Predicting the water production of a solar seawater greenhouse desalination unit using multi-layer perceptron model

Abstract

A solar seawater greenhouse is a type of desalination plant that uses solar energy and seawater to humidify the interior of the greenhouse and produce fresh water from the humid air (humidification-dehumidification process). The produced water is used both for irrigating agricultural crops and for drinking. Many parameters affect the performance of seawater greenhouses. The present study employed an artificial neural network to examine the effective parameters of the greenhouse on the fresh water production such as width, length, the height of the front evaporator, and roof transparency. A suitable structure was obtained for the multi-layer perceptron (MLP) method and the mathematical statistics % AARE, RMSE, and R2, were used to evaluate network performance. The method showed good agreement with the experimental data. Using the optimized created network, the effect of each parameter on the produced fresh water was assessed. Finally, a 125 m wide and 200 m long of greenhouse with a 4 m height of front evaporator and roof transparency of 0.6 that produced 161.6 m3/day of fresh water was introduced as the optimal seawater greenhouse.