Simulation of water desalination based on humidification and dehumidification by using the artificial neural network
Volume Title: 1
1Chemical Engineering, University Amirkabir of Technology, Tehran, Iran
2424 Hafez Ave, Tehran, Iran.Amirkabir University
Population growth, rising living standards in recent decades, access to freshwater have been challenging for many countries worldwide. Solar water desalination based on humidification and dehumidification (DHD) is one of the most efficient small-scale desalination methods that are suitable for arid climates. In this study, an artificial neural network method was used to simulate (DHD) system. The effect of parameters on inlet water temperature humidity content and mass flow rate of water as an artificial neural network input on the efficiency of the DHD system as an artificial neural network output is investigated. The implementation of smart simulation methods requires a dataset, which in this study small dataset consisting of 20 data is used. The simulation and laboratory data are well matched and the average relative error for the artificial neural network used is equal to 1.6 %. The maximum productivity for the system is concluded at the inlet water temperature of about 87°C and the mass flow rate of 4 kg/min which is equal to 2.2 kg/h.