人工神经网络-基团键贡献耦合模型预测煤液化油的偏心因子  被引量:1

Prediction of acentric factor of direct coal liquefaction oil by artificial neural network and group bond contribution coupled model

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作  者:毛学锋 

机构地区:[1]煤炭科学技术研究院有限公司煤化工分院,北京100013 [2]煤炭资源开采与洁净利用国家重点实验室,北京100013 [3]国家能源煤炭高效利用与节能减排技术装备重点实验室,北京100013

出  处:《洁净煤技术》2017年第3期39-44,50,共7页Clean Coal Technology

基  金:国家重点研发计划资助项目(2016YFB0600303)

摘  要:为探索预测煤直接液化油窄馏分的偏心因子的新方法,建立了基于人工神经网络-基团键贡献耦合模型(ANN-GBC),以煤直接液化油包含的45个基团键和常压沸点(T_b)共46个参数作为该模型的输入参数,研究了煤直接液化油15个窄馏分的偏心因子与分子结构之间的相关性。结果表明,通过计算20个模型化合物的偏心因子,表明ANN-GBC模型具有较好的模拟推算功能,计算值与理论值平均相对误差均在2.5%以下。偏心因子ω随蒸馏切割馏分温度的升高而增大,ANN-GBC模型预测值普遍高于Watanasiri、NEDOL关联式的计算值。<380℃馏分ω小于1,相对偏差较小;>380℃馏分ω偏差较大;针对>420℃馏分,因仅能定性定量分析其中20%物质,不同物质的含量差异导致个别结果的跳跃,ω偏差较大。In order to explore a new method to predict the acentric factor of narrow fractions from direct coal liquefaction oil( DCLO),artificial neural network and group bond contribution coupled model( ANN-GBC) were established. The coupled model used 45 group-bonds and atmospheric boiling point( Tb) of DCLO as input parameters,the relevance between acentric factor and molecular structure of 15 coal liquefaction narrow fractions was investigated.By calculating the acentric factors of 20 model compounds,the ANN-GBC model presents good simulation calculation function,and the average relative error between the calculated value and the theoretical value is less than2. 5%.These comparative data show that acentric factor increases with the increasing of the distillation temperature.The predicted value of ANN-GBC model is higher than that from Watanasiri and NEDOL.In terms of 380 ℃ fractions,ω is less than 1,and the deviation is relatively small,nevertheless,the deviation of 380 ℃ fractions is larger. The 420 ℃ fraction can be qualitatively and quantitatively analyzed,because only 20% substances is derived from coal liquefaction narrow fractions. In addition,the actual differences in specific substances could induce a larger deviation,the deviation is very large.

关 键 词:煤直接液化油 人工神经网络-基团键贡献耦合模型 窄馏分 偏心因子 常压沸点 

分 类 号:TQ529[化学工程—煤化学工程]

 

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