基于电性拓扑状态指数的二元液体混合物自燃温度的预测

doi:10.11949/j.issn.0438-1157.20151811

化工学报 ›› 2016, Vol. 67 ›› Issue (7): 3109-3117.DOI: 10.11949/j.issn.0438-1157.20151811

• 其他 • 上一篇

基于电性拓扑状态指数的二元液体混合物自燃温度的预测

何凡, 蒋军成, 潘勇, 倪磊

南京工业大学安全科学与工程学院, 江苏省危险化学品本质安全与控制技术重点实验室, 江苏南京 210009

收稿日期:2015-12-02 修回日期:2016-04-06 出版日期:2016-07-05 发布日期:2016-07-05
通讯作者: 蒋军成
基金资助:
国家自然科学基金项目（21436006，21576136）；江苏省自然科学基金重大项目（12KJA620001）。

Prediction of auto-ignition temperatures for binary liquid mixtures based on electro-topological state indices

HE Fan, JIANG Juncheng, PAN Yong, NI Lei

Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing 210009, Jiangsu, China

Received:2015-12-02 Revised:2016-04-06 Online:2016-07-05 Published:2016-07-05
Supported by:
supported by the National Natural Science Foundation of China (21436006, 21576136) and the Major Project of the Natural Science Foundation of Jiangsu Province (12KJA620001).

摘要/Abstract

摘要：

利用AITTA551自燃温度测试仪，测得不同组分和配比下的28组168个二元可燃混合液体自燃温度（AIT）；基于电性拓扑状态指数（ETSI）理论，计算获得不同原子类型所对应的混合ETSI值；采用添加指数递减惯性权重的改进粒子群算法（MPSO）优化支持向量机（SVM）的超平面参数，建立根据原子类型混合ETSI值来预测混合物自燃温度的MPSO-SVM模型。结果表明，基于电性拓扑状态指数能够有效预测二元液体混合物自燃温度，MPSO-SVM模型的复相关系数R²为0.991，平均绝对误差AAE为3.962 K。MPSO-SVM模型的泛化性能和预测精度明显优于多元线性回归（MLR）、网格搜索法（GSM-SVM）、遗传算法（GA-SVM）、标准粒子群算法（PSO-SVM）模型。本研究为工程上提供了一种预测二元混合物自燃温度的有效途径。

关键词: 电性拓扑状态指数, 二元混合物, 算法, 自燃温度, 预测

Abstract:

The auto-ignition temperature (AIT) values of 168 sets of binary flammable liquid mixtures composed of different components and volume ratios were measured by AITTA 551 auto-ignition temperature tester. The mixed electro-topological state indices (ETSI) values of different atom types were calculated. The modified particle swarm optimization (MPSO) algorithm with exponential decreasing inertia weight (EDIW) was applied to optimize the support vector machine (SVM) hyper-parameters and MPSO-SVM prediction model was established. The model was employed in research for predicting the AIT of mixtures according to the mixed ETSI values of different atom types. The results showed that it could effectively predict the AIT of binary liquid mixtures based on electro-topological state indices. The squared correlation coefficient (R²) and average absolute error (AAE) of MPSO-SVM model were 0.991 and 3.962 K, respectively. In terms of model generalization performance and prediction accuracy, the result of MPSO-SVM model was obviously superior to the results of multiple linear regression (MLR), grid search method (GSM-SVM), genetic algorithm (GA-SVM) and particle swarm optimization (PSO-SVM). This study provided an effective method to predict the AIT of binary liquid mixtures for engineering.

Key words: electro-topological state indices, binary mixture, algorithm, auto-ignition temperature, prediction

中图分类号:

O621.2
X937

何凡, 蒋军成, 潘勇, 倪磊. 基于电性拓扑状态指数的二元液体混合物自燃温度的预测[J]. 化工学报, 2016, 67(7): 3109-3117.

HE Fan, JIANG Juncheng, PAN Yong, NI Lei. Prediction of auto-ignition temperatures for binary liquid mixtures based on electro-topological state indices[J]. CIESC Journal, 2016, 67(7): 3109-3117.

