摘要
以大量煤质分析数据为基础，建立了利用煤工业分析数据（ 包括水份、灰份、挥发份及热值） 计算元素分析数据的ＢＰ神经网络预测模型，并将该模型与现有经验公式进行了比较，结果表明神经网络模型有很好的推广能力。
There existsintrinsicrelation between the primary elements such as carbon , hydrogen ,oxygen and nitrogen and so on ,and the proximate analysis data including mois ture content,ash content,volatile content,fixed carbon and heatvalue ofcoal- However, thisrelationis much complicated and difficultto understandfully- Based on alarge quantity of coaldata ,a BPneuralnetwork modelis putforward inthis paperto predictthe ultimate analysisofa coalfrom its proximate analysis data- Comparison with the existing empirical modelsindicatesthat neuralnetwork techniqueis quite effectivein drawingtheintrinsic and intricate relationship betweenthe ultimate analysis ofa coaland its proximate analysis data-
出处
《燃料化学学报》
EI
CAS
CSCD
北大核心
1999年第5期408-414,共7页
Journal of Fuel Chemistry and Technology
基金
浙江省中青年科技人才基金
关键词
工业分析
元素分析
神经网络
煤
模型
proximate analysis,ultimate analysis,artificial neuralnetwork
