Prediction of true critical temperature and pressure of binary hydrocarbon mixtures: A Comparison between the artificial neural networks and the support vector machine
Two main objectives have been considered in this paper: providing a good model to predict the critical temperature and pressure of binary hydrocarbon mixtures, and comparing the efficiency of the artificial neural network algorithms and the support vector regression as two commonly used soft computi...
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| Format: | Article |
| Language: | English |
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Iranian Association of Chemical Engineering (IAChE)
2019-06-01
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| Series: | Iranian Journal of Chemical Engineering |
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| Online Access: | https://www.ijche.com/article_90024_ac9ae0799df66218e20ff3997d7a24a6.pdf |
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| author | M. Etebarian k. movagharnejad |
| author_facet | M. Etebarian k. movagharnejad |
| author_sort | M. Etebarian |
| collection | DOAJ |
| description | Two main objectives have been considered in this paper: providing a good model to predict the critical temperature and pressure of binary hydrocarbon mixtures, and comparing the efficiency of the artificial neural network algorithms and the support vector regression as two commonly used soft computing methods. In order to have a fair comparison and to achieve the highest efficiency, a comprehensive search method is used in neural network modeling, and a particle swram optimization algorithm for SVM modeling. To compare the accuracy of the models, various criteria such as ARD, MAE, MSE, RAE and R2 are used. The simulation results show that the ARD for the prediction of the true critical temperature and pressure of the binary hydrocarbon mixtures for the final optimized ANN-based model is equal to 0.0161 and 0.0387, respectively. The corressponding ARD value for the SVM-based model is equal to 0.0086 and 0.0091 for critical temperature and pressure, respectively. Simulation results show that although both models have a very high predictive accuracy, the SVM has higher learning speed and accuracy than ANN. |
| format | Article |
| id | doaj-art-f7f9b73cd3e347d2990d6f4609c7635e |
| institution | DOAJ |
| issn | 1735-5397 2008-2355 |
| language | English |
| publishDate | 2019-06-01 |
| publisher | Iranian Association of Chemical Engineering (IAChE) |
| record_format | Article |
| series | Iranian Journal of Chemical Engineering |
| spelling | doaj-art-f7f9b73cd3e347d2990d6f4609c7635e2025-08-20T03:13:38ZengIranian Association of Chemical Engineering (IAChE)Iranian Journal of Chemical Engineering1735-53972008-23552019-06-01162144090024Prediction of true critical temperature and pressure of binary hydrocarbon mixtures: A Comparison between the artificial neural networks and the support vector machineM. Etebarian0k. movagharnejad1Faculty of Chemical Engineering, Babol Noshiravani University of TechnologyFaculty of Chemical Engineering, Babol Noshiravani University of TechnologyTwo main objectives have been considered in this paper: providing a good model to predict the critical temperature and pressure of binary hydrocarbon mixtures, and comparing the efficiency of the artificial neural network algorithms and the support vector regression as two commonly used soft computing methods. In order to have a fair comparison and to achieve the highest efficiency, a comprehensive search method is used in neural network modeling, and a particle swram optimization algorithm for SVM modeling. To compare the accuracy of the models, various criteria such as ARD, MAE, MSE, RAE and R2 are used. The simulation results show that the ARD for the prediction of the true critical temperature and pressure of the binary hydrocarbon mixtures for the final optimized ANN-based model is equal to 0.0161 and 0.0387, respectively. The corressponding ARD value for the SVM-based model is equal to 0.0086 and 0.0091 for critical temperature and pressure, respectively. Simulation results show that although both models have a very high predictive accuracy, the SVM has higher learning speed and accuracy than ANN.https://www.ijche.com/article_90024_ac9ae0799df66218e20ff3997d7a24a6.pdfcritical pressurecritical temperatureartificial neural networksupport vector machinebinary hydrocarbon mixtureparticle swarm optimization |
| spellingShingle | M. Etebarian k. movagharnejad Prediction of true critical temperature and pressure of binary hydrocarbon mixtures: A Comparison between the artificial neural networks and the support vector machine Iranian Journal of Chemical Engineering critical pressure critical temperature artificial neural network support vector machine binary hydrocarbon mixture particle swarm optimization |
| title | Prediction of true critical temperature and pressure of binary hydrocarbon mixtures: A Comparison between the artificial neural networks and the support vector machine |
| title_full | Prediction of true critical temperature and pressure of binary hydrocarbon mixtures: A Comparison between the artificial neural networks and the support vector machine |
| title_fullStr | Prediction of true critical temperature and pressure of binary hydrocarbon mixtures: A Comparison between the artificial neural networks and the support vector machine |
| title_full_unstemmed | Prediction of true critical temperature and pressure of binary hydrocarbon mixtures: A Comparison between the artificial neural networks and the support vector machine |
| title_short | Prediction of true critical temperature and pressure of binary hydrocarbon mixtures: A Comparison between the artificial neural networks and the support vector machine |
| title_sort | prediction of true critical temperature and pressure of binary hydrocarbon mixtures a comparison between the artificial neural networks and the support vector machine |
| topic | critical pressure critical temperature artificial neural network support vector machine binary hydrocarbon mixture particle swarm optimization |
| url | https://www.ijche.com/article_90024_ac9ae0799df66218e20ff3997d7a24a6.pdf |
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