科技创新与应用2026，Vol.16Issue(24)：44-47,4.DOI:10.19981/j.CN23-1581/G3.2026.24.010
基于RBF神经网络与强化学习的烧结机电除尘系统智能控制研究
田纲 1孔赟 1高丰 1张勇强 1敖爽 1高莹1
作者信息
- 1. 本溪北营钢铁(集团)股份有限公司,辽宁 本溪 117017
- 折叠
摘要
Abstract
Aiming at the contradiction between stable dust mass concentration control and reduced operational energy consumption in traditional 360 m2 sintering machine electrostatic precipitator systems,this paper proposes a multi-objective intelligent control scheme based on a Radial Basis Function(RBF)neural network and reinforcement learning.The system collects real-time comprehensive electric field parameters via the Modbus/TCP protocol,utilizes the RBF neural network to accurately predict dust mass concentration trends,and designs a reinforcement learning algorithm with a"concentration-energy consumption"core reward function to dynamically coordinate and optimize the output strategy of each electric field power supply.Industrial application results show that the proposed control system can not only stabilize the outlet dust mass concentration below 50 mg/m3 but also reduce the comprehensive system energy consumption by approximately 15%under the same operating conditions,achieving a win-win outcome of environmental compliance and energy savings.关键词
电除尘/烧结机/RBF神经网络/强化学习/智能控制/Modbus/TCPKey words
electrostatic precipitator/sintering machine/RBF neural network/reinforcement learning/intelligent control/Modbus/TCP分类
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田纲,孔赟,高丰,张勇强,敖爽,高莹..基于RBF神经网络与强化学习的烧结机电除尘系统智能控制研究[J].科技创新与应用,2026,16(24):44-47,4.
