Poster 139: Model Identification of Pressure Drop in Membrane Channels with Multilayer Artificial Neural Networks

Authors: Jiang-hang Gu (Sun Yat-sen University, Zhuhai, School of Chemical Engineering and Technology), Jiu Luo (Sun Yat-sen University, Guangzhou, School of Materials Science and Engineering), Ming-heng Li (California State Polytechnic University, Pomona), Yi Heng (Sun Yat-sen University, Guangzhou, School of Data and Computer Science; Sun Yat-sen University, Guangzhou, China)

Abstract: This poster presents the work of identifying a data-driven model of pressure drop in spacer-filled reverse osmosis membrane channels and conducting CFD simulations. The established model correlates the pressure drop with a wide range of design objectives, which enables a quantitative description of the geometric structures and operation conditions for improvement. This way, it aims at optimizing the spacer geometry with minimal effort. Furthermore, a high-performance computing strategy is employed to tackle the resulted intractable computational task in the identification procedure and CFD simulations.

Best Poster Finalist (BP): no

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