An Artificial Neural Network for the surface tension of alcohols

被引：51

作者：

Mulero, Angel ^{[1
]}

Pierantozzi, M. ^{[2
]}

Cachadina, Isidro ^{[1
]}

Di Nicola, G. ^{[3
]}

机构：

[1] Univ Extremadura, Dept Fis Aplicada, Badajoz 06006, Spain

[2] Univ Camerino, Scuola Architettura & Design, I-63100 Ascoli Piceno, Italy

[3] Univ Politecn Marche, Dipartimento Ingn Ind & Sci Matemat, I-60131 Ancona, Italy

来源：

FLUID PHASE EQUILIBRIA | 2017年 / 449卷

关键词：

Surface tension; Alcohols; Artificial neural network; Fluid properties; CORRESPONDING-STATES; SCALED EQUATION; PURE COMPOUNDS; IONIC LIQUIDS; REFRACTIVE-INDEXES; CHEMICAL-COMPOUNDS; BINARY-MIXTURES; TEMPERATURE; PREDICTION; REFRIGERANTS;

D O I：

10.1016/j.fluid.2017.06.003

中图分类号：

O414.1 [热力学];

学科分类号：

摘要：

An Artificial Neural Network model is proposed for the calculation and prediction of the surface tension of alcohols. A total amount of 4316 data for 147 alcohols was used for training, validating and testing the network model. After considering different architectures, the one giving better results includes an input layer that uses four independent variables (temperature, critical point temperature, critical density, and radius of gyration), two hidden layers with 21 neurons each one, and one neuron in the output layer was found to give the best results. Overall mean absolute percentage deviation of 1.04% was found, whereas models based on corresponding-states principle give mean deviations higher than 11.3%. (C) 2017 Elsevier B.V. All rights reserved.

引用

页码：28 / 40

页数：13

共 53 条

[1] Modeling of pure compounds surface tension using QSPR [J].