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Transfer learning for nonlinear batch process operation optimization
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DOI：10.1016/j.jprocont.2021.03.002.png)
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Abstract
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This paper concerns with the JY-KPLS model based transfer learning for the operation optimization of nonlinear batch processes. Due to problems of data insufficiency and uncertainties in a new nonlinear batch process that has just been put into production, the model-(new) process mismatch is usually inevitable, which is also the main reason for the poor performance of the batch process. To solve this problem, this paper first adopts the JY-KPLS model to capture the behavior of the nonlinear batch process, and takes full advantage of the information in similar batch processes to assist the modeling and operation optimization of a new process. Then, a data selection based batch-to-batch optimization control strategy is proposed in this paper to reduce the adverse effects of this mismatch on the operation of the new batch process. Finally, the feasibility of the proposed method is demonstrated by simulations. (C) 2021 Elsevier Ltd. All rights reserved.
Keywords:
Transfer learning
Nonlinear batch process
Insufficient data
Operation optimization
Data selection
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