Online prediction method of batch process product quality based on multi-scale kernel JYMKPLS transfer model

被引：0
|
作者：
Chu F. [1 ,2 ]
Peng C. [2 ]
Jia R. [3 ]
Chen T. [4 ]
Lu N. [5 ]
机构：
[1] Engineering Research Center of Ministry of Education for Intelligent Control of Underground Space, Xuzhou
[2] College of Information and Control Engineering, China University of Mining and Technology, Xuzhou
[3] Industrial Artificial Intelligence and Optimization Institute, Northeastern University, Shenyang
[4] Department of Chemical and Process Engineering, University of Surrey, Guildford
[5] College of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing
来源：
Chu, Fei (chufeizhufei@sina.com) | 1600年 / Materials China卷 / 72期
关键词：
Batchwise; Model; Multi-scale kernel; Prediction; Transfer learning;
D O I：
10.11949/0438-1157.20200995
中图分类号：
学科分类号：
摘要：
In view of the shortage of process data and the strong nonlinear and multi-scale characteristics of the new batch process, a product quality prediction method based on multi-scale kernel JYMKPLS (Joint-Y multi-scale kernel partial least squares) transfer learning is proposed, which combines the advantages of transfer learning and multi-scale kernel learning. First, the new batch process modeling efficiency and quality prediction accuracy are improved by using the old process data in the similar source domain through transfer learning. Then, in order to solve the problem of non-linear and multi-scale characteristics of the data, multi-scale kernel method is used to better fit the data features, so as to improve the prediction accuracy of the model. In addition, the online update and data elimination of the model are proposed to continuously improve the matching degree of the transfer model to the new batch process, so as to eliminate the adverse effects of the differences between similar processes on the transfer learning, so as to continuously improve the prediction accuracy. Finally, the effectiveness of the proposed method is verified by simulation. The results show that, compared with traditional data-driven modeling methods, the method proposed in this paper can effectively improve modeling efficiency and prediction accuracy. © 2021, Editorial Board of CIESC Journal. All right reserved.
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页码：2178 / 2189
页数：11
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