基于Q-learning模型的智能化放顶煤控制策略
Intelligent control strategy for top coal caving based on Q-learning model
【索引】李庆元 ,杨艺 ,李化敏,等.基于Q-learning模型的智能化放顶煤控制策略[J].工矿千亿国际app下载,2020,46(1):72-79.
【Reference】LI Qingyuan,YANG Yi,LI Huamin,et al.Intelligent control strategy for top coal caving based on Q-learning model[J].Industry and Mine Automation,2020,46(1):72-79.
【DOI】10.13272/j.issn.1671-251x.2019110001
【作者】李庆元1 ,杨艺1 ,李化敏2 ,费树岷3
【Author】LI Qingyuan1,YANG Yi1,LI Huamin2,FEI Shumin3
【作者机构】1.河南理工大学 电气工程与千亿国际app下载学院, 河南 焦作454000;2.河南理工大学 能源科学与工程学院, 河南 焦作454000;3.东南大学 千亿国际app下载学院, 江苏 南京210096
【Unit】1.School of Electrical Engineering and Automation,Henan Polytechnic University,Jiaozuo 454000,China;2.School of Energy Science and Engineering,Henan Polytechnic University,Jiaozuo 454000,China;3.School of Automation,Southeast University,Nanjing 210096,China
【摘要】传统的综放工作面放顶煤控制存在顶煤采出率低、出煤含矸率高等问题,而现有智能决策方法又存在建模困难、学习样本难以获取等障碍。针对上述问题,在液压支架放煤口动作决策过程中引入强化学习思想,提出一种基于Q-learning模型的智能化放顶煤控制策略。以最大化放煤效益为主要目标,结合顶煤放出体实时状态特征及顶煤动态赋存状态,采用基于Q-learning的放顶煤动态决策算法,在线生成多放煤口实时动作策略,优化多放煤口群组协同放煤过程,合理平衡顶煤采出率、出煤含矸率的关系。仿真和对比分析结果表明,该控制策略的顶煤平均采出率为91.24%,比传统“见矸关窗”的放煤方法提高约15.8%;平均全局奖赏值为685,比传统放煤方法提高约11.2%。该控制策略可显著减少混矸、夹矸等现象对放煤过程的影响,提高顶煤放出效益,减少煤炭资源浪费。
【Abstract】Traditional top coal caving control on fully mechanized caving face has problems of low top coal recovery ratio and high gangue proportion,and existing intelligent decision-making methods have obstacles such as difficulty in modeling and obtaining learning samples. In view of above problems,the idea of reinforcement learning was introduced into the decision-making process of coal outlet of hydraulic support,and an intelligent control strategy for top coal caving based on Q-learning model was proposed.With the main goal of maximizing the benefits of coal caving combined with real-time state characteristics of top coal release and dynamic occurrence status of top coal,a dynamic decision-making algorithm based on Q-learning is used to generate real-time action strategy of multiple coal outlets online, and optimize cooperative coal caving process of multiple coal outlets,reasonably balance relationship between top coal recovery ratio and gangue proportion. The results of simulation and comparative analysis show that the average recovery ratio of top coal of the proposed control strategy is 91.24%,which is about 15.8% higher than that of the traditional coal caving method; the average global reward value is 685,which is about 11.2% higher than that of the traditional coal caving method. The proposed control strategy can significantly reduce the impact of coal and gangue mixed phenomena on the coal caving process,improve efficiency of top coal discharge,and reduce waste of coal resources.
【关键词】 放顶煤; 煤矸分离; 强化学习; 智能放煤控制; 群组放煤; Q-learning模型
【Keywords】top coal caving; coal and gangue seperation; reinforcement learning; intelligent coal discharge control; group top coal caving; Q-learning model
【文献出处】工矿千亿国际app下载,2020年1期
【基金】国家重点研发计划项目(2018YFC0604502);河南省高等学校重点科研项目(19A413008,17A480007);河南省科技项目(192102210100,172102210270)
【分类号】TD823.97
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