A mathematical method and artificial neural network modeling to simulate osmosis membrane’s performance

Salami, E. S.; Ehetshami, M.; Karimi-Jashni, Ayoub; Salari, Marjan; Sheibani, Solmaz Nikbakht; Ehteshami, A.

doi:10.1007/s40808-016-0261-0

Cited by 27 publications

(10 citation statements)

References 26 publications

(20 reference statements)

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“…According to Garg et al [26] et salami et al [50] and further confirmation of the permeate recovery prediction of the performance membrane desalination process, the ANN model proposed in this study yielded satisfactory results that can be used as a model for controlling the desalination membrane filtration performance. Since the neural network model shows good accuracy in predicting these parameters, it could be considerate that the ANN the best-simulated model and is suitable for prediction of seawater desalination performance hybrid process NF/RO in the mean of improving the quality of the water produced and reduce water production costs.…”

Section: Comparison Of the Ann Model And Mlr Modelsupporting

confidence: 59%

Modeling and optimization of small-scale NF/RO seawater desalination using the artificial neural network (ANN)

Adda

Hanini

Bezari

et al. 2021

Environmental Engineering Research

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The performance of seawater hybrid NF/RO desalination plant including permeate conductivity; permeate flow rate and permeate recovery. Under different feed parameters time, inlet temperature, inlet pressure, inlet conductivity and inlet flow rate were modelled by Artificial Neural Network (ANN) back-propagation based on Levenberg-Marquardt training algorithm. The optimal ANN model had a 5-8-3 architecture with a hyperbolic tangent transfer function in hidden layer and linear transfer function at the output layer. The ability of ANN performed model was compared with multiple linear regression (MLR). The results show that MLR is not satisfactory for predicting the performance of NF/RO hybrid desalination process with a correlation coefficient about 0.6. The trained ANN model has presented a good agreement between the prediction and the experimental data during the training with reasonable statistical metrics values (RMSE, MAE and AARD). The coefficient of determination values for the prediction of permeate conductivity, permeate flow rate and recovery by ANN were 0.969, 0.942, and 0.963, respectively. Therefore, the ANN model can successfully predict the performance of NF/RO hybrid seawater desalination plant.

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Section: Comparison Of the Ann Model And Mlr Modelsupporting

confidence: 59%

Modeling and optimization of small-scale NF/RO seawater desalination using the artificial neural network (ANN)

Adda

Hanini

Bezari

et al. 2021

Environmental Engineering Research

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show abstract

“…It is proved that there is a good agreement between model predictions and available data. Salam et al [54] successfully achieved "the development of artificial neural network models for different types of reverse osmosis membranes under certain operating conditions. In addition to the influence of experimental data, the influence of hidden layers and the number of neurons should also be considered in model prediction.…”

Section: Application Of Artificial Neural Network In Wastewater Treat...mentioning

confidence: 99%

“…Therefore, Salami et al[48] proposed a BP neural network model in 2016 to simulate 8 kinds of SWRO membranes for seawater desalination.The established model is used to simulate and estimate the output TDS corresponding to the input flow rate, temperature and recovery rate. The data set was generated by ROSA software.…”

mentioning

confidence: 99%

Application of Artificial Neural Network in Chemical Separation Process

Wang

Zhu

2022

AJOCS

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The chemical separation process is a necessary unit operation to obtain qualified purity products. The design and operation control of the separation device are very important. Due to the numerous parameters that affect the separation efficiency and product purity, it is very difficult to optimize by traditional methods. Artificial neural network (ANN) has strong fault tolerance because of its self-learning, self-organization, self-adaptive and strong nonlinear function approximation ability. ANN can be used to map the complex nonlinear relationship between dependent variables and independent variables, and can be used for the design and control of chemical separation devices. This paper summarizes the characteristics of artificial neural network and several important artificial neural network models, and discusses the application of artificial neural network in different chemical separation processes.

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“…In general, the mathematical model of ANN can be written in a simple way as shown in Eq. (1) [17,18].…”

Section: Artificial Neural Network Conceptmentioning

confidence: 99%

Image Georeferencing using Artificial Neural Network Compared with Classical Methods

Din

2021

eijs

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Georeferencing process is one of the most important prerequisites for various geomatics applications; for example, photogrammetry, laser scan analysis, remotely sensing, spatial and descriptive data collection, and others. Georeferencing mostly involves the transformation of coordinates obtained from images that are inhomogeneous due to accuracy differences. The georeferencing depends on image resolution and accuracy level of measurements of reference points ground coordinates. Accordingly, this study discusses the subject of coordinate’s transformation from the image to the global coordinates system (WGS84) to find a suitable method that provides more accurate results. In this study, the Artificial Neural Network (ANN) method was applied, in addition to several numerical methods, namely the Affine divided difference, Newton’s divided difference, and polynomial transformation. The four methods were modelled and coded using Matlab programming language based on an image captured from Google Earth. The image was used to determine reference points within the study area (University of Baghdad campus). The findings of this study showed that the ANN enhanced the results by about 50% in terms of accuracy and 90% in terms of homogeneity, compared with the other methods.

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A mathematical method and artificial neural network modeling to simulate osmosis membrane’s performance

Cited by 27 publications

References 26 publications

Modeling and optimization of small-scale NF/RO seawater desalination using the artificial neural network (ANN)

Modeling and optimization of small-scale NF/RO seawater desalination using the artificial neural network (ANN)

Application of Artificial Neural Network in Chemical Separation Process

Image Georeferencing using Artificial Neural Network Compared with Classical Methods

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