文献类型：期刊文献

英文题名：Using Stacked Auto-Encoder and Bi-Directional LSTM For Batch Process Quality Prediction

作者：Qi, Jiakang[1];Luo, Na[1]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2021

卷号：54

期号：4

起止页码：144

外文期刊名：JOURNAL OF CHEMICAL ENGINEERING OF JAPAN

收录：;EI(收录号：20212210440409);WOS:【SCI-EXPANDED(收录号:WOS:000643562700004)】；

语种：英文

外文关键词：Batch Process; Quality Prediction; Stacked Auto-Encoder; Long Short-Term Memory

摘要：Batch process quality prediction has broad application prospects in manufacturing and chemical industries. However, during the final quality prediction of a batch process, the final target values may be related to the whole process track of the batch reaction. Thus, the final quality prediction problem embraces complex high-dimensional input and simple low-dimensional output, which also means a serious size mismatch between input data and predictive values. Motivated by these difficulties, a hybrid prediction model is proposed, which combines the advantages of stacked auto-encoder (SAE) and bi-directional long short-term memory (BLSTM) for the final quality prediction of a batch process. The feature extraction ability of SAE is used to obtain the low-dimensional features of historical process data along the time direction. Then, the validity of the framework was verified by taking penicillin fermentation as an example.