Optimization Methodology of Artificial Neural Network Models for Predicting Molecular Diffusion Coefficients for Polar and Non-Polar Binary Gases

Melzi, Naima; Khaouane, Latifa; Hanini, Salah; Laidi, Maamar; Ammi, Yamina; Zentou, Hamid

doi:10.1134/s0021894420020066

Cited by 6 publications

(2 citation statements)

References 25 publications

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“…; obviously the function d i,j+1 at the point (x, y) ∈ R 2 is differentiable, which shows that the DDOA vector d and the AOA value θ have a consistent trend [24,25]. In other words, when the DDOA vectors of two source signals are similar, their arrival direction angles AOA must also be similar.…”

Section: Methods Reliability Analysismentioning

confidence: 99%

Neural Network Optimization Method and Its Application in Information Processing

Wang

Fan

2021

Mathematical Problems in Engineering

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Neural network theory is the basis of massive information parallel processing and large-scale parallel computing. Neural network is not only a highly nonlinear dynamic system but also an adaptive organization system, which can be used to describe the intelligent behavior of cognition, decision-making, and control. The purpose of this paper is to explore the optimization method of neural network and its application in information processing. This paper uses the characteristic of SOM feature map neural network to preserve the topological order to estimate the direction of arrival of the array signal. For the estimation of the direction of arrival of single-source signals in array signal processing, this paper establishes a uniform linear array and arbitrary array models based on the distance difference vector to detect DOA. The relationship between the DDOA vector and the direction of arrival angle is regarded as a mapping from the DDOA space to the AOA space. For this mapping, through derivation and analysis, it is found that there is a similar topological distribution between the two variables of the sampled signal. In this paper, the network is trained by uniformly distributed simulated source signals, and then the trained network is used to perform AOA estimation effect tests on simulated noiseless signals, simulated Gaussian noise signals, and measured signals of sound sources in the lake. Neural network and multisignal classification algorithms are compared. This paper proposes a DOA estimation method using two-layer SOM neural network and theoretically verifies the reliability of the method. Experimental research shows that when the signal-to-noise ratio drops from 20 dB to 1 dB in the experiment with Gaussian noise, the absolute error of the AOA prediction is small and the fluctuation is not large, indicating that the prediction effect of the SOM network optimization method established in this paper does not vary. The signal-to-noise ratio drops and decreases, and it has a strong ability to adapt to noise.

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Section: Methods Reliability Analysismentioning

confidence: 99%

Neural Network Optimization Method and Its Application in Information Processing

Wang

Fan

2021

Mathematical Problems in Engineering

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“…Machine learning (ML) has proven to be a promising technique for the prediction of thermodynamic and transport properties. Example ML efforts for modeling of transport include the prediction of diffusion for organic compounds in air, binary gas mixtures, − organic compounds in water, − supercritical CO 2 , supercritical water mixtures, binary ionic mixtures, and mixtures of binary solvents and hydrocarbons. − Previous work from our group includes the use of artificial neural networks (ANNs) and random forest methods to predict self-diffusion coefficients of both Lennard-Jones (LJ) fluids and pure single component fluids. , We have also shown that ANNs can model diffusion of LJ fluids in pores, correct finite-size effects in MD simulations of self-diffusion and MS diffusion in binary LJ fluids, and predict diffusion using entirely computational-derived descriptors …”

Section: Introductionmentioning

confidence: 99%

Prediction of Self-Diffusion in Binary Fluid Mixtures Using Artificial Neural Networks

2022

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Artificial neural networks (ANNs) were developed to accurately predict the self-diffusion constants for individual components in binary fluid mixtures. The ANNs were tested on an experimental database of 4328 self-diffusion constants from 131 mixtures containing 75 unique compounds. The presence of strong hydrogen bonding molecules may lead to clustering or dimerization resulting in non-linear diffusive behavior. To address this, self- and binary association energies were calculated for each molecule and mixture to provide information on intermolecular interaction strength and were used as input features to the ANN. An accurate, generalized ANN model was developed with an overall average absolute deviation of 4.1%. Forward input feature selection reveals the importance of critical properties and self-association energies along with other fluid properties. Additional ANNs were developed with subsets of the full input feature set to further investigate the impact of various properties on model performance. The results from two specific mixtures are discussed in additional detail: one providing an example of strong hydrogen bonding and the other an example of extreme pressure changes, with the ANN models predicting self-diffusion well in both cases.

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Evaluation of diffusion and Henry's coefficients of CO2 absorption using Response Surface Methodology and Artificial Neural Network models

Behvandi,

Arefizadeh,

Ghaemi

et al. 2024

Case Studies in Chemical and Environmental Engineering

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Optimization Methodology of Artificial Neural Network Models for Predicting Molecular Diffusion Coefficients for Polar and Non-Polar Binary Gases

Cited by 6 publications

References 25 publications

Neural Network Optimization Method and Its Application in Information Processing

Neural Network Optimization Method and Its Application in Information Processing

Prediction of Self-Diffusion in Binary Fluid Mixtures Using Artificial Neural Networks

Evaluation of diffusion and Henry's coefficients of CO2 absorption using Response Surface Methodology and Artificial Neural Network models

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