Prediction of the rejection of organic compounds (neutral and ionic) by nanofiltration and reverse osmosis membranes using neural networks

Ammi, Yamina; Khaouane, Latifa; Hanini, Salah

doi:10.1007/s11814-015-0086-y

Cited by 33 publications

(17 citation statements)

References 52 publications

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“…In this work, we have used data available in the literature. [4][5][6][7][8]14,[17][18][19][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46] The first database (DB 1 ) consists of 1394 rejections of 116 organic compounds, the second database (DB 2 ) consists of 980 rejections of 102 organic compounds, and the third database (DB 3 ) consists of 436 rejections of 42 organic compounds. The list of the 116 organic compounds is shown in Supplementary Data A.…”

Section: Data Base Collectionmentioning

confidence: 99%

A Comparison of “Neural Networks and Multiple Linear Regressions” Models to Describe the Rejection of Micropollutants by Membranes

2020

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A rejection process of organic compounds by nanofiltration and reverse osmosis membranes was modelled using the artificial neural networks. Three feed-forward neural networks based on quantitative structure-activity relationship (QSAR-NN models) characterised by a similar structure (twelve neurons for QSAR-NN1, QSAR-NN2, and QSAR-NN3 in the input layer, one hidden layer and one neuron in the output layer), were constructed with the aim of predicting the rejection of organic compounds. A set of 1394 data points for QSAR-NN1, 980 data points for QSAR-NN2, and 436 data points for QSAR-NN3 were used to construct the neural networks. Good agreements between the predicted and experimental rejections were obtained by QSAR-NN models (the correlation coefficient for the total dataset were 0.9191 for QSAR-NN1, 0.9338 for QSAR-NN2, and 0.9709 for QSAR-NN3). Comparison between the feed-forward neural networks and multiple linear regressions based on quantitative structure-activity relationship “QSAR-MLR” revealed the superiority of the QSAR-NN models (the root mean squared errors for the total dataset for the QSAR-NN models were 10.6517 % for QSAR-NN1, 9.1991 % for QSAR-NN2, and 5.8869 % for QSAR-NN3, and for QSAR-MLR models they were 20.1865 % for QSAR-MLR1, 19.3815 % for QSAR-MLR2, and 16.2062 % for QSAR-MLR3).

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Section: Data Base Collectionmentioning

confidence: 99%

A Comparison of “Neural Networks and Multiple Linear Regressions” Models to Describe the Rejection of Micropollutants by Membranes

2020

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“…4(b) shows the linear regression curves of the diffusion coefficient rates calculated by the MLR2 model with the experimental diffusion coefficient rates with regres-sion vectors approaching approval [α, β, R] = [0.5004, 37.4867, 0.7038] of the total phase. The correlation coefficient of MLR2 was 0.7511 in the satisfactory range (0.50 ≤ R < 0.90); 34 with an interval of confidence equal to IC95% = 0.0717. Similarly to MLR1, the results indicated that the MLR2 model showed a good performance for the prediction of the diffusion coefficient of gases.…”

Section: Multiple Linear Regressionsmentioning

confidence: 83%

Usporedna studija predviđanja koeficijenta molekularne difuzije za polarni i nepolarni binarni plin pomoću neuronskih mreža i višestrukih linearnih regresija

et al. 2019

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In the current study, an artificial neural network (ANN) and multiple linear regressions (MLR) have been used to develop predictive models for the estimation of molecular diffusion coefficients of 1252 polar and non-polar binary gases at multiple pressures over a wide range of temperatures and substances. The quality and reliability of each method were estimated in terms of the correlation coefficient (R), mean squared errors (MSE), root mean squared error (RMSE), and in terms of external validation coefficients (Q2ext). The comparison between the artificial neural network (ANN) and the multiple linear regressions (MLR) revealed that the neural network models showed a good predicting ability with lower errors (the roots of the mean squared errors in the total database were 0.1400 for ANN1 and 0.1300 for ANN2), and (root mean squared errors in the total databases were 0.5172 for MLR1 and 0.5000 for MLR2).

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“…The values of the standard deviations (STD), mean (Mean), minimum (Min), and maximum (Max) of the used database are shown in Table 1. Artificial Neural Networks (ANNs) provides an appropriate control strategy for the controlled process [22,23]. The feed forward neural network (FFNN) is one of the most used neural network paradigms in modelling a wide range of nonlinear systems, such as the biological and chemical engineering processes [24,25].…”

Section: Experimental Datamentioning

confidence: 99%

Managing and Control of Nanofiltration / Reverse Osmosis Desalination System: Application of Artificial Neural Network

Adda¹,

Hanini²,

Bezari³

et al. 2020

IJDNE

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Some models of the artificial neural network (ANN) are introduced in the control system of a Nanofiltration / Reverse Osmosis desalination in order to manage the operation and to improve the overall efficiency. This study is carried out on a small-scale prototype of NF/RO seawater desalinate++on plant installed in Saudi Arabia and allowing it to operate with input power. The ANN models are developed to generate the permeate flow rate and recovery after taking into account the temperature, conductivity and pressure of the feed water and the available electrical power. The utilized ANN models after training proved their ability to control the operating of the unit with success. In addition, the statistical tests revealed minimum values of RMSE and MAE. A dimensioning of a photovoltaic system to power the plant is also carried out.

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Prediction of the rejection of organic compounds (neutral and ionic) by nanofiltration and reverse osmosis membranes using neural networks

Cited by 33 publications

References 52 publications

A Comparison of “Neural Networks and Multiple Linear Regressions” Models to Describe the Rejection of Micropollutants by Membranes

A Comparison of “Neural Networks and Multiple Linear Regressions” Models to Describe the Rejection of Micropollutants by Membranes

Usporedna studija predviđanja koeficijenta molekularne difuzije za polarni i nepolarni binarni plin pomoću neuronskih mreža i višestrukih linearnih regresija

Managing and Control of Nanofiltration / Reverse Osmosis Desalination System: Application of Artificial Neural Network

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