.. Neural network model A set o data obtaned n the ndustral unt was used to tran the neural network and the other set to check the traned neural network. As shown n Fg 4, usng the derence o two values, calculated real actor (Φ 0 ) or target value and predcted actor (Φ) or output value, neural network model can be traned. Because desred output, Φ 0 s not measured n plant, Φ 0 s calculated rom governng equaton and real plant data. Fg. 4. Schematc dagram o learnng method Fgs 5 and 6 show the calculated (Φ 0 ) versus predcted deactvaton actor (Φ) or the learnng and test set. The acheved agreement s wthn the error range, 0%. Fg. 5. alculated vs. predcted actors o reactor As the output varable s () deactvaton actor at tme pont k (Φ(k))

4 Fg. 6. alculated vs. predcted actors o reactor. Hybrd Model Ater tranng, the proposed neural network model supples the catalyst deactvaton actor (Φ) at any operatng condtons. Mass balance, energy balance and pressure drop equatons can be solved usng gven plant data. From these equatons, reactor output data, such as temperature and composton o the output low o each reactor, can be obtaned. The structure o the hybrd model, n whch the mathematcal model and the neural network model are coupled, s presented n Fg 7. Fg. 7. Schematc dagram o hybrd model 4. Hybrd Model Test 4. Examples The proposed hybrd model s well tted wth the real plant data. Fg. 8 compares the real plant data wth smulaton results usng predcted catalyst deactvaton actor (Φ). These gures show the perormance o styrene monomer and ethylbenzene that are the man materals o ths process. The smulaton results show good perormance o 0.4% relatve error, compare wth.7% relatve error o mathematcal model (Lm 00a). Fg 8. omparson o smulaton data and real plant data o reactors: a) reactor, b) reactor 4. Senstvty Analyss or S/O rato hange Selectvty (%) DOS (day) Fg. 9. Selectvty or S/O rato change onverson (%) DOS (day) Fg. 0. onverson or S/O rato change To see the smulaton o the styrene monomer reactor, one o the operatng varables s smulated. The nput condtons are based on ndustral data. Fg 9 shows the smulaton results wth changng S/O rato and DOS (days on stream). Accordng to the gure, the converson o early DOS s smlar. But, ncreasng DOS, hgher S/O rato condton shows hgher converson o styrene monomer rom ethylbenzene and lower catalyst deactvaton eect.

5 4. Optmzaton or Operatng ondtons The operatng condtons are optmzed usng gven boundares. The obectve uncton, gven condtons and the results are shown n Table. Ths optmzaton result shows that hgher S/O rato, load, temperature, and pressure ncrease the converson o ethylbenzene to styrene monomer and the producton rate o styrene monomer. Table alculated and speced values or constrants and obectve uncton onstrant urrent alculated operaton Startng Optmum S/O rato Load (%) Reactor nlet temperature ( ) Reactor nlet temperature ( ) Reactor outlet pressure (mmhg) rot onstrant range Operatng varable Mn Max S/O rato.0.0 Load (%) Reactor nlet temperature ( ) Reactor nlet temperature ( ) Reactor outlet pressure (mmhg) Obectve uncton: F = SMSALE EBVALUE ATVALUE + TLSALE STEAMVALUE SMSALE [$/day]=(sm prce [$/ton])*(outlet SM lowrate [ton/day]) EBVALUE [$/day]=(eb prce [$/ton])*((total EB nlet low rate [ton/day])-(recycled EB lowrate [ton/day]))+(eb recycle prce [$/ton])*(recycled EB lowrate [ton/day]) ATVALUE=(catalyst prce [$/day])*w TLSALE=(TL prce [$/ton])*(outlet TL lowrate [ton/day]) STEAMVALUE=(steam prce [$/day])*(nlet stream lowrate)*w (W & W : scalar weght) 5. ONLUSIONS In ths study, the styrene monomer producton process n an adabatc redal low reactor has been successully modeled. Ths model has been developed va hybrd model n whch the mathematcal model and neural network models are combned. Also the perormances o the developed model have been presented. The mathematcal model can renorce the nsucent perormance owng to lack o data that s not enough to use neural network models. And neural network model can gve more accurate catalyst deactvaton actor than one that a mathematcal model can gve. Although neural networks are not dcult to use, process varables should be known, and the qualty o data s crucal to obtan reasonable results. Usng ths model, determnng optmal operatng condtons and testng new operatng condtons are perormed easly. On the stuaton o changng catalyst, ths smulator shows good perormance because the catalyst parameters are updated usng current process data. 6. AKNOWLEDGEMENT Ths work was partally supported by the Bran Korea proect. Fundng and all plant normaton were provded by LG hemcals co. BZ p EB ET g NOMENLATURE Benzene Heat capacty o gas low [kj/(kg K)] Ethylbenzene Ethane Mass racton o component [kg/kg] Reacton rate o component [kmol/(m s)] Reacton rate o reacton [kmol/(m s)] F Total mass low rate [kg/s] H Hydrogen H O Water (steam) Enthalpy o reacton [kj/kmol] H k k 0 p omponent Reacton Rate constant o component Reerence rate constant o component k, 0 k 0 [Kmol/(kg s ka)]; k, 0 k, 40 k [Kmol/(kg s ka )]; 50 k [Kmol/(kg s ka )]) 60 K Equlbrum constant o man reacton [ka] m Number o reacton ME Methane MW Molecular weght o component [kg/kmol] N Number o component ressure [ka] artal pressure o component Inlet pressure [ka] n out Outlet pressure [ka] r Radal drecton [m] R Gas constant [ka m /(mol K)] SM Styrene monomer t Tme [day] T Temperature [K] TL Toluene Φ atalyst deactvaton actor

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