Parametric optimization of single-effect distillation via artificial neural network for desalination process
Year: 2025
Type: article
Abstract: With the growing global demand for clean water, efficient and sustainable desalination technologies are becoming increasingly critical. This study explores the optimization of a single-effect distiller (SED) for desalination using artificial neural network modeling, specifically a multi-layer perceptron (MLP), benchmarked against support vector regression (SVR) and linear and polynomial regression models. The models predict the distillate mass flow rate under varying operating conditions, including hot-water temperature, cold-water temperature, and vacuum pressure. Among the models, the MLP demonstrates superior performance, achieving an R 2 of 0.9671 and an RMSE of 0.3867 g/min, compared to those of SVR with R 2 of 0.9658 and RMSE of 0.3884 g/min. Contour plots generated by the MLP model provided actionable insights, identifying optimal operating conditions for maximizing the distillate mass flow rate. Specifically, the optimal performance is achieved with high hot-water temperatures (above 85 °C), low cold-water temperatures (approximately 22 °C), and vacuum pressures below 10 kPa. These conditions are practical, as room-temperature seawater can effectively cool the condenser without additional energy for cooling, while solar energy can heat the hot water. A conceptual design for a solar-powered SED that integrates these findings, offering a sustainable and energy-efficient desalination solution is proposed. The machine learning-driven optimization framework presented here provides a valuable pathway for addressing global water scarcity challenges.
Source: Desalination
Authors: Darren Tze Huei Lee, Ying Pio Lim, Heng Kam Lim, Yew Mun Hung
Institutions: Monash University Malaysia, Monash University
Language: English
Field: Energy
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,580
OpenAlex ID: W4410237187