运用人工神经网络关联二元混合物中气液相平衡

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运用人工神经网络关联二元混合物中气液相平衡

Hajir Karimi¹; Fakhri Yousefi²

¹ Chemical Engineering Department, Yasouj University, Yasouj 75914-353, Iran
² Department of Chemistry, College of Science, Shiraz University, Shiraz 71454, Iran

收稿日期:2007-03-06 修回日期:1900-01-01 出版日期:2007-10-28 发布日期:2007-10-28
通讯作者: Hajir Karimi

Correlation of vapour liquid equilibria of binary mixtures using artificial neural networks

Hajir Karimi¹; Fakhri Yousefi²

¹ Chemical Engineering Department, Yasouj University, Yasouj 75914-353, Iran
² Department of Chemistry, College of Science, Shiraz University, Shiraz 71454, Iran

Received:2007-03-06 Revised:1900-01-01 Online:2007-10-28 Published:2007-10-28
Contact: Hajir Karimi

摘要/Abstract

摘要： In this paper, a back propagation artificial neural network (BP-ANN) model is presented for the simultaneous estimation of vapour liquid equilibria (VLE) of four binary systems viz chlorodifluoromethan-carbondioxide, trifluoromethan-carbondioxide, carbondisulfied-trifluoromethan and carbondisulfied-chlorodifluoromethan. VLE data of the systems were taken from the literature for wide ranges of temperature (222.04—343.23K) and pressure (0.105 to 7.46MPa). BP-ANN trained by the Levenberg-Marquardt algorithm in the MATLAB neural network toolbox was used for building and optimizing the model. It is shown that the established model could estimate the VLE with satisfactory precision and accuracy for the four systems with the root mean square error in the range of 0.054—0.119. Predictions using BP-ANN were compared with the conventional Redlich-Kwang-Soave (RKS) equation of state, suggesting that BP-ANN has better ability in estimation as compared with the RKS equation (the root mean square error in the range of 0.115—0.1546).

关键词: vapour liquid equilibria;artificial neural networks;refrigerant

Abstract: In this paper, a back propagation artificial neural network (BP-ANN) model is presented for the simultaneous estimation of vapour liquid equilibria (VLE) of four binary systems viz chlorodifluoromethan-carbondioxide, trifluoromethan-carbondioxide, carbondisulfied-trifluoromethan and carbondisulfied-chlorodifluoromethan. VLE data of the systems were taken from the literature for wide ranges of temperature (222.04—343.23K) and pressure (0.105 to 7.46MPa). BP-ANN trained by the Levenberg-Marquardt algorithm in the MATLAB neural network toolbox was used for building and optimizing the model. It is shown that the established model could estimate the VLE with satisfactory precision and accuracy for the four systems with the root mean square error in the range of 0.054—0.119. Predictions using BP-ANN were compared with the conventional Redlich-Kwang-Soave (RKS) equation of state, suggesting that BP-ANN has better ability in estimation as compared with the RKS equation (the root mean square error in the range of 0.115—0.1546).

Key words: vapour liquid equilibria, artificial neural networks, refrigerant

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]. CIESC Journal.

Hajir Karimi, Fakhri Yousefi. Correlation of vapour liquid equilibria of binary mixtures using artificial neural networks[J]. .

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运用人工神经网络关联二元混合物中气液相平衡

Correlation of vapour liquid equilibria of binary mixtures using artificial neural networks

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