湿法冶金2024，Vol.43Issue(6)：710-716,7.DOI:10.13355/j.cnki.sfyj.2024.06.017
基于DDQN优化控制及ResNet异常检测的湿法冶金设备智能控制模型研究
Intelligent Control Model of Hydrometallurgical Equipment Based on DDQN Optimization Control and ResNet Anomaly Detection
摘要
Abstract
An economic benefit optimization model for hydrometallurgical equipment control was established,and an optimization algorithm based on Double Deep Deterministic Q-Network(DDQN)model is introduced.At the same time,Residual Network is combined with residual network.ResNet's deep learning capability to realize the detection and early warning of abnormal equipment operation status.The simulation results show that the intelligent control algorithm can not only greatly improve the operating efficiency of hydrometallurgical equipment,but also enhance the stability and reliability of the system and improve the economic benefit of enterprises.关键词
湿法冶金/设备/智能控制/DDQN/ResNet/仿真分析Key words
hydrometallurgy/equipment/intelligent control/DDQN/ResNet/simulation analysis分类
矿业与冶金引用本文复制引用
赵秋锦..基于DDQN优化控制及ResNet异常检测的湿法冶金设备智能控制模型研究[J].湿法冶金,2024,43(6):710-716,7.基金项目
河南省高等学校重点科研项目(23B520047). （23B520047）
