Asynchronous Fuzzy Cognitive Networks Modeling and Control for Goethite Iron Precipitation Process
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Asynchronous Fuzzy Cognitive Networks Modeling and Control for Goethite Iron Precipitation Process∗ CHEN Ning · PENG Junjie · GUI Weihua · ZHOU Jiaqi · DAI Jiayang
DOI: 10.1007/s11424-020-9120-1 Received: 10 April 2019 / Revised: 31 December 2019 c The Editorial Office of JSSC & Springer-Verlag GmbH Germany 2020 Abstract Goethite iron precipitation process is a key step in direct leaching process of zinc, whose aim is to remove ferrous ions from zinc sulphate solution. The process consists of several cascade reactors, and each of them contains complex chemical reactions featured by strong nonlinearity and large time delay. Therefore, it is hard to build up an accurate mathematical model to describe the dynamic changes in the process. In this paper, by studying the mechanism of these reactions and combining historical data and expert experience, the modeling method called asynchronous fuzzy cognitive networks (AFCN) is proposed to solve the various time delay problem. Moreover, the corresponding AFCN model for goethite iron precipitation process is established. To control the process according to fuzzy rules, the nonlinear Hebbian learning algorithm (NHL) terminal constraints is firstly adopted for weights learning. Then the model parameters of equilibrium intervals corresponding to different operating conditions can be calculated. Finally, the matrix meeting the expected value and the weight value of steady states is stored into fuzzy rules as prior knowledge. The simulation shows that the AFCN model for goethite iron precipitation process could precisely describe the dynamic changes in the system, and verifies the superiority of control method based on fuzzy rules. Keywords Asynchronous fuzzy cognitive networks, fuzzy rules database, Goethite iron precipitation process, Hebbian learning.
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Introduction
Zinc is an important non-ferrous metal raw material. Currently, the iron precipitation method by leaching zinc sulfide in acid solution to recycle elemental sulfur process is one of CHEN Ning · PENG Junjie · GUI Weihua · ZHOU Jiaqi School of Automation, Central South University, Changsha 410083, China. Email: [email protected]; [email protected]; [email protected]. DAI Jiayang School of Automation, Central South University, Changsha 410083, China; School of Electrical Engineering, Guangxi University, Nanning 530004, China. Email: [email protected]. ∗ This work was supported in part by the Program of the National Natural Science Foundation of China under Grant No. 61673399, in part by the Program of National Natural Science Foundation of Hunan Province under Grant No. 2017JJ2329, and in part by Fundamental Research Funds for Central Universities of Central South University under Grant No. 2018zzts550. This paper was recommended for publication by Editor SUN Jian.
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CHEN NING, et al.
the best zinc hydrometallurgy methods and also a process of obtaining zinc sulphate which is adopted by most zinc smelting enterprises[1] . Zinc sulfate solution contains a large number of iron ions. If iron ions concentration
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