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

Juan A. Lazzús

doi:10.1016/S1004-9541(09)60133-6

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

Updated：2026-01-06

- 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
- 中国化学工程学报（英文版） 2010年18卷第5期页码：817-823
- Affiliations：
  
  Department of Physics, University of La Serena, Casilla 554, La Serena,Chile
- Author bio：
- Funds：
- DOI：10.1016/S1004-9541(09)60133-6
  中图分类号：
- 纸质出版：2010
- Accepted：
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Juan A. Lazzús. Prediction of Flash Point Temperature of Organic Compounds Using a Hybrid Method of Group Contribution+Neural Network+Particle Swarm Optimization[J]. 中国化学工程学报（英文版）, 2010,18(5):817-823.

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.
Juan A. Lazzús. Prediction of Flash Point Temperature of Organic Compounds Using a Hybrid Method of Group Contribution+Neural Network+Particle Swarm Optimization[J]. 中国化学工程学报（英文版）, 2010,18(5):817-823. DOI： 10.1016/S1004-9541(09)60133-6.

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. DOI： 10.1016/S1004-9541(09)60133-6.

摘要

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).

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