A Real-time Updated Model Predictive Control Strategy for Batch Processes Based on State Estimation

Nonlinear model predictive control （NMPC） is an appealing control technique for improving the per- formance of batch processes, but its implementation in industry is not always possible due to its heavy on-line computation. To facilitate the implementation of NMPC in batch processes, we propose a re...

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Bibliographic Details
Published inChinese journal of chemical engineering Vol. 22; no. 3; pp. 318 - 329
Main Author 杨国军 李秀喜 钱宇
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.03.2014
Subjects
batch process
exothermic batch
model update
nonlinear model predictive control
reactor
real-time
state estimation
在线计算
实时更新
更新策略
状态估计
间歇式反应器
间歇过程
非线性模型预测控制
预测控制策略
model update
real-time
batch process
reactor
state estimation
nonlinear model predictive control
exothermic batch
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ISSN1004-9541
2210-321X
DOI10.1016/S1004-9541(14)60057-4

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Summary:Nonlinear model predictive control （NMPC） is an appealing control technique for improving the per- formance of batch processes, but its implementation in industry is not always possible due to its heavy on-line computation. To facilitate the implementation of NMPC in batch processes, we propose a real-time updated model predictive control method based on state estimation. The method includes two strategies： a multiple model building strategy and a real-time model updated strategy. The multiple model building strategy is to produce a series of sim- plified models to reduce the on-line computational complexity of NMPC. The real-time model updated strategy is to update the simplified models to keep the accuracy of the models describing dynamic process behavior. The method is validated with a typical batch reactor. Simulation studies show that the new method is efficient and robust with respect to model mismatch and changes in process parameters.
Bibliography:batch process, exothermic batch reactor, nonlinear model predictive control, state estimation, real-time model update
Nonlinear model predictive control （NMPC） is an appealing control technique for improving the per- formance of batch processes, but its implementation in industry is not always possible due to its heavy on-line computation. To facilitate the implementation of NMPC in batch processes, we propose a real-time updated model predictive control method based on state estimation. The method includes two strategies： a multiple model building strategy and a real-time model updated strategy. The multiple model building strategy is to produce a series of sim- plified models to reduce the on-line computational complexity of NMPC. The real-time model updated strategy is to update the simplified models to keep the accuracy of the models describing dynamic process behavior. The method is validated with a typical batch reactor. Simulation studies show that the new method is efficient and robust with respect to model mismatch and changes in process parameters.
YANG Guojun LI Xiuxi and QIAN Yu School of Chemical Engineering, South China University of Technology, Guangzhou 510640, China
11-3270/TQ
ISSN:1004-9541
2210-321X
DOI:10.1016/S1004-9541(14)60057-4