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.
This work consists to study the storage system effect on thermal performance of an agricultural greenhouse in semi-arid climate case Ghardaia. The data climate is used for predict the energy needs as comparison with another without storage system. The obtained results indicate that the outside needs are less than the not heated with 3 to 5°C during winter night. Were the product rate being 2kg/day. The thermal behavior of the greenhouse was study numerically and the results are corroborating with the literature.
The present paper discusses the feasibility study of an autonomous hybrid PV-Wind power system used for public electrification in the city of Adrar-South of Algeria, with an average consumption of 3445 Wh/day. The system includes a 1KW of PV arrays (78 % solar energy penetration), one wind turbine of 900 W (22 % wind energy penetration), 16 unit batteries (12V-100Ah) and 800 W sized power converters. The main source of power to the energy system is photovoltaic panels, whereas, wind generators are the supported additional sources.
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