A fuzzy control method based on rule extraction for the zinc leaching process of zinc hydrometallurgy

In the neutral leaching process of zinc hydrometallurgy, the pH value directly affects the leaching rate of zinc calcine. However, due to the complex mechanism of the leaching process and frequent fluctuation of working conditions, it is difficult to control the pH value by manual control. Therefore, this study proposes a fuzzy control method based on rule extraction under multiple working conditions, and analyzes the pH control performance. Firstly, the different operating states are classified according to process mechanisms and the Pearson method. Secondly, under the framework of the active learning algorithm, the approximate linear dependence (ALD) method is introduced to iteratively select samples with rule information in the original sample set for manual annotation. Then, the support vector machine (SVM) method is used to extract fuzzy control rules in different working conditions. Finally, a fuzzy rule set is established according to the samples mapped by the support vector. The simulation results based on industrial data show that the proposed method has better control performance than conventional fuzzy control and proportional–integral–derivative (PID) control.