文章基本信息

标题：An adaptive soft sensor deterioration evaluation and model updating method for time-varying chemical processes
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作者：Longhao Li ; Yongshou Dai
期刊名称：Chemical Industry and Chemical Engineering Quarterly
印刷版ISSN：1451-9372
出版年度：2020
卷号：26
期号：2
页码：135-149
DOI：10.2298/CICEQ190419032L
出版社：Association of the Chemical Engineers
摘要：Due to the time-varying nature of chemical processes, soft sensor models deteriorate, and data prediction accuracy decreases. To address this problem, an adaptive soft sensor modeling method is proposed that not only evaluates the model deterioration by an adaptive moving window-constrained statistical hypothesis test, but also adaptively updates the modeling samples using moving window-cosine similarity. First, this method evaluates the model deterioration via positioning by constrained statistical hypothesis testing based on the differences between the prediction performance evaluation index data obtained from moving window stepping and the original prediction performance evaluation indexes. Additionally, the dynamic temporal variation in chemical processes causes changes in the impacts of the auxiliary variables on the dominant variable, and this effect limits the improvement in the prediction accuracy of the soft sensor model by updating only the auxiliary variable data. The moving window-cosine similarity method is combined to propose a strategy that updates both the modeled auxiliary variables and the auxiliary variable data. Finally, the parameters of the soft sensor model are optimized via particle swarm optimization (PSO) to improve the fitting performance. Simulated data of a continuous stirred tank reactor (CSTR) and actual data from a debutanizer column process (DCP) are used for model verification to evaluate the performance of the proposed adaptive soft sensor modeling method, and the results show its effectiveness.
关键词：chemical process; adaptive soft sensor model; statistical hypothesis test; moving window; cosine similarity; PSO