Prediction of Flash Point Temperature of Organic Compounds Using a Hybrid Method of Group Contribution+Neural Network+Particle Swarm Optimization

›› 2010, Vol. 18 ›› Issue (5): 817-823.

Prediction of Flash Point Temperature of Organic Compounds Using a Hybrid Method of Group Contribution+Neural Network+Particle Swarm Optimization

Juan A. Lazzús

Department of Physics, University of La Serena, Casilla 554, La Serena, Chile

收稿日期:2009-09-18 修回日期:2010-05-20 出版日期:2010-10-28 发布日期:2010-10-28
通讯作者: Juan A. Lazzús,E-mail:jlazzus@dfuls.cl

Prediction of Flash Point Temperature of Organic Compounds Using a Hybrid Method of Group Contribution+Neural Network+Particle Swarm Optimization

Juan A. Lazzús

Department of Physics, University of La Serena, Casilla 554, La Serena, Chile

Received:2009-09-18 Revised:2010-05-20 Online:2010-10-28 Published:2010-10-28

摘要/Abstract

摘要： The flash points of organic compounds were estimated using a hybrid method that includes a simple group contribution method (GCM) implemented in an artificial neural network (ANN) with particle swarm optimization (PSO). Different topologies of a multilayer neural network were studied and the optimum architecture was determined. Property data of 350 compounds were used for training the network. To discriminate different substances the molecular structures defined by the concept of the classical group contribution method were given as input variables. The capabilities of the network were tested with 155 substances not considered in the training step. The study shows that the proposed GCM+ANN+PSO method represent an excellent alternative for the estimation of flash points of organic compounds with acceptable accuracy (AARD=1.8%;AAE=6.2 K).

关键词: flash point, group contribution method, artificial neural networks, particle swarm optimization, property estimation

Abstract: The flash points of organic compounds were estimated using a hybrid method that includes a simple group contribution method (GCM) implemented in an artificial neural network (ANN) with particle swarm optimization (PSO). Different topologies of a multilayer neural network were studied and the optimum architecture was determined. Property data of 350 compounds were used for training the network. To discriminate different substances the molecular structures defined by the concept of the classical group contribution method were given as input variables. The capabilities of the network were tested with 155 substances not considered in the training step. The study shows that the proposed GCM+ANN+PSO method represent an excellent alternative for the estimation of flash points of organic compounds with acceptable accuracy (AARD=1.8%;AAE=6.2 K).

Key words: flash point, group contribution method, artificial neural networks, particle swarm optimization, property estimation

Juan A. Lazzús. Prediction of Flash Point Temperature of Organic Compounds Using a Hybrid Method of Group Contribution+Neural Network+Particle Swarm Optimization[J]. , 2010, 18(5): 817-823.

参考文献

1 Gharagheizi, F., Alamdari, R.F., “Prediction of flash point temperature of pure components using a quantitative structure-property relationship model”, QSAR Comb. Sci., 27, 679-683 (2008).
2 Taskinen, J., Yliruusi, J., “Prediction of physicochemical properties based on neural network modeling”, Adv. Drug Deliv. Rev., 55, 1163-1183 (2003).
3 Vidal, M., Rogers, W.J., Holste, J.C., Mannan, M.S., “A review of estimations method for flash points and flammability limits”, Process Saf. Progress, 23, 47-55 (2004).
4 Suzuki, T., Ohtaguchi, K., Koide, K., “A method for estimating flash points of organic compounds from molecular structures”, J. Chem. Eng. Jpn., 24, 258-261 (1991).
5 Satyanarayana, K., Rao, P.G., “Improved equation to estimate flash points of organic compounds”, J. Hazard. Mater., 32, 81-85 (1992).
6 Tetteh, J., Suzuki, T., Metcalfe, E., Howells, S., “Quantitative structure-property relationships for the estimation of boiling point and flash point using a radial basis function neural network”, J. Chem. Inf. Comput. Sci., 39, 491-507 (1999).
7 Katritzky, A.R., Petrukhin, R., Jain, R., Karelson, M., “QSPR analysis of flash points”, J. Chem. Inf. Comput. Sci., 41, 1521-1530 (2001).
8 Zhokhova, N.I., Baskin, I.I., Palyulin, V.A., Zefirov, A.N., Zefirov, N.S., “Fragmental descriptors in QSPR:Flash point calculations”, Russ. Chem. Bull. Int. Ed., 52, 1885-1892 (2003).
9 Albahri, T.A., “Flammability characteristics of pure hydrocarbons”, Chem. Eng. Sci., 58, 3629-3641 (2003).
10 Vazhev, V.V., Aldabergenov, M.K., Vazheva, N.V., “Estimation of flash points and molecular masses of alkanes from their IR spectra”, Petrol. Chem., 46, 136-139 (2006).
11 Pan, Y., Jiang, J., Wang, Z., “Quantitative structure-property relationship studies for predicting flash points of alkanes using group bond contribution method with back-propagation neural network”, J. Hazard. Mater., 147, 424-430 (2007).
12 Katritzky, A.R., Stoyanova-Slavova, I.B., Dobchev, D.A., Karelson, M., “QSPR modeling of flash points:An update”, J. Mol. Graph. Model., 26, 529-536 (2007).
13 Lazzús, J.A., “Neural network based on quantum chemistry for predicting melting point of organic compounds”, Chin. J. Chem. Phys., 22, 19-26 (2009).
14 Lazzús, J.A., “ρ-T-P prediction for ionic liquids using neural networks”, J. Taiwan Inst. Chem. Eng., 40, 213-232 (2009).
15 Lazzús, J.A., “Prediction of solid vapor pressures for organic and inorganic compounds using a neural network”, Thermochim. Acta, 489, 53-62 (2009).
16 Lazzús, J.A., “Estimation of density as a function of temperature and pressure for imidazolium-based ionic liquids using a multilayer net with particle swarm optimization”, Int. J. Thermophys., 30, 833-909 (2009).
17 Lazzús, J.A., “Hybrid method to predict melting points of organic compounds using group contribution+neural network+particle swarm algorithm”, Ind. Eng. Chem. Res., 48, 8760-8766 (2009).
18 Luo, Q., Yi, D., “A co-evolving framework for robust particle swarm optimization”, Appl. Math. Comput., 199, 611-622 (2008).
19 MathWorks, MatLab version 6.5.0., The MathWorks Inc. (2002).
20 Da, Y., Xiurun, G., “An improved PSO-based ANN with simulated annealing technique”, Neurocomputing, 63, 527-533 (2005).
21 Jiang, Y., Hu, T., Huang, C., Wu, X., “An improved particle swarm optimization algorithm”, Appl. Math. Comput., 193, 231-239 (2007).
22 Daubert, T.E., Danner, R.P., Sibul, H.M., Stebbins, C.C., Physical and Thermodynamic Properties of Pure Chemicals. Data Compilation, Taylor & Francis, London (2000).

