基于BP神经网络和遗传算法的综采面工艺参数优化研究

针对综采面生产过程机理复杂、数学模型难以建立等问题,通过数据挖掘和深度学习技术,挖掘隐藏在数据中的规律,通过智能建模技术和多目标优化技术,根据矿井综合生产指标对工艺控制参数进行模拟,建立工艺参数优化模型、通过海量历史数据对模型训练,给出合理工艺参数优化控制策略情况预测,以提升工作面生产效能为目的,选择出优化的、合理的工艺控制参数,为降低生产成本和能耗、提高生产效率提供智能决策方案,为矿山工作人员提供辅助决策方法。

Research on technology parameter optimization algorithm of fully mechanized coal mining face based on BP neural network and Genetic Algorithm

Taking the production process of fully mechanized coal mining face as the research object, aiming at the complex mechanism of the process, difficult to establish mathematical models and other problems, through data mining and deep learning technology, mining hidden laws in data, through intelligent modeling technology and multi-objective optimization technology, the process control parameters are simulated according to the comprehensive production index of the mine, the optimization model of process parameters is established, and the model is trained by massive historical data, in order to improve the production efficiency of working face, the optimized and reasonable process control parameters are selected, in order to reduce the production cost and energy consumption, improve the production efficiency to provide intelligent decision-making scheme, mining staff to provide auxiliary decision-making methods.