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.
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