Steady-state Identification and Optimization of Grinding Process
|Course||Control Theory and Control Engineering|
|Keywords||the grinding process steady-state detection Similarity Criterion chaos multi-objective optimization Particle Swarm Optimization(PSO) algorithm|
In the beneficiation process, the grinding process demands to consume a large amount of energy, steel and water. The power consumption approximates to 70-80% of the total energy consumption and it takes up a large proportion of the dressing beneficiation cost. With the rapid development of economy, the demand for mineral raw materials is also on the rise, making the volume of grinding operations increase significantly as well. Studies have shown that the optimization process of the grinding operation can reduce the volatility of product size, lower the dressing cost and improve the ore processing capacity. Therefore, in the fierce market competition, the demand for optimizing the quality and yield of the grinding products by the ore-dressing enterprises becomes increasingly pressing.Based on the previous work, this paper takes the grinding process of one beneficiation factory as its research object, combines with the production requirement of the beneficiation factory and establishes a mathematical model; then design a steady-state detection algorithm to detect the steady-state of the production data; Finally optimize the grinding model with an improved Particle Swarm Optimization(PSO)algorithm when the system state is steady.The main research of this paper are as follows:(1) Starting with the systematic analysis of the grinding process, this paper further analyzes the state and the variables/parameters of the grinding process, select the variables which need steady-state detection in the grinding process and then design the steady-state detection algorithm.(2) This paper selects some representative parameters to perform/proceed dimensional analysis, from which Similarity Criterion that reflects the input, output and the state properties of the grinding system can be figured out. Besides, according to the relationship between decisive and non-decisive dimensionless numbers, this paper will establish a Similarity Criterion mathematical model which describes both the grinding product output and the particle size distribution characteristics, and utilize the actual production data to identify the unknown parameters in the model. (3) In addition, this paper summarizes the development and research status of the Multi-Objective Optimization Method, and further an in-depth study of the multi-objective Particle Swarm Optimization (PSO) algorithm.(4) Moreover, this paper presents an introduction to the basic knowledge of chaos and adds a chaos mechanism on the basis of Particle Swarm Optimization (PSO) algorithm. According to the received mathematical models and the multi-objective optimization theory, this paper constructs a multi-objective optimization model for the grinding process and applies the improved algorithm to optimize the grinding process. Finally, this paper simulates the process in MATLAB, so as to obtain more suitable system operating parameters for actual production.