参考文献 22

[1]	张宏哲, 王宁, 王亚琴, 等. 活化温度对活性炭自燃危险性的影响[J]. 化工学报, 2012, 63(11): 3730-3735. ZHANG H Z, WANG N, WANG Y Q, et al. Influence of activation temperature on spontaneous ignition behavior of activated carbon [J]. CIESC Journal, 2012, 63(11): 3730-3735.
[2]	YEONG S K, SUNG K L, JAE H K, et al. Prediction of autoignition temperatures (AITs) for hydrocarbons and compounds containing heteroatoms by the quantitative structure-property relationship [J]. Journal of the Chemical Society, Perkin Transactions 2, 2002, 12: 2087-2092.
[3]	PAN Y, JIANG J C, WANG R, et al. Advantages of support vector machine in QSPR studies for predicting auto-ignition temperatures of organic compounds [J]. Chemometrics and Intelligent Laboratory Systems, 2008, 92(2): 169-178.
[4]	PAN Y, JIANG J C, WANG R, et al. Predicting the auto-ignition temperatures of organic compounds from molecular structure using support vector machine [J]. Journal of Hazardous Materials, 2009, 164(2/3): 1242-1249.
[5]	TSAI F, CHEN C, LIAW H. A model for predicting the auto-ignition temperature using quantitative structure property relationship approach [J]. Procedia Engineering, 2012, 45: 512-517.
[6]	BAGHERI M, BORHANI T N G, ZAHEDI G. Estimation of flash point and auto-ignition temperature of organic sulfur chemicals [J]. Energy Conversion and Management, 2012, 58(6): 185-196.
[7]	KONG D, ECKHOFF R K, ALFERT F. Auto-ignition of CH4/air, C3H8/air, CH4/C3H8/air and CH4/CO2/air using a 1 L ignition bomb [J]. Journal of hazardous materials, 1995, 40(1): 69-84.
[8]	ROTA R, ZANOELO E F. Prediction of the auto-ignition hazard of industrial mixtures using detailed kinetic modeling [J]. Industrial and Engineering Chemistry Research, 2003, 42(13): 2940-2945.
[9]	NORMAN F, VAN DEN SCHOOR F, VERPLAETSEN F. Auto-ignition and upper explosion limit of rich propane-air mixtures at elevated pressures [J]. Journal of Hazardous Materials, 2006, 137(2): 666-671.
[10]	VAN DEN SCHOOR F, NORMAN F, VANDEBROEK L, et al. A numerical study of the influence of ammonia addition on the auto-ignition limits of methane/air mixtures [J]. Journal of Hazardous Materials, 2009, 164(2/3): 1164-1170.
[11]	American Institute of Chemical Engineers (AIChE). Design Institute for Physical Properties (DIPPR) [EB/OL]. [2014-09-11]. http://www.aiche.org/dippr/projects/801.
[12]	International Safety Intergovernmental Organization. International Chemical Safety Cards (ICSCs) [EB/OL]. [2010-03-11]. http://www.i-nchem. org/pages/icscs.html.
[13]	蒋军成, 潘勇. 有机化合物的分子结构与危险特性[M]. 北京: 科学出版社, 2011: 231-248. JIANG J C, PAN Y. The Molecular Structure and Dangerous Properties of Organic Compounds [M]. Beijing: Science Press, 2011: 231-248.
[14]	KIER L B, HALL L H. An electro-topological state index for atoms in molecules [J]. Pharmaceutical Research, 1990, 7(8): 801-807.
[15]	HALL L H, KIER L B. Electro-topological state indices for atom types: a novel combination of electro-topological and valencestate information [J]. Journal of Chemical Information and Computer Sciences, 1995, 35(6): 1039-1045.
[16]	安剑奇, 陈易斐, 吴敏. 基于改进支持向量机的高炉一氧化碳利用率预测方法[J]. 化工学报, 2015, 66(1): 206-214. AN J Q, CHEN Y F, WU M. A prediction method for carbon monoxide utilization ratio of blast furnace based on improved support vector regression [J]. CIESC Journal, 2015, 66(1): 206-214.
[17]	张晓南, 刘安心, 刘斌, 等. 基于优化PSO-SVM模型的软件可靠性预测[J]. 计算机应用, 2011, 31(7): 1761-1764, 1772. ZHANG X N, LIU A X, LIU B, et al. Software reliability prediction based on improved PSO-SVM model [J]. Journal of Computer Applications, 2011, 31(7): 1761-1764, 1772.
[18]	CHANG C C, LIN C J. LIBSVM: a library for support vector machines [J]. ACM Transactions on Intelligent Systems and Technology, 2011, 2(3): 389-396.
[19]	王梦寒, 李雁召, 夏知姿, 等. 基于Kriging模型与遗传算法结合的RHCM成型工艺参数优化[J]. 化工学报, 2014, 65(12): 5054-5060. WANG M H, LI Y Z, XIA Z Z, et al. Processing parameters optimization of rapid heat cycle molding based on Kriging meta-model and genetic algorithm [J]. CIESC Journal, 2014, 65(12): 5054-5060.
[20]	GRAMATICA P, PILUTTI P, PAPA E. Validated QSAR prediction of OH tropospheric degradation of VOCs: splitting into training_test sets and consensus modeling [J]. Journal of Chemical Information and Computer Sciences, 2004, 44(5): 1794-1802.
[21]	LIPNICK R L. Outliers: their origin and use in the classification of molecular mechanisms of toxicity [J]. Science of the Total Environment, 1991, 109: 131-153.
[22]	潘勇. 有机物定量结构燃爆特性相关性及预测模型研究[D]. 南京: 南京工业大学, 2009. PAN Y. Research on prediction models and quantitative relationships between the structures and flammability characteristics of organic compounds [D]. Nanjing: Nanjing University of Technology, 2009.

