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Batch-to-batch Optimal control of batch processes based on recursively updated nonlinear partial least squares models

Lookup NU author(s): Dr Jie ZhangORCiD



A batch-to-batch optimal control approach for batch processes based on batch-wise updated nonlinear partial least squares (NLPLS) models is presented in this article. To overcome the difficulty in developing mechanistic models for batch/semi-batch processes, a NLPLS model is developed to predict the final product quality from the batch control profile. Mismatch between the NLPLS model and the actual plant often exists due to low-quality training data or variations in process operating conditions. Thus, the optimal control profile calculated from a fixed NLPLS model may not be optimal when applied to the actual plant. To address this problem, a recursive nonlinear PLS (RNPLS) algorithm is proposed to update the NLPLS model using the information newly obtained after each batch run. The proposed algorithm is computationally efficient in that it updates the model using the current model parameters and data from the current batch. Then the new optimal control profile is recalculated from the updated model and implemented on the next batch. The procedure is repeated from batch to batch and, usually after several batches, the control profile will converge to the optimal one. The effectiveness of this method is demonstrated on a simulated batch polymerization process. Simulation results show that the proposed method achieves good performance, and the optimization with the proposed NLPLS model is more effective and stable than that with a batch-wise updated linear PLS model.

Publication metadata

Author(s): Li C, Zhang J, Wang G

Publication type: Article

Publication status: Published

Journal: Chemical Engineering Communications

Year: 2007

Volume: 194

Issue: 3

Pages: 261-279

Print publication date: 01/03/2007

ISSN (print): 0098-6445

ISSN (electronic): 1563-5201

Publisher: Taylor & Francis


DOI: 10.1080/00986440600829796


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