摘要
煤炭发热量是动力煤煤质的重要衡量指标,其决定了煤炭销售价格,对市场非常重要。因此,如何实现发热量的精准预测十分必要。文章以顾桥煤矿商品煤发热量为研究对象,以灰分和全水分作为关键关联指标建立了煤炭发热量的BP神经网络预测模型。研究结果显示,BP神经网络模型具有较高的预测精度,且实现过程相对简单,能够满足工业生产需要。
the calorific value of coal is an important index to measure the quality of steam coal,which determines the sales price of coal and is very important to the market.Therefore,it is necessary to accurately predict the calorific value.Taking the calorific value of commercial coal in Guqiao Coal Mine as the research object,taking ash and total moisture as the key correlation indexes,the BP neural network prediction model of coal calorific value is established.The results show that BP neural network model has high prediction accuracy,and the realization process is relatively simple,which can meet the needs of industrial production.
出处
《科技视界》
2021年第12期57-59,共3页
Science & Technology Vision
关键词
BP神经网络
发热量
灰分
全水分
顾桥煤矿
BP neural network
Calorific value
Ash content
Total moisture
Guqiao Coal Mine
