Linear Polyethers as Additives for AOT-Based Microemulsions: Prediction of Percolation Temperature Changes Using Artificial Neural Networks

被引：7

作者：

Adrian Moldes, Oscar ^{[1
]}

Cid, Antonio ^{[2
]}

Montoya, I. A. ^{[1
]}

Carlos Mejuto, Juan ^{[1
]}

机构：

[1] Univ Vigo, Fac Ciencias, Dept Phys Chem, Orense 32004, Spain

[2] Univ Nova Lisboa, Fac Ciencias & Tecnol, Dept Quim, REQUIMTE, P-2829516 Monte De Caparica, Portugal

来源：

TENSIDE SURFACTANTS DETERGENTS | 2015年 / 52卷 / 04期

关键词：

Microemulsion; polyethers; percolation; prediction; artificial neural networks; NITROSO GROUP-TRANSFER; IN-OIL MICROEMULSIONS; ELECTRICAL PERCOLATION; WATER/AOT/ISOOCTANE MICROEMULSIONS; PSEUDOPHASE APPROACH; REVERSE MICELLES; QUANTITATIVE EXPLANATION; W/O MICROEMULSIONS; WATER; DYNAMICS;

D O I：

10.3139/113.110374

中图分类号：

O69 [应用化学];

学科分类号：

081704 ;

摘要：

Predictive models based on artificial neural networks have been developed for the percolation threshold of AOT based microemulsions with addition of either glymes or polyethylene glycols. Models have been built according to the multilayer perceptron architecture, with five input variables (concentration, molecular mass, log P, number of C and O of the additive). Best model for glymes has a topology of five input neurons, five neurons in a single hidden layer and one output neuron. Polyethylene glycol model's architecture consists in five input neurons, three hidden layers with eight neurons in both first two and five in the last, and a neuron in the last output layer. All of them have a good predictive power according to several quality parameters.

引用

页码：264 / 270

页数：7

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