Research On Operational Optimization Control For Overall Process Of Hydrometallurgy

With the continuous advancement of industrialization,the high-grade non-ferrous metal resources in China are gradually exhausted.Hydrometallurgical technology can treat low-grade and complex non-ferrous metal mines.Compared with traditional high-emission smelting methods,it has the advantage of less environmental pollution.Hydrometallurgical technology has been widely used in the non-ferrous metal smelting industry.The hydrometallurgical production process has the characteristics of high complexity,strong coupling and nonlinearity.Under the influence of uncertain factors such as fluctuations in working conditions,the actual process often deviates from the set optimal operating state,resulting in the comprehensive production index not meeting expectations.Therefore,it is necessary to keep the production process in the optimal state by means of optimizing compensation,setting value adjustment and re-optimization.Taking a gold smelter hydrometallurgy production process as the background,this paper establishes a hydrometallurgical process optimization model based on the characteristics of each process and solves the optimal set value of decision variables through particle swarm optimization.According to the results of the hydrometallurgical optimal operation state evaluation,different optimization adjustment strategies are adopted to solve the problem that the actual process deviates from the optimal operation state.The main research contents of this paper include:1.According to the mechanism model of each process in the hydrometallurgical industry and the material conservation relationship between the processes,the whole process mechanism model of hydrometallurgy is obtained.By analyzing the influence of process variables on production indicators,a full-process optimization model with comprehensive economic benefits as the optimization goal is established,and the optimization model is solved by particle swarm optimization.The simulation results verify the effectiveness of the optimization method.2.On the basis of in-depth analysis of the influence of process variables on the running state of the whole process,he feature attributes of each state level data are extracted,and the process running state evaluation model is established.The rank of the process running state is determined by the similarity between the online data and each state level.This paper divides the hydrometallurgical production process into four grades:excellent,good,medium and poor.If the evaluation result of the operating state is "poor",the mechanism model is re-optimized.If the running status is evaluated as other results,different optimization strategies will be used to adjust the operating variable setting values.3.If the operating condition evaluation result is "excellent" or "good",the production process fluctuates within a small range around the optim al operating condition,and the self-optimizing control method is used for optimization compensation.By selecting the appropriate controlled variable,the set value of the controlled variable is tracked,and the optimal control of the whole process under the uncertain disturbance is realized.The simulation verifies the effecti veness of the optimized algorithm.4.If the operating condition evaluation result is"medium",the production process has deviated from the optimal working point,the whole process optimization and adjustment strategy of hydrometallurgy is adopted based on case-based reasoning.The source case that is most similar to the target case is matched in the case base of hydrometallurgy.If the similarity is greater than the threshold,the solution of the source case is reused.Otherwise,the case correction link is entered,and the adjustment value of the operation variable is obtained through the association rule mining algorithm,and the solution is solved.The problem of difficult to extract rules in case reasoning.