文献类型：期刊文献

英文题名：Data-Driven Soft Sensing for Batch Processes Using Neural Network-Based Deep Quality-Relevant Representation Learning

作者：Jiang, Qingchao[1,2]; Wang, Ziwen[1,2]; Yan, Shifu[1,2]; Cao, Zhixing[1,2]

机构：[1] East China University of Science and Technology, Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, Shanghai, 200237, China; [2] Tongji University, Shanghai Institute of Intelligent Science and Technology, Shanghai, 200092, China

年份：2023

卷号：4

期号：4

起止页码：602

外文期刊名：IEEE Transactions on Artificial Intelligence

收录：EI(收录号：20220611601236)

语种：英文

外文关键词：Batch data processing - Injection molding - Multilayer neural networks - Network layers - Process control - Redundancy

摘要：Soft sensors provide a means to reliably estimate unmeasurable variables, thereby playing a prevalent role in formulating closed-loop control in batch processes. In soft sensor development, enhancing quality-relevant information and eliminating quality-irrelevant information are important. This study proposes a neural network-based deep quality-relevant representation learning approach to improve the soft sensing performance in dynamic batch processes. The structure of a deep neural network is optimized in a layer-by-layer manner. First, given the generally abundant predictor variables, the maximal relevance-minimal redundancy criterion is used to optimize the input layer, select the most beneficial variables, and eliminate modeling redundancy. Second, mutual information-based quality-relevant representation selection is performed in the middle layers to enhance the quality-relevant information and eliminate the influence of irrelevant representations. Third, deep quality-relevant representations are extracted, and a soft sensor model is developed. The proposed method is tested on a fed-batch penicillin fermentation process and an industrial injection molding process. Lastly, it shows that the proposed method outperforms several state-of-The-Art approaches, thereby confirming its effectiveness. ? 2020 IEEE.