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

Abstract

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.

Integrated Modeling of Hybrid Nanofiltration/Reverse Osmosis Desalination Plant Using Deep Learning-Based Crow Search Optimization Algorithm

“…The independent variables consisted of temperature ( • C), time (h), pressure (kg/cm 2 ), flow feed (m 3 /h), and conductivity feed (µS/cm), while the dependent variable was permeating conductivity (PC) (µS/cm). The data were obtained from the Saline Water Desalination Research Institute (SWDRI) of the Saline Water Conversion Corporation (SWCC), Saudi Arabia [36], and open-source data were collected from [8]. Refer to [36] for details of the experimental analysis and set-up discussion; the diagram is presented in Figure 1a.…”

“…On the other hand, the global desalination landscape is dominated by the RO system, with a staggering 90% of facilities around the world harnessing this technique. The RO method leverages semi-permeable membranes to sieve out salts and other unwanted minerals from water [8]. Its popularity is not unwarranted; RO is recognized for its economical nature, impressive salt rejection rates, and the high quality of the resultant water.…”

Prediction of Groundwater Contamination in an Open Landfill Area Using a Novel Hybrid Clustering Based AI Model

“…Several studies have focused on developing predictive models that use a combination of statistical and machine learning techniques to identify the factors that contribute to groundwater contamination [1][2][3][4]. These models consider various parameters such as land use, hydrogeological characteristics, and environmental factors, to identify areas that are most vulnerable to contamination.…”

“…Groundwater samples were collected from various locations, including residential, industrial, and agricultural areas, and analyzed for various parameters, including heavy metals, pesticides, and organic compounds.Groundwater contamination prediction is a crucial component of groundwater management, as it helps to identify potential sources of contamination and take appropriate measures to prevent or mitigate the impacts of contamination. In recent years, there has been signi cant research in the eld of groundwater contamination prediction, aimed at developing reliable models and tools for assessing the vulnerability and risk of groundwater contamination.Several studies have focused on developing predictive models that use a combination of statistical and machine learning techniques to identify the factors that contribute to groundwater contamination [1][2][3][4]. These models consider various parameters such as land use, hydrogeological characteristics, and environmental factors, to identify areas that are most vulnerable to contamination.…”

Utilization of Data-Driven Methods in Solar Desalination Systems: A Comprehensive Review

“…Since the experimental works are costly and time-consuming, it would be useful to propose models for performance prediction and assessment of the desalination systems. Data-driven methods, with outstanding ability in modeling of complex systems, would be attractive options for performance forecasting of desalination systems (Gao et al, 2007;Chauhan et al, 2020;Adda et al, 2021). These methods have shown their outstanding performance in a wide variety of applications such as predicting the properties of materials (Ramezanizadeh et al, 2019a;Ramezanizadeh et al, 2019b), fault diagnosis (Venkatasubramanian and Chan, 1989), etc.…”