Probability density function of bubble size based reagent dosage predictive control for copper roughing flotation |
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| Affiliation: | 1. School of Information Science and Engineering, Central South University, Hunan 410083, China;2. Nanchang Campus, JiangXi University of Science and Technology, Nanchang 330029, China;3. Department of Mathematics and Statistics, Curtin University, Perth, WA 6845, Australia;1. University HASSAN II Casablanca-Mohammedia, FSBM, LTI Lab, Morocco;2. University of Caen Basse-Normandie, GREYC Lab UMR CNRS, 14032 Caen, France;3. University of Rabat-Souissi, ENSET, Rabat, Morocco;1. Department of Mining Engineering, Universidad de Chile, Chile;2. Advanced Mining Technology Center, AMTC, Universidad de Chile, Chile;1. School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, PR China;2. Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XW, UK;3. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, PR China;1. Outotec (Finland) Oy, Rauhalanpuisto 9, 02230 Espoo, Finland;2. Boliden Kevitsa Mine, Kevitsantie 730, 99670 Petkula, Finland |
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| Abstract: | As an effective measurement indicator of bubble stability, bubble size structure is believed to be closely related to flotation performance in copper roughing flotation. Moreover, reagent dosage has a very important influence on bubble size structure. In this paper, a novel reagent dosage predictive control method based on probability density function (PDF) of bubble size is proposed to implement the indices of roughing circuit. Firstly, the froth images captured in the copper roughing are segmented by using a two-pass watershed algorithm. In order to characterize bubble size structure with non-Gaussian feature, an entropy based B-spline estimator is hence investigated to depict the PDF of the bubble size. Since the weights of B-spline are interrelated and related to the reagent dosage, a multi-output least square support vector machine (MLS-SVM) is applied to depict a dynamic relationship between the weights and the reagent dosage. Finally, an entropy based optimization algorithm is proposed to determine reagent dosage in order to implement tracking control for the PDF of the output bubble size. Experimental results can show the effectiveness of the proposed method. |
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| Keywords: | Froth flotation Reagent dosage Predictive control Bubble size Probability density function MLS-SVM |
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