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Self‐correcting modifier‐adaptation strategy for batch‐to‐batch optimization based on batch‐wise unfolded PLS model
Author(s) -
Jia Runda,
Mao Zhizhong,
Wang Fuli
Publication year - 2016
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.22565
Subject(s) - computer science , process (computing) , mathematical optimization , convergence (economics) , heuristic , iterative learning control , batch processing , adaptation (eye) , scheme (mathematics) , control (management) , mathematics , artificial intelligence , mathematical analysis , economics , programming language , economic growth , operating system , physics , optics
The problem of optimizing a batch process under model uncertainty using a batch‐wise unfolded PLS (BW‐PLS) model‐based modifier‐adaptation (MA) strategy is described. The main idea behind the strategy is to use measurements and iteratively modify the model to compensate for the mismatch of the necessary condition of optimality (NCO) between the plant and the model‐based optimization problem. It is proven that the popular data‐driven model‐based iterative learning control (ILC) strategy is equivalent to the proposed MA strategy using only zero‐order modifier. Inspired by the effectiveness of the ILC being enhanced by rebuilding the data‐driven model, a more elaborate model updating scheme is proposed in this paper to improve the optimization performances. The heuristic rules for choosing filtering gain matrix are also presented to further accelerate the convergence rate and reduce the variation of the cost during the period of evolution. Finally, the efficacy of the proposed MA strategy is illustrated via a simulated typical batch reaction and a simulated cobalt oxalate synthesis process.