Konferensartikel

Modeling and Simulation of a Semi-batch Reactor

Anna Nyström
Mathematical Sciences, Chalmers University of Technology and Mathematical Sciences, Göteborg University, Sweden

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Ingår i: The 48th Scandinavian Conference on Simulation and Modeling (SIMS 2007); 30-31 October; 2007; Göteborg (Särö)

Linköping Electronic Conference Proceedings 27:21, s. 173-182

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Publicerad: 2007-12-21

ISBN:

ISSN: 1650-3686 (tryckt), 1650-3740 (online)

Abstract

The operation of an industrial semi-batch reactor; in which the bulk chemical EHEC; ethyl hydroxyethyl cellulose; is produced; is studied and simulated. In the reactor a strongly exothermic polymerization reaction takes place followed by a slightly exothermic reaction; and we want to minimize the duration of the operation of the process. Various operational as well as quality and safety related constraints have to be met during the batch. The complete process model; derived from measurements; first principles; and reasoning about effects on molecular level; is stated. The model includes heat and mass balances of the reactor; a pressure model; models of PID controllers; the jacket and the condenser. Technical limitations; for instance maximal and minimal jacket temperature changes due to limitations in the heat exchanger; have been modeled as constraints.

The equations have been implemented in SIMULINK; MATLAB and the model predicts the process variables rather well over time. During the first reaction; the model is not able to reproduce the jacket temperature to the desired accuracy; but the other variables have acceptable predictions. An optimization problem is formulated; wherein the total batch time is minimized under the constraints of the differential algebraic equation system and other constraints originating from the process; for instance limited pump capabilities.

As a first step in optimizing the operation of the process; a series of simulations has been performed in order to decrease the total batch time. It is concluded that a 10 % shorter batch time than today is possible if the quality is discarded; and a 5 % shorter batch time can be reached while using the existing requirements for the quality.

Nyckelord

Semi-batch reactor; simulation; optimization

Referenser

[1] O. Abel; A. Helbig;W.Marquardt; H. Zwick; and T. Daszkowski. Productivity optimization of an industrial semi-batch polymerization reactor under safety constraints. Journal of Process Control; 10:351–362; 2000.

[2] L. T. Biegler; A. M. Cervantes; and A. W¨achter.Advances in simultaneous strategies for dynamic process optimization. Chemical Engineering Science; 57:575–593; 2002.

[3] D. Bonvin. Optimal operation of discontinuousreactors - A personal view. Journal of Process Control; 8:355–368; 1998.

[4] D. Bonvin; B. Srinivasan; and D. Ruppen. Dynamic optimization in the batch chemical industry. Aiche Symposium Series; 326:255–273; 2002.

[5] A. M. Cervantes and L. T. Biegler. A stable elementaldecomposition for dynamical process optimization. Journal of Computational and Applied Mathematics; 120:41–57; 2000.

[6] A. M. Cervantes and L. T. Biegler. Optimization strategies for dynamic systems. In C. Floudas and P. Pardalos; editors; Encyclopedia of Optimization; volume 4; pages 216–227. Kluwer; New York; 2001.

[7] F. A. D’Angelo; L Brunet; P Cognet; and M. Cabassud. Modelling and constraint optimisation of an aromatic nitration in liquid-liquid medium. Chemical Engineering Journal; 91:75–84; 2003.

[8] I. Datkov; G. M. Ostrovsky; L. E. K. Achenie; and Y. M. Volin. Process optimization under uncertainty when there is not enough process data at the operation stage. Optimization and Engineering; 7:249–276; 2006.

[9] S.M. Khuu; J. A. Rodriguez; J. A. Romagnoli; and K. F. Ngian. Optimisation and control of an industrial surfactant reactor. Computers & Chemical Engineering; 24:863–870; 2000.

[10] D. B. Leineweber; A. Sch¨afer;H. G. Bock; and J. P. Schl ¨oder. An efficient multiple shooting based SQP strategy for large-scale dynamic process optimization; Part II: Software aspects and applications. Computers & Chemical Engineering; 27:167– 174; 2003.

[11] Z. K. Nagy and R. D. Braatz. Open-loop and closed-loop robust optimal control of batch processes using distributional and worst case analysis. Journal of Process Control; 14:411–422; 2004.

[12] R. H. Perry and D. W. Green. Perry’s Chemical Engineers’ Handbook. McGraw-Hill; New York; 7 edition; 1997.

[13] V. Pontryagin; V. Boltyanskii; R. Gamkrelidge; and E. Mishchenko. The Mathematical Theory of Optimal Processes. New York: Interscience Publishers Inc.; 1962.

[14] D. W. T. Rippin. Simulation of single- and multiproduct batch chemical plants for optimal design and operation. Computers & Chemical Engineering; 7:137–156; 1983.

[15] B. Srinivasan; D. Bonvin; E. Visser; andS. Palanki. Dynamic optimization of batch processes: II. Role of measurements in handling uncertainty. Computers & Chemical Engineering; 27:27–44; 2002.

[16] P. Terwiesch; M. Agarwal; and D. W. T. Rippin. Batch unit optimizationwith imperfectmodelling: A survey. Journal of Process Control; 4:238– 258; 1994.

[17] P. Terwiesch; D. Ravemark; B. Schenker; and D. W. T. Rippin. Semi-batch process optimization under uncertainty: Theory and experiments. Computers & Chemical Engineering; 22:201–213; 1998.

[18] Z. Verwater-Lukszo. A practical approach to recipe improvement and optimization in the batch processing industry. Computers in Industry; 36:279–300; 1998.

[19] V. M. Zavala; A. Flores-Tlacuahuac; and E. Vivaldo-Lima. Dynamic optimization of a semi-batch reactor for polyurethane production. Chemical Engineering Science; 60:3061–3079; 2005. 182

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