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Autoignition Temperature Prediction Using an Artificial Neural Network with Particle Swarm Optimization

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Abstract

The autoignition temperatures of organic compounds were estimated using a hybrid method that includes a simple group contribution method (GCM) implemented in an artificial neural network (ANN) replacing a standard back-propagation algorithm with particle swarm optimization (PSO). A data set of 250 compounds was used for training the network. The optimal condition of the network was obtained by adjusting various parameters by trial-and-error. The capabilities of the designed network were tested in the prediction of the autoignition temperature of 93 compounds not considered during the training step. The proposed model is shown to be more accurate than those of other published works. The results show that the proposed GCM + ANN + PSO method represent an excellent alternative for the estimation of this property with acceptable accuracy (AARD = 1.7%; AAE = 10K).

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Authors and Affiliations

  1. Departamento de Física, Universidad de La Serena, Casilla 554, La Serena, Chile

    Juan A. Lazzús

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  1. Juan A. Lazzús
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Correspondence to Juan A. Lazzús.

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Lazzús, J.A. Autoignition Temperature Prediction Using an Artificial Neural Network with Particle Swarm Optimization. Int J Thermophys 32, 957–973 (2011). https://doi.org/10.1007/s10765-011-0956-4

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  • DOI: https://doi.org/10.1007/s10765-011-0956-4

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