Open-loop optimization for batch reaction systems: a case study

A comparison of the results obtained from deterministic and stochastic model-based optimization approaches for the determination of the optimal open-loop operating policy for a semi-batch reaction system are presented. The commercial synthesis of an intermediate in the production of the hypertension agent Eprosartan is considered in this case study. Identification of the reaction mechanism and estimation of the kinetic model parameters from experimental data are carried out as part of the development of a detailed semi-batch reaction system model that is used with the optimization approaches. The results of this study indicate that conversion optimization of the operating conditions can provide a good approximation to the economic optimum in the case of high conversion operation. This study also indicates that the optimal operating policy for the catalyst and reactant are batch, with the catalyst addition occurring prior to the reactant, and that a reduction in the amount of reactant from current practice is possible.

[1]  Mukul Agarwal.  Feasibility of on-line reoptimization in batch processes , 1997 .

[2]  Y. Arkun,et al.  Estimation and model predictive control of non-linear batch processes using linear parameter varying models , 1999 .

[3]  Kenneth R. Muske,et al.  Sensitivity analysis and stochastic optimization for open-loop batch operating policy determination , 2001, Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228).

[4]  David L. Ma,et al.  Worst‐case performance analysis of optimal batch control trajectories , 1999 .

[5]  Wolfgang Marquardt,et al.  Model predictive control for online optimization of semi-batch reactors , 1998, Proceedings of the 1998 American Control Conference. ACC (IEEE Cat. No.98CH36207).

[6]  Kenneth R. Muske,et al.  Monitoring process-scale reactions using API mass spectrometry , 1999 .

[7]  Berç Rustem,et al.  An algorithm for constrained nonlinear optimization under uncertainty , 1999, Autom..

[8]  Lazaros G. Papageorgiou,et al.  Robustness metrics for dynamic optimization models under parameter uncertainty , 1998 .

[9]  John R. Birge,et al.  Introduction to Stochastic Programming , 1997 .

[10]  D. Rippin,et al.  Semi-batch process optimization under uncertainty: Theory and experiments , 1998 .

[11]  P. Kebarle,et al.  Dependence of ion intensity in electrospray mass spectrometry on the concentration of the analytes in the electrosprayed solution , 1993 .

[12]  S. Palanki,et al.  A feedback-based implementation scheme for batch process optimization , 2000 .

[13]  James B. Rawlings,et al.  End-Point Control in Semi-Batch Chemical Reactors , 1989 .

[14]  Ioannis K. Kookos,et al.  An Algorithm for Simultaneous Process Design and Control , 2001 .

[15]  D. Rippin,et al.  Implementation of Adaptive Optimal Operation for a Semi-Batch Reaction System , 1998 .

[16]  Dominique Bonvin,et al.  Run-to-run optimization via control of generalized constraints , 2001 .

[17]  Efstratios N. Pistikopoulos,et al.  An optimization approach for process engineering problems under uncertainty , 1996 .

[18]  S. A. Dadebo,et al.  Dynamic optimization of constrained chemical engineering problems using dynamic programming , 1995 .

[19]  Dominique Bonvin,et al.  Performance Analysis of On-line Batch Optimization Systems , 1997 .

[20]  Babatunde A. Ogunnaike,et al.  Model-based quality monitoring of batch and semi-batch processes , 2000 .

[21]  Jay H. Lee,et al.  Model predictive control technique combined with iterative learning for batch processes , 1999 .

[22]  Peter Terwiesch,et al.  Cautious on-line correction of batch process operation , 1995 .

[23]  C. Párkányi.  Theoretical organic chemistry , 1998 .

[24]  Oskar von Stryk,et al.  Direct and indirect methods for trajectory optimization , 1992, Ann. Oper. Res..

[25]  Costas Kravaris,et al.  SYNTHESIS OF STATE FEEDBACK LAWS FOR END-POINT OPTIMIZATION IN BATCH PROCESSES , 1993 .

[26]  George M. Siouris,et al.  Applied Optimal Control: Optimization, Estimation, and Control , 1979, IEEE Transactions on Systems, Man, and Cybernetics.

[27]  W. J. Studden,et al.  Theory Of Optimal Experiments , 1972 .

[28]  D. Bonvin,et al.  Optimization of batch reactor operation under parametric uncertainty — computational aspects , 1995 .

[29]  Dominique Bonvin,et al.  Dynamic Optimization in the Batch Chemical Industry , 2001 .

[30]  Ignacio E. Grossmann,et al.  Design optimization of stochastic flexibility , 1993 .

[31]  Wolfgang Marquardt,et al.  Structural Concepts for Optimization Based Control of Transient Processes , 2000 .

[32]  Mayumi Tanaka,et al.  The Knoevenagel Reactions of Aldehydes with Carboxy Compounds. I. Reactions of p-Nitrobenzaldehyde with Active Methine Compounds , 1988 .

[33]  J. Rawlings,et al.  Feedback control of chemical processes using on-line optimization techniques , 1990 .

[34]  Mukul Agarwal,et al.  Batch unit optimization with imperfect modelling: a survey , 1994 .

[35]  G. C. Tiao,et al.  Bayesian inference in statistical analysis , 1973 .

[36]  Masoud Soroush,et al.  Optimal design and operation of batch reactors. 1. Theoretical framework , 1993 .

[37]  T. Brubaker,et al.  Nonlinear Parameter Estimation , 1979 .

[38]  W. E. Stewart,et al.  Sensitivity analysis of initial value problems with mixed odes and algebraic equations , 1985 .

[39]  B. Srinivasan,et al.  Economic Performance Analysis in the Design of On-line Batch Optimization Systems , 1999 .

[40]  Sirish L. Shah,et al.  ADAPTIVE CONTROL OF A BATCH REACTOR , 1985 .

[41]  Rein Luus,et al.  Optimal control of batch reactors by iterative dynamic programming , 1994 .

[42]  Michael G. Ikonomou,et al.  Investigations of the electrospray interface for liquid chromatography/mass spectrometry , 1990 .

[43]  Dominique Bonvin,et al.  Optimal operation of batch reactors—a personal view , 1998 .

[44]  J. E. Cuthrell,et al.  Simultaneous optimization and solution methods for batch reactor control profiles , 1989 .

[45]  J. Banga,et al.  Dynamic Optimization of Batch Reactors Using Adaptive Stochastic Algorithms , 1997 .

[46]  Elijah Polak,et al.  Optimization: Algorithms and Consistent Approximations , 1997 .

[47]  Konrad Hungerbühler,et al.  Economic Optimization of an Industrial Semibatch Reactor Applying Dynamic Programming , 1998 .

[48]  R. Sargent,et al.  Solution of a Class of Multistage Dynamic Optimization Problems. 2. Problems with Path Constraints , 1994 .

[49]  Wolfgang Marquardt,et al.  Productivity optimization of an industrial semi-batch polymerization reactor under safety constraints , 2000 .

[50]  Mukul Agarwal,et al.  Online correction of pre-optimized input profiles for batch reactors , 1994 .

[51]  Bjarne A. Foss,et al.  A field study of the industrial modeling process , 1998 .

[52]  Mukul Agarwal,et al.  ROBUST INPUT POLICIES FOR BATCH REACTORS UNDER PARAMETRIC UNCERTAINTY , 1995 .

[53]  B. Srinivasan,et al.  Optimization of a semi-batch reaction system under safety constraints , 1999, 1999 European Control Conference (ECC).