Ahmed F. Mashaly

MLP and MLR models for instantaneous thermal efficiency prediction of solar still under hyper-arid environment

Solar still was used to produce water.The instantaneous thermal efficiency (?ith) of solar still was modeled.Multilayer perceptron (MLP) neural network and multiple linear regression (MLR) were used in the modeling process.The MLP model was better than MLR model.Using the MLP model provides ?ith values with high accuracy. The purpose of this study was to determine the viability of modeling the instantaneous thermal efficiency (?ith) of a solar still, using weather and operational data with Multi-Layer Perceptron (MLP) neural network and multiple linear regressions (MLR). This study used weather and operational variables that were hypothesized to affect solar still performance. In the MLP model, nine variables were used as input parameters: Julian day, ambient temperature, relative humidity, wind speed, solar radiation, temperature of feed water, temperature of brine water, total dissolved solids of feed water, and total dissolved solids of brine water. The ?ith was the one node present in the output layer. The same parameters were used in the MLR model. Discussions of advantages and disadvantages are given from different points of view for both models. Performance evaluation criteria indicated that the MLP model was better than the MLR model. The average value of the coefficient of determination for the MLP model was higher by 11.23% than for the MLR model. The average value of the root mean square error for the MLP model (2.74%) was lower compared to the MLR model. The relative errors of predicted ?ith values for the MLP model were mostly in the vicinity of ?10%. Therefore, the MLP model is preferred as a highly precise model in predicting ?ith compared to the MLR model. It is expected that this study could be highly beneficial to those dealing with the design of solar desalination systems.

Comparison of ANN, MVR, and SWR models for computing thermal efficiency of a solar still

ABSTRACT In this paper, the viability of modeling the instantaneous thermal efficiency (ηith) of a solar still was determined using meteorological and operational data with an artificial neural network (ANN), multivariate regression (MVR), and stepwise regression (SWR). This study used meteorological and operational variables to hypothesize the effect of solar still performance. In the ANN model, nine variables were used as input parameters: Julian day, ambient temperature, relative humidity, wind speed, solar radiation, feed water temperature, brine water temperature, total dissolved solids of feed water, and total dissolved solids of brine water. The ηith was represented by one node in the output layer. The same parameters were used in the MVR and SWR models. The advantages and disadvantages were discussed to provide different points of view for the models. The performance evaluation criteria indicated that the ANN model was better than the MVR and SWR models. The mean coefficient of determination for the ANN model was about 13% and14% more accurate than those of the MVR and SWR models, respectively. In addition, the mean root mean square error values of 6.534% and 6.589% for the MVR and SWR models, respectively, were almost double that of the mean values for the ANN model. Although both MVR and SWR models provided similar results, those for the MVR were comparatively better. The relative errors of predicted ηith values for the ANN model were mostly in the vicinity of ±10%. Consequently, the use of the ANN model is preferred, due to its high precision in predicting ηith compared to the MVR and SWR models. This study should be extremely beneficial to those coping with the design of solar stills.

Neural network approach for predicting solar still production using agricultural drainage as a feedwater source

AbstractThis study investigates the application of artificial neural network (ANN) for predicting solar still production (MD). Agricultural drainage water (ADW) was desalinated using a solar still. Important meteorological variables: ambient air temperature, relative humidity, wind speed, and solar radiation, together with the operational variables of flow rate, temperature, and total dissolved solids of feedwater, were considered as input parameters for ANN modeling. The output parameter was MD. The results revealed that the ANN model with five neurons and hyperbolic tangent transfer function was the most appropriate for MD prediction based on the minimum measures of error. The optimal ANN model had a 7–5–1 architecture. The ANN model was also compared to multiple linear regression (MLR). The results indicated that, compared to the MLR model, the ANN model provided better prediction results in all modeling stages. The average of the coefficient of determination between the ANN results and the experimenta...