2020
DOI: 10.1016/j.desal.2020.114427
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Modeling of forward osmosis process using artificial neural networks (ANN) to predict the permeate flux

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Cited by 86 publications
(20 citation statements)
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“…R 2 values for FFA, MC, and a w model were 0.9241, 0.9058, and 0.9311, respectively. This represents the variability of the parameters by the models, which indicated that the predicted values obtained by the quadratic polynomial equations had a strong correlation with the actual experimental values of Pearson's correlation coefficients (Jawad, et al, 2020). These models were well adapted to the response and were effective to predict the FFA, MC, and a w of the SRB samples.…”
Section: Resultsmentioning
confidence: 95%
See 1 more Smart Citation
“…R 2 values for FFA, MC, and a w model were 0.9241, 0.9058, and 0.9311, respectively. This represents the variability of the parameters by the models, which indicated that the predicted values obtained by the quadratic polynomial equations had a strong correlation with the actual experimental values of Pearson's correlation coefficients (Jawad, et al, 2020). These models were well adapted to the response and were effective to predict the FFA, MC, and a w of the SRB samples.…”
Section: Resultsmentioning
confidence: 95%
“…The prediction model was validated and presented in Table 5. The criterion for fitting the efficiency data of FFA, MC, and a w model is calculated as the % E difference between the experimental and predicted data by Equation (2) (Jawad, et al, 2020). The FFA, MC, and a w values predicted by the RSM model are shown in Table 5 and % E of each variable was 12.62, 7.37, and 8.90, respectively.…”
Section: Resultsmentioning
confidence: 99%
“…In our previous study, an ANN model was developed with nine input variables to predict the membrane flux, but the weights associated with the network were not analyzed for their contribution towards the output [ 20 ]. In this paper, the relative importance of each input variable was assessed on the ANN model output (membrane flux) using a sensitivity analysis approach that employs the partitioning of the weights.…”
Section: Resultsmentioning
confidence: 99%
“…The study also evaluated the importance of the parameters using analysis of variance (ANOVA) that could affect the quality characteristics of FO. In our previous study, an ANN model for a generalized prediction of membrane flux for lab-scale FO desalination [ 20 ]. The model was compared to multiple linear regression and published mathematical models, which showed satisfactory performance.…”
Section: Introductionmentioning
confidence: 99%
“…A literature review found that research on predicting membrane fouling in forward osmosis membrane filtration process by the ANN is rarely exercised. Jawada et al [ 54 ] constructed a multilayer neural network model to predict the permeation flux of forward osmosis. The model studied the influence of the number of neurons and hidden layers on the performance of the neural network, which is helpful in optimizing the development of the network structure.…”
Section: Methods Based On Artificial Neural Network To Predict Membrane Foulingmentioning
confidence: 99%