Optimal control of the raw slurry blending process based on the model and neural network

Raw slurry blending process is a key unit in the sintering alumina industry. The optimal control objective of this blending process is to make the quality indices of the raw slurry into their targeted ranges. Flow rates of raw materials are the key factors that affect the quality indices of raw slurry. How to obtain the appropriate set-points of flow rates is the key problem in the optimal control. An intelligent optimal control method, which is comprised of the setting layer and the loop control layer, is proposed. In the setting layer, mathematical model and neural network are adopted to obtain the appropriate set-points of the control loops. In the loop control layer, the actual flow rates of raw materials follow their set-points obtained from the setting layer. At last, the results of industry experiments have proven the effectiveness of the proposed method.