Operating Performance Assessment Method and Application for Complex Industrial Process Based on ISDAE Model

被引：0
|
作者：
Chu F. [1 ,2 ]
Fu Y.-L. [1 ]
Zhao X. [1 ]
Wang P. [1 ]
Shang C. [3 ]
Wang F.-L. [4 ]
机构：
[1] Research Center of Underground Space Intelligent Control Engineering of the Ministry of Education, School of Information and Control Engineering, China University of Mining and Technology, Xuzhou
[2] State Key Laboratory of Process Automation in Mining & Metallurgy, Beijing Key Laboratory of Process Automation in Mining & Metallurgy, Beijing General Research Institute of Mining & Metallurgy, Beijing
[3] Department of Automation, Tsinghua University, Beijing
[4] State Key Laboratory of Integrated Automation for Process Industries, Northeastern University, Shenyang
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2021年 / 47卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Complex industrial process; Comprehensive economic index; ISDAE model; Non-optimal cause identification; Operating performance assessment;
D O I：
10.16383/j.aas.c200475
中图分类号：
学科分类号：
摘要：
The operating performance assessment of industrial process is of great significance to ensure the product quality and improve the comprehensive economic benefits of the enterprise. In view of the problems of strong process non-linearity, information redundancy and the influence of uncertainty factors in the complex industrial processes that are not conducive to establishing a robust and reliable operating performance assessment model, a comprehensive economic index driven sparse denoising autoencoder model (ISDAE) based operating performance assessment method is proposed for complex industrial processes. Firstly, SDAE (Sparse denoising autoencoder) is forced to learn data features related to comprehensive economic indexes by introducing comprehensive economic indexes prediction error term and a feature extraction model based on ISDAE is established. Secondly, the features learned from the ISDAE model will be used as input to train the operating performance identification model, and then the feature extraction model and performance assessment model are cascaded and the operating performance assessment model is obtained by fine-tuning the neural network. Then, for the non-optimal operating performance, a non-optimal cause identification method based on the autoencoder contribution plot algorithm is proposed, and the non-optimal cause is identified by calculating the contribution rate of the variables. Finally, the proposed method is applied to the dense medium coal preparation process to verify its effectiveness and practicability. Copyright © 2021 Acta Automatica Sinica. All rights reserved.
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页码：849 / 863
页数：14
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