Affiliation:
1. Ulsan Ship and Ocean College Ludong University Yantai China
2. Shandong Marine Aerospace Equipment Technological Innovation Center Ludong University Yantai China
3. School of Information and Electrical Engineering Ludong University Yantai China
Abstract
AbstractData‐driven soft sensor models have been extensively utilized in industrial processes. Batch processes are usually employed to manufacture low‐volume and high value‐added products in chemical, materials, and pharmaceutical industries. The most distinctive features of batch process lie in nonlinear, repetition, and slow time varying characteristics. In this paper, a data‐driven soft sensor modelling method based on linear slow feature analysis (LSFA) and least squares support vector regression (LSSVR) is proposed. In this method, LSFA was used to effectively capture the driving force behind the data features that change as slowly as possible. Then, a LSSVR model was constructed between the extracted slow feature variables and quality variables. Finally, a numerical example, industrial penicillin fermentation processes, and cobalt oxalate synthesis process were utilized to confirm the prediction accuracy and model reliability of the proposed approach.
Funder
National Natural Science Foundation of China
Natural Science Foundation of Shandong Province
Subject
General Chemical Engineering
Cited by
1 articles.
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