Online complex nonlinear industrial process operating optimality assessment using modified robust total kernel partial M-regression

doi:10.1016/j.cjche.2017.06.019

Chinese Journal of Chemical Engineering ›› 2018, Vol. 26 ›› Issue (4): 775-785.DOI: 10.1016/j.cjche.2017.06.019

• Process Systems Engineering and Process Safety • 上一篇下一篇

Online complex nonlinear industrial process operating optimality assessment using modified robust total kernel partial M-regression

Fei Chu¹, Wei Dai¹, Jian Shen¹, Xiaoping Ma¹, Fuli Wang^2,3

1 School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China;
2 State Key Laboratory of Integrated Automation for Process Industries, Northeastern University, Shenyang 110819, China;
3 College of Information Science and Engineering, Northeastern University, Shenyang 110819, China

收稿日期:2016-11-19 修回日期:2017-06-28 出版日期:2018-04-28 发布日期:2018-05-19
通讯作者: Fei Chu,E-mail addresses:chufeizhufei@sina.com,chufei@cumt.edu.cn
基金资助:
Supported by the National Natural Science Foundation of China (61503384, 61603393), Natural Science Foundation of Jiangsu (BK20150199, BK20160275), the Foundation Research Funds for the Central Universities (2015QNA65), the Postdoctoral Foundation of Jiangsu Province (1501081B).

Online complex nonlinear industrial process operating optimality assessment using modified robust total kernel partial M-regression

Fei Chu¹, Wei Dai¹, Jian Shen¹, Xiaoping Ma¹, Fuli Wang^2,3

1 School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China;
2 State Key Laboratory of Integrated Automation for Process Industries, Northeastern University, Shenyang 110819, China;
3 College of Information Science and Engineering, Northeastern University, Shenyang 110819, China

Received:2016-11-19 Revised:2017-06-28 Online:2018-04-28 Published:2018-05-19
Contact: Fei Chu,E-mail addresses:chufeizhufei@sina.com,chufei@cumt.edu.cn
Supported by:
Supported by the National Natural Science Foundation of China (61503384, 61603393), Natural Science Foundation of Jiangsu (BK20150199, BK20160275), the Foundation Research Funds for the Central Universities (2015QNA65), the Postdoctoral Foundation of Jiangsu Province (1501081B).

摘要/Abstract

摘要： Although industrial processes often perform perfectly under design conditions, they may deviate from the optimal operating point owing to parameters drift, environmental disturbances, etc. Thus, it is necessary to develop efficacious strategies or procedure to assess the process performance online. In this paper, we explore the issue of operating optimality assessment for complex industrial processes based on performance-similarity considering nonlinearities and outliers simultaneously, and a general enforced online performance assessment framework is proposed. In the offline part, a new and modified total robust kernel projection to latent structures algorithm, T-KPRM, is proposed and used to evaluate the complex nonlinear industrial process, which can effectively extract the optimal-index-related process variation information from process data and establish assessment models for each performance grades overcoming the effects of outlier. In the online part, the online assessment results can be obtained by calculating the similarity between the online data from a sliding window and each of the performance grades. Furthermore, in order to improve the accuracy of online assessment, we propose an online assessment strategy taking account of the effects of noise and process uncertainties. The Euclidean distance between the sliding data window and the optimal evaluation level is employed to measure the contribution rates of variables, which indicate the possible reason for the non-optimal operating performance. The proposed framework is tested on a real industrial case:dense medium coal preparation process, and the results shows the efficiency of the proposed method comparing to the existing method.

关键词: Performance assessment, Optimization, Model, Economics, T-KPRM, Robust

Abstract: Although industrial processes often perform perfectly under design conditions, they may deviate from the optimal operating point owing to parameters drift, environmental disturbances, etc. Thus, it is necessary to develop efficacious strategies or procedure to assess the process performance online. In this paper, we explore the issue of operating optimality assessment for complex industrial processes based on performance-similarity considering nonlinearities and outliers simultaneously, and a general enforced online performance assessment framework is proposed. In the offline part, a new and modified total robust kernel projection to latent structures algorithm, T-KPRM, is proposed and used to evaluate the complex nonlinear industrial process, which can effectively extract the optimal-index-related process variation information from process data and establish assessment models for each performance grades overcoming the effects of outlier. In the online part, the online assessment results can be obtained by calculating the similarity between the online data from a sliding window and each of the performance grades. Furthermore, in order to improve the accuracy of online assessment, we propose an online assessment strategy taking account of the effects of noise and process uncertainties. The Euclidean distance between the sliding data window and the optimal evaluation level is employed to measure the contribution rates of variables, which indicate the possible reason for the non-optimal operating performance. The proposed framework is tested on a real industrial case:dense medium coal preparation process, and the results shows the efficiency of the proposed method comparing to the existing method.

Key words: Performance assessment, Optimization, Model, Economics, T-KPRM, Robust

Fei Chu, Wei Dai, Jian Shen, Xiaoping Ma, Fuli Wang. Online complex nonlinear industrial process operating optimality assessment using modified robust total kernel partial M-regression[J]. Chinese Journal of Chemical Engineering, 2018, 26(4): 775-785.

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Online complex nonlinear industrial process operating optimality assessment using modified robust total kernel partial M-regression

Online complex nonlinear industrial process operating optimality assessment using modified robust total kernel partial M-regression

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