基于改进粒子群算法的湿法冶金技术优化控制

Optimization control of hydrometallurgical technology based on improved particle swarm algorithm

分析了湿法冶金技术的关键工艺,构建了优化控制模型,并利用自适应惯性权重和模拟退火算子对粒子群算法进行改进,对湿法冶金技术进行优化控制。仿真试验结果显示:在A风力发电场优化数据集中测试中,AIW-SAO-PSO算法迭代225次时趋于稳定,适应度值约为0.165,且迭代100次时,算法的均方根误差、平均绝对误差、相对标准偏差分别为0.0080,0.0045和0.971%;在湿法冶金技术优化控制模型的寻优求解中,得到的综合效益值为1.9×10^(5)元/h,与目标期待值的绝对误差约为0.1×10^(4)元/h。实现了湿法冶金技术的优化控制,并为同类型优化控制提供理论支持。

This study analyzes the key processes of hydrometallurgical technology,constructs an optimization control model,and improves the particle swarm algorithm using adaptive inertia weight and simulated annealing operators to achieve optimization control of hydrometallurgical technology.Simulation test results show that in the test on the A wind farm optimization dataset,the AIW-SAO-PSO algorithm stabilizes after 225 iterations with a fitness value of 0.165.At 100 iterations,the algorithm s root mean square error,mean absolute error,and relative standard deviation(RSD)are 0.0080,0.0045,and 0.971%,respectively.In the optimization control model for hydrometallurgical technology,the obtained comprehensive benefit value is 1.9×10^(5) yuan/h,with an absolute error of about 0.1×10^(4) yuan/h from the target expected value.This achieves the optimization control of the hydrometallurgical process and provides technical support for similar optimization control applications.