Study on prediction of supernatant turbidity in fine mineral settling based on machine learning

In order to improve the settling efficiency of coal slurry water, tackle the problems of agent waste and high environmental costs caused by manual dosage relying on experience, and realize the accurate prediction of supernatant turbidity, a machine learning based method for predicting the turbidity of fine mineral settling supernatant is proposed. This study focuses on the supernatant turbidity after settling tests of quartz and montmorillonite, selecting magnesium chloride, calcium chloride, alum, polyaluminum chloride, and gypsum as coagulants, six machine learning prediction models including support vector regression, decision tree, k-nearest neighbors, random forest, linear regression, and multi-layer perceptron are constructed, the model performance is evaluated by MSE, RMSE, MAE, MAPE, R² indexes, the effects of coagulant types and pulp concentration on the turbidity of supernatant are studied and the fitting effect of these models is evaluated. The results show that the settling effects of quartz and montmorillonite pulps vary with different coagulants, and the same coagulant also has different effects on the settling trends of pulps with different concentrations, the optimal coagulant for both is alum; comparing the prediction results of machine learning algorithm with the experimental results, the sequence of model fitting accuracy from high to low is multilayer perceptron, linear regression, random forest, k-nearest neighbor, decision tree and support vector regression model; multi-layer perceptron model has high accuracy in predicting the supernatant turbidity of quartz, montmorillonite and other fine minerals, the R² value of the model reaches 0.99, and compare with the linear regression model, RMSE and MAE are reduced by 89.02% and 87.25%, respectively. The results can provide theoretical guidance and technical support for the development of intelligent dosing system in coal slurry water treatment.