A method of hybrid intelligent optimal setting control for flotation process

In the flotation process, the concentrate grade and the tailing grade are crucial technical indices which reflect the product quality and efficiency. Such technical indices have close relationship with the reagent feeding, the air flowrate and the pulp level. There are strong nonlinearity and uncertainties in dynamic behavior, which can hardly be described using any accurate mathematical model. The technical indices which can not be measured online continuously vary with the variation of the slurry density, the slurry flowrate, the particle size and the ore grade, etc. Therefore conventional control methods are incapable of keeping the actual indices values within the target ranges. In this paper, a hybrid intelligent optimal setting method comprised of a pre-setting model based CBR and a feedback compensator model based RBR is proposed innovatively. The hybrid intelligent optimal setting control system updates adaptively the setpoints of control loops of the flotation reagent feeding, the air flowrate and flotation level as long as the boundary condition change so as to control the concentrate grade and the tailing grade within their respective target ranges. This method has been successfully applied to a flotation process in China, and significant application effect has been achieved. The successful application in a flotation process indicates that the proposed method has an extensive prospect in the application domain of the optimal control for the technical indices of the complex industrial process.