Dynamic Multi-Objective Optimization Method Of The Concentrate Production Indices For Mineral Processing

Recently, the rapid development of the steel industry leads to the market demand for metal ores and the market competition to more intensely. Therefore, in order to improve economic efficiency and market competitiveness, concentrator have to improve production efficiency and product quality, save energy and reduce production costs. Although intelligent optimization algorithms have achieved great development, the most studies are based only on the idea situation to make optimizing decisions, the situation includes the ideal requirement of decision goal, equipment capacity and energy resource constraints, in the application of integrated production index optimization of beneficiation. However, the equipment capacity, operating time, and the constraint index is changing with time in the actual production processing. So in order to improve decision-making capabilities to optimize integrated production index for mineral processing, concentration plant need to analysis more precise for optimal model, including consideration of change with time of the device capability and operating time.Subjective to the above problem, supported by the National Natural Science Foundation project "closed-loop optimal decision-making approach of technology index for complex industrial processes (61273031)", the research on the optimal decision-making of production indices for beneficiation processes under dynamic environment has been carried out. The detail work has been summarized as follows:1. The multi-objective optimization problem of production indices for mineral processing is described. The operation planning, production planning and complex production indices are introduced respectively. According to actual mineral production processing, the paper analyzed the optimization problems and the difficulties of whole production planning optimization for mineral processing.2. The G-DNSGA-Ⅱ genetic algorithm based on gradient-driven is proposed. It introduces three mechanisms in solving dynamic optimization problems, which respectively is the mechanism of environmental change detection, the mechanism to adapt to changes of the environment and the mechanism to explore population after environmental change. By the calculation of fitness value of the parent population again after environmental change to ensure the algorithm’s ability to adapt to environmental changes. We set the detector function to ensure detect changes quickly when the environment changes. In order to improve the algorithm’s ability to explore new population spatial after changes of the environment, we introduces a gradient-driven strategy which make up for the defects of classic optimization algorithm that is easy to fall into local optimum, distribution convergence direction by initial population and slowly convergence. The G-DNSGA-II algorithm and A-DNSGA-II algorithm are tested and compared on two dynamic test problems that is MFDA2 and HE2. The results of algorithm performance evaluation IGD shows that G-DNSGA-II algorithm has faster convergence. The feature is more obvious in the case of a large degree of change in the environment. HVR evaluated the ability of the algorithm to maintain population diversity and the result show that G-DNSGA-II algorithm has better performance.3. The paper analyzed the correlation between objective function, constraints range and parameters of beneficiation integrated production indicators optimization problem and the correlation between them with time in the actual production process. Through the description objective function and the scope of the constraints in the problem of beneficiation integrated production index optimization, this paper expounds the relationship each other and the difficulties in dealing with problem. Through analyzing the correlation integrated production indicators optimization problem of beneficiation with time, we find a change rule of device operational capability and the total running time, and then according to the law of variation with time of their,we establish the integrated production index dynamic optimization model of beneficiation.4. The changes characteristics of optimal values and optimal fitness of the math model we proposed are analyzed. We have studied the capacity of G-DNSGA-II algorithm in solving the beneficiation comprehensive production index dynamic optimization problem. When the device capability and operating time follow the time change and driven constraints change, G-DNSGA-II algorithm can quickly solve the pareto solution set of the concentrate grade, the metal recovery, the concentrate output, the concentration ratio and the production cost in different time period and demonstrate the effectiveness of G-DNSGA-Ⅱ algorithm to solve the dynamic multi-objective optimization problem of the concentrate production indices for mineral processing.