化工学报2018，Vol.69Issue(6)：2567-2575,9.DOI:10.11949/j.issn.0438-1157.20171388
基于过程迁移的间歇过程终点质量预报方法
Prediction approach for terminal batch process quality based on process transfer
摘要
Abstract
Adequate process data is the foundation for implementing a data-driven modeling approach. Nevertheless, it is often impossible to meet this requirement for a new batch process. A new quality prediction approach based on process transfer was proposed to establish an accurate prediction model for new batch processes without sufficient data. With application of multivariate statistical regression analysis model, JY-PLS (Joint-Y partial least squares) regression model, this approach realized rapid modeling and quality prediction of new batch processes by construction of common latent variable space between similar batch processes and transfer of present data information from similar batch processes to new and non-modeled batch processes. During online application, the process transfer model was updated with online data and simultaneously estimated confidence interval of prediction error to determine stability of the prediction error. In order to overcome adverse effects on process transfer model caused by possible differences between batch processes, similar process data was eliminated gradually according to data similarity. Finally, effectiveness of the proposed approach was verified by penicillin process simulation.关键词
间歇式/过程迁移/模型/预测/模型更新/数据剔除/算法Key words
batch-wise/process transfer/model/prediction/model update/data elimination/algorithm分类
信息技术与安全科学引用本文复制引用
褚菲,程相,代伟,赵旭,王福利..基于过程迁移的间歇过程终点质量预报方法[J].化工学报,2018,69(6):2567-2575,9.基金项目
国家自然科学基金项目(61503384,61603393) （61503384,61603393）
江苏省自然科学基金项目(BK20150199,BK20160275,BK20150204) （BK20150199,BK20160275,BK20150204）
中央高校基本科研业务费专项资金(2015QNA65) （2015QNA65）
江苏省博士后基金项目(1501081B).supported by the National Natural Science Foundation of China (61503384, 61603393), the Natural Science Foundation of Jiangsu Province (BK20150199, BK20160275, BK20150204), the Fundamental Research Funds for the Central Universities (2015QNA65) and the Jiangsu Provincal Postdoctoral Fund (1501081B). （1501081B）
