A Minimum-Cost Modeling Method for Nonlinear Industrial Process Based on Multimodel Migration and Bayesian Model Averaging Method
文献类型：期刊论文
| 作者 | Chu, Fei1,4,5,6,7; Dai, Bangwu3; Ma, Xiaoping2,7; Wang, Fuli3,8; Ye, Bin2,7 |
| 刊名 | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
 |
| 出版日期 | 2020-04-01 |
| 卷号 | 17期号:2页码:947-956 |
| ISSN号 | 1545-5955 |
| 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 |
| 通讯作者 | Chu, Fei(chufeizhufei@sina.com) |
| 英文摘要 | 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. |
| WOS关键词 | PREDICTIVE CONTROL ; MULTIPLE |
| 资助项目 | Fundamental Research Funds for the Central Universities[2019XKQYMS64] |
| WOS研究方向 | Automation & Control Systems |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:000528673100033 |
| 资助机构 | Fundamental Research Funds for the Central Universities |
| 源URL | [http://ir.ia.ac.cn/handle/173211/39397]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_智能化团队 |
| 通讯作者 | Chu, Fei |
| 作者单位 | 1.State Key Lab Proc Automat Min & Met, Beijing 100160, Peoples R China 2.China Univ Min & Technol, Sch Informat & Control Engn, Xuzhou 221116, Jiangsu, Peoples R China 3.Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110004, Peoples R China 4.Xuzhou Key Lab Artificial Intelligence & Big Data, Xuzhou 221116, Jiangsu, Peoples R China 5.China Univ Min & Technol, Natl Engn Res Ctr Coal Preparat & Purificat, Xuzhou 221116, Jiangsu, Peoples R China 6.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100080, Peoples R China 7.China Univ Min & Rechnol, Engn Res Ctr Intelligent Control Underground Spac, Minist Educ, Xuzhou 221116, Jiangsu, Peoples R China 8.Northeastern Univ, State Key Lab Synthet Automat Proc Ind, Shenyang 110819, Peoples R China |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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