Prediction of Pulp Concentration Using Extreme Learning Machine

Abstract

Pulp concentration is one of the most important production parameters during ore dressing process. Generally, pulp concentration not only affects concentrate grade and recovery rate, but also has a major influence on the chemical and power consumptions during the flotation process. Recently, there has been a growing interest in the study of prediction for pulp concentration to improve the productivity and reduce consumption of various resources. Since the pulp concentration and other production parameters are nonlinearly related, it imposes very challenging obstacles to the prediction for this parameter. Because extreme learning machine (ELM) has the advantages of extremely fast learning speed, good generalization performance, and the smallest training errors, we employ ELM to predict pulp concentration in this paper. Pulp concentration data is first preprocessed using phase space reconstruction method. Then time series prediction model is adjusted from one dimension to multiple dimensions and thus it is established by several improved ELM algorithms, including traditional ELM, kernel-based ELM (Kernel-ELM), regularized ELM (R-ELM), and \(L_2\)-norm based ELM (ELM-L2). The experiments are conducted with a real-world production data set from a mine. The experimental results show the effectiveness of ELM-based prediction approaches, and we can also find that ELM-L2 has better prediction effects than other algorithms with the increase of sample size. Both training speed and prediction accuracy are improved by employing ELM-L2 to the prediction of pulp concentration.