[1]	Danlei Chen, Yiqing Luo, Xigang Yuan. Cascade refrigeration system synthesis based on hybrid simulated annealing and particle swarm optimization algorithm[J]. 中国化学工程学报, 2023, 58(6): 244-255.
[2]	Guangyao Zhao, Minglei Yang, Wenli Du, Feifei Shen, Feng Qian. A stochastic reconstruction strategy based on a stratified library of structural descriptors and its application in the molecular reconstruction of naphtha[J]. 中国化学工程学报, 2022, 51(11): 153-167.
[3]	Danlei Chen, Yiqing Luo, Xigang Yuan. Refrigeration system synthesis based on de-redundant model by particle swarm optimization algorithm[J]. 中国化学工程学报, 2022, 50(10): 412-422.
[4]	Markus Enekvist, Xiaodong Liang, Xiangping Zhang, Kim Dam-Johansen, Georgios M. Kontogeorgis. Estimating Hansen solubility parameters of organic pigments by group contribution methods[J]. 中国化学工程学报, 2021, 29(3): 186-197.
[5]	Alireza Baghban, Jafar Sasanipour, Sajjad Habibzadeh, Zhi'en Zhang. Estimating solubility of supercritical H₂S in ionic liquids through a hybrid LSSVM chemical structure model[J]. 中国化学工程学报, 2019, 27(3): 620-627.
[6]	Dongfang Zhao, Guanghui Liu, Jian Sun, Lisheng Wang. Enthalpy of phase transition of isonicotinic acid[J]. , 2017, 25(7): 971-975.
[7]	Jin Lang, Jiao Zhao. Modeling and optimization for oil well production scheduling[J]. , 2016, 24(10): 1423-1430.
[8]	周颖, 林真, 吴可君, 徐国华, 何潮洪. A Group Contribution Method for the Correlation of Static Dielectric Constant of Ionic Liquids[J]. Chinese Journal of Chemical Engineering, 2014, 22(1): 79-88.
[9]	Morteza Atabati, Reza Emamalizadeh. A Quantitative Structure Property Relationship for Prediction of Flash Point of Alkanes Using Molecular Connectivity Indices[J]. Chinese Journal of Chemical Engineering, 2013, 21(4): 420-426.
[10]	吴新杰, 崔春阳, 胡晟, 李志宏, 吴成东. The Velocity Measurement of Two-phase Flow Based on Particle Swarm Optimization Algorithm and Nonlinear Blind Source Separation[J]. , 2012, 20(2): 346-351.
[11]	王建林, 薛尧予, 于涛, 赵利强. Run-to-run Optimization for Fed-batch Fermentation Process with Swarm Energy Conservation Particle Swarm Optimization Algorithm[J]. , 2010, 18(5): 787-794.
[12]	王建林, 赵利强, 于涛. On-line Estimation in Fed-batch Fermentation Process Using State Space Model and Unscented Kalman Filter[J]. , 2010, 18(2): 258-264.
[13]	徐圆, 朱群雄. Research and Implementation of Decreasing the Acetic Acid Consumption in Purified Terephthalic Acid Solvent System[J]. , 2008, 16(4): 650-655.
[14]	罗祎青, 袁希钢. Global Optimization for the Synthesis of Integrated Water Systems with Particle Swarm Optimization Algorithm[J]. Chinese Journal of Chemical Engineering, 2008, 16(1): 11-15.
[15]	Hajir Karimi, Fakhri Yousefi. 运用人工神经网络关联二元混合物中气液相平衡[J]. , 2007, 15(5): 765-771.

Prediction of Flash Point Temperature of Organic Compounds Using a Hybrid Method of Group Contribution+Neural Network+Particle Swarm Optimization

Prediction of Flash Point Temperature of Organic Compounds Using a Hybrid Method of Group Contribution+Neural Network+Particle Swarm Optimization

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价