Optimization Of Aza Furnace Ingredients Based On Improved Particle Swarm Optimization

Isa furnace is the core process of copper production, it still maintained a high efficiency of operation and loading rate. The volatility of the raw material composition, the level matte, and the mount of the fuel, and that will lead to temperature fluctuations of the molten pool that many specific process parameters always changes in real time.Ingredients is a mix process of the different composition of minerals that it can ensure the component satisfies the requirement of smelting after we finish ingredients, and also it can guarantee the stability of the chemical composition. Ingredients has great significance, and Isa furnace has a high requirement about the stability of dubbed concentrate. Concentrate is stable the fluctuation of Isa furnace temperature is small. It is more significant about improving the quality of the matte, protecting Isa furnace and ensure the proportion of silica and iron. Therefore, the merits of the ingredients can lead to the quality of the products. And now, the problem of batching optimization is becoming more and more attention by public. Now the batching optimization problem is becoming more and more attention by people.The research of this paper which focuse on the batching mathematical model of ISA furnace and the method of bating optimization. Firstly, the paper established the mathematical model of batching system. Mathematical modeling is aimed to reduce the production costs and increase production outputs by batching optimization. We can reduce the production cost through the low consumption and increasing outputs. It mainly includes:Control the type of slag and matte grade well, etc. We can use the indirection method and a combination of multi-constraint conditions which with the production cost to the objective function and the various parameters in the process of production. In this paper, it proposed an improved particle swam optimization algorithm to optimize the batching method of ISA furnace. The disadvantage of standard particle swarm algorithm is premature convergence and slow convergence. With the increased number of iterations, the standard particle swarm optimization algorithm cannot continuous optimization of the solutions. And it is easy to hover in the vicinity of local optima, thus trapped into local solution. The improvements of inertia weight algorithm, and the change of inertia weight has a great influence on the algorithm which is global search and local search. This article introduces the inertia weight of the linear transformation to balance the global search and local search, thus preventing algorithm falls into local optimization. The paper made mathematical model constraints combined with particle swarm optimization algorithm. Mathematical model constraints combined with particle swarm optimization algorithm can reduce the range of the effective search of particle swarm. The experimental simulation demonstrate the effectiveness of the modeling and optimization methods. In this paper, it also satisfied the optimization model by sintering ingredients multi-objective, and it design and development the batching optimization system of ISA furnace.