湿法冶金2025，Vol.44Issue(5)：692-697,6.DOI:10.13355/j.cnki.sfyj.2025.05.015
大数据驱动的湿法冶金全流程优化控制模型及实证研究
Full-process Optimization Control Model and Empirical Research of Hydrometallurgy Driven by Big Data
蔡云龙1
作者信息
- 1. 呼伦贝尔职业技术学院信息工程系,内蒙古 呼伦贝尔 021000
- 折叠
摘要
Abstract
A big data-driven hydrometallurgical full-process optimization control model was proposed.Firstly,a LSTM model based on improved attention mechanism was constructed,and the self-attention mechanism was used to enhance the model's attention to key time steps to improve the prediction accuracy.Secondly,the dynamic adjustment and control of real-time production parameters are realized by using DDQN algorithm improved by priority experience playback.Finally,the optimal control model of economic benefit of the whole process is designed to obtain the optimal solution.The results show that the coefficient of determination R2 of the model is 0.92,0.90,0.92 in different numerical simulations,which shows that the model is superior to the traditional method in many aspects.关键词
LSTM/注意力机制/DDQN/优先经验回放/数值仿真Key words
LSTM/attention mechanism/DDQN/prioritized experience replay/numerical simulation分类
矿业与冶金引用本文复制引用
蔡云龙..大数据驱动的湿法冶金全流程优化控制模型及实证研究[J].湿法冶金,2025,44(5):692-697,6.
