Comparison of Neural Network Models in the Estimation of the Performance of Solar Still Under Jordanian Climate

Abstract

Abstract-Three

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

“…On the other hand, the mathematical algorithms used for these computations are still complicated, involving the solution of complex equations and requiring large computational power and need a considerable long computational time [8]. With the progress in computer technology and mathematical modeling techniques, the employment and usage of Artificial Neural Networks (ANNs) in solar desalination by stills could achieve results that are not facilely obtained with classical modeling techniques.…”

“…(2016) [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15] www.deswater.com doi: 10.1080/19443994.2016.1193770 face of growing demand for water. One of the interests for using desalinated water in the irrigation and agricultural sector, in general, is that the use of desalinated water increases the yield [4] and quality of some agricultural products and at the same time results in lower water consumption and recovery of salinityaffected soils [5].…”

Predictive model for assessing and optimizing solar still performance using artificial neural network under hyper arid environment

“…For this reason, this study aims to examine the effectiveness of the solar still by modeling its performance with different types of water (seawater, groundwater and agricultural drainage water). Previous studies, for example, Santos et al (2012) and Hamdan et al (2013) indicated that there is a gap in this area on the development of inputs and outputs. All the main meteorological and operational data that may affect the desalination process and in particular the processes of evaporation and condensation were not included.…”

“…Also, Lecoeuche and Lalot (2005) showed an application of ANNs to forecast the in-situ daily performance of solar air collectors where the output of the ANN is the outlet temperature of the collector, and inputs to the network are the solar radiation and the thermal heat loss coefficients. Hamdan et al (2013) used three ANN models (Feed forward, Elman, and Nonlinear Autoregressive Exogenous (NARX) networks) to find the performance of triple solar still operating under Jordanian climate. They utilized nine input variables namely time, hourly variation of cover glass temperature, water temperature in the upper basin, water temperature in the middle basin, water temperature in the lower basin of the triple basin still, distillate volume, ambient temperature, plate temperature and hourly solar intensity as inputs to the network.…”

Control of PV Panel System Temperature Using PID Cuckoo Search

“…There are a lot of previous studies on PV panel's performance forecasting using ANN algorithms. For example, Hamdan et al (2013) used three types of ANN (NARX, Elman and feed forward), and the best model for solar panel performance prediction was the feedforward [6]. Di Piazza et al (2013) studied the prediction of solar radiation using feed forward time delay and NARX and both methods were suitable for the system [7].…”