Operating condition recognition based on temporal cumulative distribution function and AdaBoost-extreme learning machine in zinc flotation process

Abstract Bubble size distribution (BSD) is a crucial indicator of operating conditions in froth flotation. However, current BSD-based recognition methods have some limitations in the BSD estimation and don't achieve satisfactory results. Therefore, an alternative operating condition recognition method is proposed. Firstly, the temporal cumulative distribution function (TCDF) is designed to represent the BSD on the froth video. Then, the piecewise basis function is established to fit the TCDF. Secondly, the TCDF-Minimum Redundancy Maximum Relevancy (MRMR) feature is generated by MRMR to increase the sensitivity of TCDF features to the operating conditions. Lastly, the proposed TCDF-MRMR feature together with froth velocity and texture features are fed into an AdaBoost-extreme learning machine (ELM) model to recognize the operating conditions. Experiments in a zinc flotation process demonstrate that the TCDF-MRMR feature is more effective than other BSD-based features, and the AdaBoost-ELM shows better performance than traditional single recognition models.