Investigation of direct contact membrane distillation (DCMD) performance using CFD and machine learning approaches

Abstract

Direct Contact Membrane Distillation (DCMD) is emerging as an effective method for water desalination, known for its efficiency and adaptability. This study delves into the performance of DCMD by integrating two powerful analytical tools: Computational Fluid Dynamics (CFD) and Artificial Neural Networks (ANN). The research thoroughly examines the impact of various factors, such as inlet temperatures, velocities, channel heights, salt concentration, and membrane characteristics, on the process's efficiency, specifically calculating the water vapor flux. A rigorous validation of the CFD model aligns well with established studies, ensuring reliability. Subsequently, over 1000 data points reflecting variations in input factors are utilized to train and validate the ANN. The training phase demonstrated high accuracy, with near-zero mean squared errors and R2 values close to one, indicating a strong predictive capability. Further analysis post-ANN training shed light on key relationships: higher membrane porosity boosts water vapor flux, whereas thicker membranes reduce it. Additionally, it was detailed how salt concentration, channel dimensions, inlet temperatures, and velocities significantly influence the distillation process. Finally, a mathematical model was proposed for water vapor flux as a function of key input factors. The results highlighted that salt mole fraction and hot water inlet temperature have the most effect on the water vapor flux. This comprehensive investigation contributes to the understanding of DCMD and emphasizes the potential of combining CFD and ANN for optimizing and innovating water desalination technology.