Optimization of Cement and Coal Consumption Based on CatBoost-PSO
In the actual production process of the cement industry,there are time-varying and ductile factors,as well as interference from too many uncertain factors in the production process.In order to reduce the actual coal con-sumption in cement production,a data-driven modeling optimization method for process parameters has been proposed.Firstly,feature selection was performed based on Pearson correlation coefficient and normalized variance.Then,random forest,XGBoost,LightGBM and CatBoost were used to predict coal consumption per ton,and TPE was used to opti-mize the regression model parameters.The R2 determination coefficients were increased by 0.009,0.0204,0.0033 and 0.0017 respectively.Finally,consider the iteration time to determine the use of CatBoost combined with particle swarm optimization algorithm to predict coal consumption per ton and optimize relevant feature parameters.Finally,the mini-mum coal consumption per ton was determined to be 77.91 kg and the optimal process parameters were determined.The impact of different parameters on coal consumption optimization was analyzed,among which the coal consumption rate of the decomposition furnace,the tertiary air temperature of the preheater,the C1 outlet pressure,and the main kiln speed have the greatest impact,with optimization weights above 10％.
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