Study on parameters effective on the performance of a humidification-dehumidification seawater greenhouse using support vector regression

Abstract

A Seawater greenhouse is a desalination plant that, using solar energy and seawater, humidifies the interior of the greenhouse and produces water from the humid air. The produced water can be used both for irrigation and human consumption. Many factors affect the performance of a seawater greenhouse. In this study, using artificial neural networks, the effects of greenhouse width and length, first evaporator height, and roof transparency on the water production and energy consumption of a seawater greenhouse were examined with the help of Support Vector Regression (SVR) method. A suitable structure was obtained for this method, and %AARE, RMSE and R2 statistic measures were used for evaluating the performance of the network. This method shows the favorable correspondence with experimental data. Using the prepared optimized network, the effect of each parameter on water production and energy consumption was examined for a wide range of variations in the parameter values. Finally, a 125m wide, 200m long greenhouse with a 4m high evaporator and permeability of 0.6 was found to be the optimum configuration, offering a daily water production of 161.6m3 for 1.558kWh of energy consumed per cubic meter of water produced.