基于神经网络预测的锌挥发率影响机制分析

The recovery and reuse of zinc and other valuable metals in leaching residues is a key segment in the green recycling of resources in the zinc hydrometallurgy industry. The typical process of zinc leaching residues treatment in rotary kilns is characterized by multivariate coupling, large delays, therefore, extensive energy consumption, unstable zinc volatilization rate and other problems arise, which is hard to be optimized rapidly and regulated immediately. The research object is about the recovery engineering of leaching slag in the large-scale rotary kiln of 300 000 tons/year in China. A particle swarm optimization BP neural network to predict the zinc volatilization rate had been established as a prioritization scheme in conjunction with a grey relational analysis of the main process parameters. Based on the single factor scenario analysis method, three model scenarios such as coke powder, kiln tail temperature, and mainly associated element of Fe content in the leaching slag had been set up, which were applied to analyze the trend and the impact mechanism of three aspects on zinc volatilisation rate. The results showed that the coke powder input intensity had the greatest influence on the zinc volatilisation rate and the correlation coefficient is 0.842. Meanwhile, the fit goodness of the PSO-BP (Particle Swarm Optimization Back Propagation) prediction model reached 0.987 and the prediction error is within ±0.6%, which achieved fast prediction of zinc volatilization rate and well solved the industrial process lag problem. The effect mechanism of coke powder input intensity, kiln tail temperature, and Fe content of the leaching residues on the volatility of zinc was illustrated in conjunction with the chemical reaction mechanism. Under the condition that the other influencing parameters were taken as the average of the sample data for the stable working conditions, the optimal simulation values for coke powder input intensity, kiln tail temperature, and Fe content of the leaching residues were 0.60 t/t, 680℃ and 23wt% . The theoretical guidance for the energy-efficient recovery of zinc from leaching residues and the optimal regulation of prevention and control of secondary pollution was demonstrated in the research.