Tensammetric Analysis of Nonionic Surfactant Mixtures by Artificial Neural Network

Safavi, Afsaneh; Sedaghatpour, F; Shahbaazi, Hamidreza

doi:10.1002/elan.200403225

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Representation Of a Typical Neural Network With 3 Neurons In The Input Layer 5 Neurons In One Hidden Layer And One Neuron In The1

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“…Nevertheless, the use of artificial neural networks is not only limited to the prediction of the previous properties, ANNs can also be used to tensammetric analysis of different nonionic surfactants (Brij 30, 35, 56 and 96) [43]. Authors concluded that ANNs can be a possible candidate to determine nonionic surfactants.…”

Section: Representation Of a Typical Neural Network With 3 Neurons In The Input Layer 5 Neurons In One Hidden Layer And One Neuron In Thementioning

confidence: 99%

Modeling the Behavior of Amphiphilic Aqueous Solutions

Astray¹,

Martinez-Castillo²,

Alonso-Ferrer³

et al. 2021

Deep Learning Applications

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Two types of predictive models based on artificial neural networks (ANN) and quadratic regression model developed in our laboratory will be summarized in this book chapter. Both models were developed to predict the density, speed of sound, kinematic viscosity and surface tension of amphiphilic aqueous solutions. These models were developed taking into account the concentration, the number of carbons and the molecular weight values. The experimental data were compiled from literature and included different surfactants: i) hexyl, ii) octyl, iii) decyl, iv) tetradecyl and v) octadecyl trimethyl ammonium bromide. Neural models present better adjustment values, with R2 values above 0.902 and AAPD values under 2.93% (for all data), than the quadratic regression models. Finally, it is concluded that the quadratic regression and the neural models can be powerful prediction tools for the physical properties of surfactants aqueous solutions.

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Section: Representation Of a Typical Neural Network With 3 Neurons In The Input Layer 5 Neurons In One Hidden Layer And One Neuron In Thementioning

confidence: 99%