A Minimum-Cost Modeling Method for Nonlinear Industrial Process Based on Multimodel Migration and Bayesian Model Averaging Method

doi:10.1109/TASE.2019.2952376

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 复杂系统智能机理与平行控制团队

	A Minimum-Cost Modeling Method for Nonlinear Industrial Process Based on Multimodel Migration and Bayesian Model Averaging Method
	Chu, Fei 1,2,3,4,5; Dai, Bangwu 6; Ma, Xiaoping 1,7; Wang, Fuli 6,8; Ye, Bin 1,7
发表期刊	IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
ISSN	1545-5955
	2020-04-01
卷号	17 期号:2 页码:947-956
通讯作者	Chu, Fei(chufeizhufei@sina.com)
摘要	With increasing drastic market competition, establishing an accurate and reliable performance prediction model for control and optimization at a minimum cost is a growing trend in industrial production. This article proposes a minimum-cost modeling method to develop the performance prediction model of a new nonlinear industrial process. The core idea of this approach is to migrate the useful information on multiple old and similar processes to develop a new process model. A multimodel migration strategy is proposed to migrate the useful information by combining the existing nonlinear process models and take full advantage of minimum data from the new nonlinear process. In order to obtain a set of optimal weights for combining the multiple old and similar process models, the Bayesian model averaging method is employed to estimate the contributions of each available old nonlinear process model to the new nonlinear process model. Moreover, a further experiment used nested Latin hypercube design (NLHD) to gather the necessary minimum data on the new nonlinear process for model migration. Finally, we apply the proposed minimum-cost modeling method to the new multistage centrifugal compressor in the combined cycle power plant, and the results show that the proposed method can develop an accurate compressor model at a minimal cost in terms of the amount of new process data. Note to Practitioners-Process optimal control and condition monitoring are vital for the stability and economic operation of industrial processes, and the basis of them is to quickly establish an accurate and reliable process performance prediction model. Traditional methods for developing process performance prediction models often require a large amount of complex calculations and rich process data, which is time- and cost-consuming. In particular, these methods focus only on the current process to be modeled, while ignoring the existing and similar process information, wasting process information. This article presents a minimum-cost modeling method for nonlinear industrial processes, which can make full use of information on multiple similar existing processes to assist the modeling of a new process to reduce the modeling cost of the new process. Specifically, a multimodel migration strategy including Bayesian model averaging is designed to migrate useful information from similar processes to the new process. The nested Latin hypercube design (NLHD) is employed to collect the necessary minimum data on the new nonlinear process. By applying the proposed approach to the industrial nonlinear process, it is possible to achieve the accurate performance prediction model with minimal new process data.
关键词	Predictive models Data models Adaptation models Process control Computational modeling Integrated circuit modeling Bayes methods Bayesian model averaging (BMA) minimum cost multimodel migration nonlinear performance prediction
DOI	10.1109/TASE.2019.2952376
关键词[WOS]	PREDICTIVE CONTROL ; MULTIPLE
收录类别	SCI
语种	英语
资助项目	Fundamental Research Funds for the Central Universities[2019XKQYMS64]
项目资助者	Fundamental Research Funds for the Central Universities
WOS研究方向	Automation & Control Systems
WOS类目	Automation & Control Systems
WOS记录号	WOS:000528673100033
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/39398
专题	多模态人工智能系统全国重点实验室_复杂系统智能机理与平行控制团队
通讯作者	Chu, Fei
作者单位	1.China Univ Min & Rechnol, Engn Res Ctr Intelligent Control Underground Spac, Minist Educ, Xuzhou 221116, Jiangsu, Peoples R China 2.State Key Lab Proc Automat Min & Met, Beijing 100160, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100080, Peoples R China 4.China Univ Min & Technol, Natl Engn Res Ctr Coal Preparat & Purificat, Xuzhou 221116, Jiangsu, Peoples R China 5.Xuzhou Key Lab Artificial Intelligence & Big Data, Xuzhou 221116, Jiangsu, Peoples R China 6.Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110004, Peoples R China 7.China Univ Min & Technol, Sch Informat & Control Engn, Xuzhou 221116, Jiangsu, Peoples R China 8.Northeastern Univ, State Key Lab Synthet Automat Proc Ind, Shenyang 110819, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Chu, Fei,Dai, Bangwu,Ma, Xiaoping,et al. A Minimum-Cost Modeling Method for Nonlinear Industrial Process Based on Multimodel Migration and Bayesian Model Averaging Method[J]. IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING,2020,17(2):947-956.
APA	Chu, Fei,Dai, Bangwu,Ma, Xiaoping,Wang, Fuli,&Ye, Bin.(2020).A Minimum-Cost Modeling Method for Nonlinear Industrial Process Based on Multimodel Migration and Bayesian Model Averaging Method.IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING,17(2),947-956.
MLA	Chu, Fei,et al."A Minimum-Cost Modeling Method for Nonlinear Industrial Process Based on Multimodel Migration and Bayesian Model Averaging Method".IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 17.2(2020):947-956.