Final quality prediction method for new batch processes based on improved JYKPLS process transfer model

Data-driven methods have been successfully used in modern industrial production. The sufficient data is the basis for implementing these methods. However, it is often impossible to meet the requirement for a new industrial process. In this study, an improved JYKPLS (Joint-Y kernel partial least squa...

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Bibliographic Details
Published in:Chemometrics and intelligent laboratory systems 2018-12, Vol.183, p.1-10
Main Authors: Chu, Fei, Cheng, Xiang, Jia, Runda, Wang, Fuli, Lei, Meng
Format: Article
Language:English
Subjects:
Batch process
Data Elimination
JYKPLS
Model updating
Quality prediction
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Summary:Data-driven methods have been successfully used in modern industrial production. The sufficient data is the basis for implementing these methods. However, it is often impossible to meet the requirement for a new industrial process. In this study, an improved JYKPLS (Joint-Y kernel partial least squares) process transfer model is proposed to solve this issue and perform final product quality prediction for a new batch process. Based on the latent variable transfer technology, the rich information from similar old process data is transferred to accelerate the proceeding of building a new process model. The requirements on the amount of modeling data and prior knowledge of new processes are visibly reduced. Moreover, in order to handle the nonlinear correlation in process data, the kernel function is introduced to make data linear and separable. With actual productions operating, the transfer model is improved gradually by updating it with online data. When the prediction error falls into its confidence interval, the old data with lower similarity will be eliminated to avoid the negative transfer. The prediction results of penicillin concentration verify the effectiveness of the proposed method. •A novel process transfer method, Joint-Y kernel partial least squares, is proposed.•JYKPLS model is used to perform final quality prediction for a new batch process.•The requirements on the amount of data and prior knowledge are visibly reduced.•Kernel mapping is applied to cope with the nonlinearity in process transfer.•Prediction performance is further improved by model updating and data elimination.
ISSN:0169-7439
1873-3239
DOI:10.1016/j.chemolab.2018.10.004