基于电性拓扑状态指数的二元液体混合物自燃温度的预测

Prediction of auto-ignition temperatures for binary liquid mixtures based on electro-topological state indices

PDF (PC)

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献 22

相关文章 15

编辑推荐

Metrics

本文评价

[1]	曹跃, 余冲, 李智, 杨明磊. 工业数据驱动的加氢裂化装置多工况切换过渡状态检测[J]. 化工学报, 2023, 74(9): 3841-3854.
[2]	诸程瑛, 王振雷. 基于改进深度强化学习的乙烯裂解炉操作优化[J]. 化工学报, 2023, 74(8): 3429-3437.
[3]	闫琳琦, 王振雷. 基于STA-BiLSTM-LightGBM组合模型的多步预测软测量建模[J]. 化工学报, 2023, 74(8): 3407-3418.
[4]	尹刚, 李伊惠, 何飞, 曹文琦, 王民, 颜非亚, 向禹, 卢剑, 罗斌, 卢润廷. 基于KPCA和SVM的铝电解槽漏槽事故预警方法[J]. 化工学报, 2023, 74(8): 3419-3428.
[5]	郭雨莹, 敬加强, 黄婉妮, 张平, 孙杰, 朱宇, 冯君炫, 陆洪江. 稠油管道水润滑减阻及压降预测模型修正[J]. 化工学报, 2023, 74(7): 2898-2907.
[6]	周继鹏, 何文军, 李涛. 异形催化剂上乙烯催化氧化失活动力学反应工程计算[J]. 化工学报, 2023, 74(6): 2416-2426.
[7]	李艳辉, 丁邵明, 白周央, 张一楠, 于智红, 邢利梅, 高鹏飞, 王永贞. 非常规服役超临界锅炉的微纳尺度腐蚀动力学模型建立及应用[J]. 化工学报, 2023, 74(6): 2436-2446.
[8]	于源, 陈薇薇, 付俊杰, 刘家祥, 焦志伟. 几何相似涡流空气分级机环形区流场变化规律研究及预测[J]. 化工学报, 2023, 74(6): 2363-2373.
[9]	高学金, 姚玉卓, 韩华云, 齐咏生. 基于注意力动态卷积自编码器的发酵过程故障监测[J]. 化工学报, 2023, 74(6): 2503-2521.
[10]	郑书闽, 郭鹏程, 颜建国, 王帅, 李文博, 周淇. 微小通道内过冷流动沸腾阻力特性实验及预测研究[J]. 化工学报, 2023, 74(4): 1549-1560.
[11]	张中秋, 李宏光, 石逸林. 基于人工预测调控策略的复杂化工过程多任务学习方法[J]. 化工学报, 2023, 74(3): 1195-1204.
[12]	顾学荣, 刘硕士, 杨思宇. 基于并行EGO和代理模型辅助的多参数优化方法研究[J]. 化工学报, 2023, 74(3): 1205-1215.
[13]	吴心远, 刘奇磊, 曹博渊, 张磊, 都健. Group2vec：基于无监督机器学习的基团向量表示及其物性预测应用[J]. 化工学报, 2023, 74(3): 1187-1194.
[14]	张生安, 刘桂莲. 高效太阳能电解水制氢系统及其性能的多目标优化[J]. 化工学报, 2023, 74(3): 1260-1274.
[15]	袁海鸥, 叶方俊, 张硕, 罗祎青, 袁希钢. 考虑中间换热器的能量集成精馏序列合成[J]. 化工学报, 2023, 74(2): 796-806.