Intelligent Optimal-Setting Control for Grinding Circuits of Mineral Processing Process

During the operation of a grinding circuit (GC) in mineral processing plant the main purpose of control and optimal operation is to control the product quality index, namely the product particle size, into its technically desired ranges. Moreover, the grinding production rate needs to be maximized. However, due to the complex dynamic characteristics between the above two indices and the control loops, such control objectives are difficult to achieve using existing control methods. The complexity is reflected by the existence of process heavy nonlinearities, strong coupling and large time variations. As a result, the lower level loop control with human supervision is still widely used in practice. However, since the setpoints to the involved control loops cannot be accurately adjusted under the variations of the boundary conditions, the manual setpoints control cannot ensure that the actual production indices meet with technical requirements all the time. In this paper, an intelligent optimal-setting control (IOSC) approach is developed for a typical two-stage GC so as to optimize the production indices by auto-adjusting on line the setpoints of the control loops in response to the changes in boundary conditions. This IOSC approach integrates case-based reasoning (CBR) pre-setting controlling, neural network (NN)-based soft-sensor and fuzzy adjusting into one efficient control model. Although each control element is well known, their innovative combination can generate better and more reliable performance. Both industrial experiments and applications show the validity and effectiveness of the proposed IOSC approach and its bright application foreground in industrial processes with similar features. Note to Practitioners-From a process engineering point of view, the purpose of GC control should not only achieve a perfect tracking of the controlled variables with respect to their setpoints, but also realize the optimization of production indices, namely the product particle size and the grinding production rate. However, these production indices cannot be optimized solely by the lower level control systems (LLCS) because of the process complexity and time-varying nature of the grinding operation. As a result, the operator is needed to determine the setpoints of each control loop of the LLCS using operational experience. Unfortunately, the manual operation cannot ensure that the actual production indices meet with technical requirements. In this paper, an IOSC approach is developed for the GC so as to optimize the concerned production indices. In a detailed description, this IOSC approach is composed of a case-based reasoning (CBR)-based loop pre-setting controller, a NN based particle size soft-sensor module and a fuzzy adjustor, and is used to auto-adjust the setpoints of lower level controllers under the varying boundary conditions. As long as the outputs of the LLCS track their renewed setpoints, the process can optimize the production indices and achieve the desired performance of the system. The proposed approach has been successfully applied to the grinding process of a large hematite mineral processing plant in China. It is believed that the results of this paper can be extended to a wide range of processes with the similar feature that the production indices cannot be optimized solely by the human supervised LLCS.