基于响应曲面法-随机森林的塔磨工艺多参数耦合优化与分选增效研究OA

As an efficient fine grinding equipment,the process parameters of tower mill have a significant impact on the degree of ore dissociation and subsequent separation indicator.However,there is a complex nonlinear coupling relationship between tower grinding process parameters,and it is difficult to fully reveal its mechanism by traditional single factor or empirical method optimization.In order to further explore the influence of tower grinding process parameters on the separation efficiency of iron concentrate and realize the optimization of process conditions,this paper takes an iron mine in Hebei Province as the research object,and uses the response surface method to study the interac-tion between different factors.In the model,the pulp mass fraction,medium filling rate,material-ball ratio and rota-tion rate were selected as the influencing factors,and the separation efficiency was used as the response value.Effect mechanism of the interaction between various factors on the separation efficiency was systematically investigated,and the corresponding prediction model was established.Based on the results of single factor exploration test,a four-factor and three-level optimization test scheme was designed,and the optimization test results were fitted.Finally,the regression model was used to optimize the tower grinding process parameters.The results show that the mass fraction of pulp and the ratio of material to ball are the main factors affecting the separation efficiency,and the interaction between the mass fraction of pulp and the filling rate of medium,the ratio of material to ball and the rotation rate is extremely significant.Under the optimized conditions,the predicted separation efficiency is 74.96％,the verification test value is 75.12％,and the relative error is only 0.21％.With the help of the nonlinear fitting and strong generali-zation ability of the random forest model,the prediction model of separation efficiency was constructed with the single factor and response surface test data as the training set.The R2 of the training set and the test set of the prediction model were 0.887 91 and 0.849 18,respectively.The predicted value was highly consistent with the measured value,which proved that the RSM-RF composite model could accurately predict the separation efficiency of iron ore.The research results can provide theoretical support and method reference for the optimization of tower mill process para-meters and the separation efficiency in industrial practice.