Pulp Chemistry Variables for Gaussian Process Prediction of Rougher Copper Recovery

Abstract

Insight about the operation of froth flotation through modelling has been in existence since the early 1930s. Irrespective of the numerous industrial models that have been developed over the years, modelling of the metallurgical outputs of froth flotation often do not involve pulp chemistry variables. As such, this work investigated the influence of pulp chemistry variables (pH, Eh, dissolved oxygen and temperature) on the prediction performance of rougher copper recovery using a Gaussian process regression algorithm. Model performance assessed with linear correlation coefficient (r), root mean square error (RMSE), mean absolute percentage error (MAPE) and scatter index (SI) indicated that pulp chemistry variables are essential in predicting rougher copper recovery, and obtaining r values > 0.98, RMSE values < 0.32, MAPE values < 0.20 and SI values < 0.0034. RNCA feature weights reveal the pulp chemistry relevance in the order dissolved oxygen > pH > Eh > temperature.