摘要
结合系统工程学、应用数学、最优化决策以及煤层气开采等相关理论,以煤与煤层气协调开发过程中时空促进与制约关系的定量化总结为基础,构建了BP神经网络的三区不同种类煤层气开采技术相对应煤层气产能预测模型,基于煤层气资源回收率、采气经济效益、资源开采效率3个指标量化了煤层气开发技术优化指标体系,研究了煤层气开采技术优化分析方法,建立了以三区时长促进与制约关系方程为核心,资源开发量守恒为本构模型,三区转换基本量化指标和瓦斯灾害防治要求相关指标为求解约束条件的煤与煤层气协调开发优化决策的数学模型,最终实现了基于B/S架构模式的煤与煤层气协调开发优化决策系统的研发。
Combines the relevant theories of system engineering,applied mathematics,optimization decision-making,and coalbed methane mining,and constructs a prediction model of coalbed methane production capacity corresponding to different types of coalbed methane mining technologies in three areas based on BP neural network,based on the recovery rate of coalbed methane resources,economic benefits of gas production.The three indicators of resource exploitation efficiency quantifies the optimization index system of coalbed methane development technology,studies the optimization analysis method of coalbed methane exploitation technology,and establishes a mathematical model for the optimization decision of coordinated development of coal and coalbed methane,with the three areas'duration promotion and restriction relationship equation as the core,the conservation of resource exploitation as the constitutive model,and the three areas'conversion of basic quantitative indicators and relevant indicators of gas disaster prevention requirements as the solution to the constraint conditions,finally,the research and development of the optimization decision-making system for coordinated development of coal and coalbed methane based on B/S architecture mode was realized.
作者
郭建行
GUO Jianhang(China Coal Research Institute,Beijing 100013,China;State Key Laboratory of Coal Mining and Clean Utilization(China Coal Research Institute),Beijing 100013,China;Beijing Coal Mine Safety Engineering Technology Research Center,Beijing 100013,China)
出处
《煤炭技术》
CAS
北大核心
2023年第8期89-94,共6页
Coal Technology
关键词
煤与煤层气
协调开发
煤层气产能预测
开发技术优化
资源量守恒
优化决策
coal and coalbed methane
coordinated development
coalbed methane productivity prediction
development technology optimization
conservation of resources
optimization decision