林产化学与工业2017，Vol.37Issue(4)：123-128,6.DOI:10.3969/j.issn.0253-2417.2017.04.018
神经网络模型预测炭材料吸附VOCs的工作容量
Neural Network Model for Working Capacity Prediction of VOCs Adsorption on Carbon Materials
摘要
Abstract
The adsorption performance of volatile organic components(VOCs)on adsorbent was strongly affected by its porous structure.The traditional adsorbent screening required not only porous characterization,but also n-butane working capacity(BWC)measurement.In order to improve the screening efficiency,the average deviation of the experimental BWC and the calculated ones obtained from the BP artificial neural network constructed by machine learning on corresponding characterizations after thirty repeats of 61 kinds of activated carbon samples was about 6.64％.It was significant to explore such quantitative relationship between feature properties of activated carbon and their related BWC,which was meaningful for the further reduction of expenditure on adsorbent screening.关键词
多孔材料/孔隙结构/构效关系/丁烷吸附/人工智能算法应用Key words
amorphous materials/porous structure/quantitative structure-property relationship/butane adsorption/application of artificial intelligence algorithm分类
化学化工引用本文复制引用
王国栋,蒋剑春..神经网络模型预测炭材料吸附VOCs的工作容量[J].林产化学与工业,2017,37(4):123-128,6.基金项目
"十二五"国家科技支撑计划资助(2015BAD21B05) （2015BAD21B05）
