湿法冶金草酸钴粒度分布建模与优化研究

提出了一种基于实数编码的自适应遗传算法的优化控制策略,对湿法冶金草酸钴粒度分布进行了优化控制研究.利用最小二乘支持向量机与草酸钴合成过程动态机理模型相结合的混合建模方法,来预报草酸钴的粒度分布.在所建立的混合模型基础上,确定了草酸钴粒度分布的优化模型的目标函数与约束条件,并运用自适应遗传算法实现了对草酸钴粒度分布的优化.优化结果表明,所建立的优化模型较好地优化了草酸钴的粒度分布,提高了产品的质量,对草酸钴的工业生产以及促进硬质合金工业的发展有指导意义.

Modeling and Optimization of Cobalt Oxalate Particle Size Distribution in Hydrometallurgy Synthesis Process

A control strategy based on real-coding adaptive genetic algorithm(AGA) was developed and applied to the optimal control of cobalt oxalate synthesis. Based on the least squares support vector machine with the dynamic mechanism model of the synthesis process, a hybrid modeling method of the distribution was used to predict the particle size distribution(PSD). On this basis, the objective function and constraints of the PSD optimization model were established. The real-coding AGA was developed to solve the optimization problem. Simulation results showed that the method proposed can achieve a higher yield. Therefore, the optimization of cobalt oxalate PSD is of great importance to the industrial production of cobalt oxalate and to promote the development of cemented carbide industry.