Recovery and grade accurate prediction of pilot plant flotation column concentrate: Neural network and statistical techniques

In this study, the metallurgical performance (grade and recovery) forecasting of pilot plant flotation column using Artificial Neural Networks (ANN) and Multivariate Non-Linear Regression (MNLR) models is investigated. Modeling is performed based on 90 datasets at different operating conditions. The values of chemical reagents dosage, froth height, air and wash water flow rates, gas holdup and Cu, Mo grades in the rougher feed and flotation column feed, column tail and final concentrate streams are used to the simulation by means of NN and MNLR. The model validation analysis demonstrates the capability of both models to predict Cu and Mo grades and recoveries for a wide range of operating conditions in pilot flotation columns. It must be noted that ANN approach offers superior predictive capability over statistical method. It was also found that the error in prediction of metallurgical performance using the NN model was less than the error of the regression model. The best network is proposed with multi-layer perceptron (MLP) model, sigmoid activation function and Levenberg–Marquardt learning rule with 8-12-8-2 and 8-9-12-2 architectures, in order to estimate metallurgical performance of Cu and Mo respectively in flotation column. The results of this study indicate that a back-propagation neural network model with Root Mean Square Errors (RMSE) of 0.68 and 0.02 for prediction of Cu and Mo grades and 0.48 and 1.16 for prediction of Cu and Mo recoveries respectively has a better performance than the statistical method.