Researchers have developed a powerful artificial intelligence (AI) technique to predict the separation of liquid mixtures in vacuum membrane distillation (VMD) processes. By combining computational fluid dynamics (CFD) simulations with machine learning models, the study demonstrates the superior accuracy of Support Vector Machine (SVM) in forecasting temperature distribution – a crucial parameter in membrane separation. This breakthrough paves the way for more efficient and reliable liquid separation technologies, with potential applications in water treatment, pharmaceutical purification, and beyond. Membrane technology and machine learning are set to revolutionize the future of separation processes.
Unraveling the Complexity of Membrane Separation
Membrane separation techniques, such as fluiddynamics’>computational fluid dynamics and machine learning can be applied to a wide range of separation processes, including filtration, chromatography, and adsorption, revolutionizing industries such as water treatment, pharmaceutical manufacturing, and chemical processing.
Author credit: This article is based on research by Yanfen Wei.
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