Feature selection and Gaussian process prediction of rougher copper recovery

The emergence of advanced computational techniques provides opportunity in better modelling complex, nonlinear relationship of industrial processes. In this work, the performance of Gaussian Process Regression (GPR) models in predicting rougher copper recovery, following development with and without model-selected, relevant rougher flotation variables has been investigated. Randomly selected training and validation data sets as well as an independent testing data set (later than the original training and validation sets) were used to demonstrate the model predictive behaviour. The results showed that exponential GPR covariance function, modelled with selected relevant rougher flotation variables (throughput, feed particle size, xanthate dosage, frother dosage and froth depth), was the best model yielding correlation coefficient (r), root mean square error (RMSE), normalised root mean square error (NRMSE), variance accounted for (VAF) and performance index (PI) values of 0.99, 0.10, 0.01%, 99.99% and 1.97, respectively for training set. In the same manner, the validation set yielded 0.95, 0.40, 0.06%, 91.0%, and 1.75 whilst the testing set gave 0.96, 0.39, 0.07%, 92.10% and 1.78, confirming that exponential GPR algorithm can make good rougher copper recovery prediction given a set of useful rougher flotation variables. In all cases, the Regularised Neighbourhood Component Analysis algorithm performed best better in selecting useful flotation variables for rougher copper recovery prediction. Sobol's global sensitivity analysis indicated feed particle size as the most sensitive input variable recording first and total sobol indices values of ~63% and ~67%, respectively. Partial dependence plots revealed the functional relationship between the top two sensitive input variables (feed particle size and froth depth of tank cell 1) and predicted rougher copper recovery, suggesting their critical operating regime.