煤的工业分析至元素分析的BP神经网络预测模型  

RELATIONSHIPBETWEEN ULTIMATEANALYSISOF ANY COAL ANDITSPROXIMATEANALYSISDATA

作　　者：殷春根[1] 骆仲泱 倪明江[1] 岑可法[1] 

机构地区：[1]浙江大学热能工程研究所,杭州310027

出　　处：《燃料化学学报》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

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-

关 键 词：工业分析 元素分析 神经网络 煤 模型 

分 类 号：TQ533[化学工程—煤化学工程]