基于改进粒子群算法的湿法冶金技术优化控制.

This study analyzes the key processes of hydrometallurgical technology, constructs an optimization con- trol 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 AIWSAO - 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 0.008 0, 0.004 5, and 0.971%, respectively. In the optimization control model for hydrometallurgical technology, the obtained comprehensive benefit value is 1.9 x 105 yuan/h, with an absolute error of about 0. 1 x 104 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. [ABSTRACT FROM AUTHOR]