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A data-driven optimal control approach for solution purification process

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成果类型:
期刊论文
作者:
Sun, Bei;He, Mingfang;Wang, Yalin;Gui, Weihua;Yang, Chunhua;Zhu, Quanmin
通讯作者:
Yang, CH
作者机构:
[Yang, Chunhua; Gui, Weihua; Wang, Yalin; Sun, Bei] Cent S Univ, Sch Informat Sci & Engn, Changsha 410083, Hunan, Peoples R China.
[Zhu, Quanmin] Univ West England, Dept Engn Design & Math, Coldharbour Lane, Bristol BS16 1QY, Avon, England.
[Sun, Bei] Aalto Univ, Sch Chem Engn, Dept Chem & Met Engn, Espoo 02150, Finland.
[He, Mingfang] Cent South Univ Forestry & Technol, Sch Comp & Informat Engn, Changsha 410004, Hunan, Peoples R China.
通讯机构:
[Yang, Chunhua] Cent S Univ, Sch Informat Sci & Engn, Changsha 410083, Hunan, Peoples R China.
语种:
英文
关键词:
Solution purification process;Receding horizon control;Data-driven control;Adaptive dynamic programming;Process state space
期刊:
JOURNAL OF PROCESS CONTROL
ISSN:
0959-1524
年:
2018
卷:
68
页码:
171-185
文献类别:
WOS:Article
所属学科:
WOS学科类别:Automation & Control Systems;Engineering, Chemical
入藏号:
基金类别:
National Natural Science Foundation of China [61603418]; 111 Project [B17048]; Foundation for Innovative Research Groups of the National Natural Science Foundation of China [61621062]; State Key Program of National Natural Science of China [61533021]; Major Program of the National Natural Science Foundation of China [61590921]
机构署名:
本校为其他机构
院系归属:
计算机与信息工程学院
摘要:
Solution purification holds a critical position in hydrometallurgy. With its inherent complexity and the mixed raw material supply, solution purification process exhibits various working conditions, and has nonlinear, time-varying dynamics. At current stage, a comprehensive and precise model of a solution purification process is still costly to obtain. More specifically, the model structure could be derived by applying physical and chemical principles, while the accurate model parameters cannot be obtained under certain working conditions due to reasons like insufficient data samples. This, in turn, introduces obstacles in achieving the optimal operation. In order to circumvent the modeling difficulty, this paper proposes a 'Process State Space' descriptive system to re-describe the optimal control problem of solution purification process, accordingly establishes a two-layer receding horizon framework for developing a data-driven optimal control of solution purification process. In the optimal control scheme, on the 'optimization' layer, by utilizing the 'multiple-reactors' characteristic of solution purification process, a 'gradient' optimization strategy is proposed to transform the dosage minimization problem into obtaining the optimal variation gradient of the outlet impurity concentrations along the reactors. On the 'control' layer, a model-free input constrained adaptive dynamic programming algorithm is devised and applied to calculate the optimal dosages for each reactor by learning from the real-time production data. Case studies are performed to illustrate the effectiveness and efficiency of the proposed approach. The results and problems need future research are also discussed. (C) 2018 Elsevier Ltd. All rights reserved.
参考文献:
Abu-Khalaf M, 2005, AUTOMATICA, V41, P779, DOI 10.1016/j.automatica.2004.11.034
Antropov L. I., 1977, THEORETICAL ELECTROC, P403
Bei Sun, 2015, IFAC - Papers Online, V48, P80, DOI 10.1016/j.ifacol.2015.10.082
Bockman O, 2000, HYDROMETALLURGY, V54, P65, DOI 10.1016/S0304-386X(99)00059-6
CHEN S, 1991, IEEE T NEURAL NETWOR, V2, P302, DOI 10.1109/72.80341

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