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作 者:贾一飞 张翔宇[1] 李恩宽[1] 李强坤[1] 白乐[1] JIA Yi-fei;ZHANG Xiang-yu;LI En-kuan;LI Qiang-kun;BAI Le(Yellow River Institute of Hydraulic Research,Zhengzhou 450003,China)
出 处:《水电能源科学》2024年第5期76-79,共4页Water Resources and Power
基 金:国家自然科学基金项目(42041007)。
摘 要:为提高煤矿的取用水效率,建立了煤矿取用水智慧水务管理系统,该系统采用MLP神经网络模型,通过深度学习不同煤矿各用水环节的用水指标,并与相关用水定额进行综合对比,确定适合于系统应用煤矿各环节的合理用水区间,通过实时计算分析用水数据,及时预报预警不合理的用水环节,并将该系统应用于内蒙古鄂尔多斯市某煤矿中。结果表明,系统应用后该煤矿年取水量由117.75×10~4m~3降至86.87×10~4m~3,节水率达到26.23%,节水效果明显。可为后续煤矿类智慧水务管理系统的建设提供参考。In order to improve the efficiency of water intake in coal mines,a intelligent water management system for coal mine water intake has been established.The system uses an MLP neural network model to deeply learn the water consumption indicators of different coal mine water consumption stages,and compares them with relevant water con-sumption quotas to determine a reasonable water consumption range suitable for the application of the system in various coal mine stages.Through real-time calculation and analysis of water use data,timely prediction and warning of unrea-sonable water use links are carried out.The system is applied to a coal mine in Ordos City.The results show that the an-nual water intake decreased from 1.1775 million m3 to 868700 m3,with a water saving rate of 26.23%,and the water saving effect is obvious.This system can provide reference for the construction of the follow-up intelligent water affairs system of coal mines.
分 类 号:TV213.4[水利工程—水文学及水资源]
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