Reagent Predictive Control Using Joint Froth Image Feature for Antimony Flotation Process

Abstract

The prerequisite for achieving automatic control and optimal operation in flotation process is to extract distinctive froth image features. With the fluctuation of flotation conditions, the morphological features of surface bubbles will change. It is not reliable to impose bubble size distribution as the decisive character to represent the complex flotation conditions. Therefore, a novel joint froth image feature was proposed via analysing bubble size and shape simultaneously. To consist with the observation process of operators in plant and overcome the deficiency of bubble samples, splicing froth images from sequential frames were segmented to obtain the joint distribution and a nonparametric estimation using B-spline functions was introduced to depict the shape of distribution. Then a multi-output least square support vector regressor (MLS-SVR) was implemented to construct the relationship between the weights of the B-spline functions and the reagent dosage. Finally, a joint feature based reagent predictive controller was constructed to validate the proposed method. Actual industrial data experiment results have shown its effectiveness and feasibility.