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doi:10.3808/jei.202200474
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Development of A Simulation-Based Multi-Objective Optimization Method for Improving the Advanced Oxidizing Capacity of Hydrodynamic Cavitation Reactor - A Case Study of Self-Excited Oscillation Cavity

S. L. Nie1, J. K. Zhou1, H. Ji1 *, Z. Y. Dai2, and Z. H. Ma1

  1. Beijing Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing 100124, China
  2. Hubei Key Laboratory of Critical Zone Evolution, China University of Geosciences, Wuhan, Hubei 430074, China

*Corresponding author. Tel: +86 1067396362; fax: +86 1067391617. E-mail address: jihui@bjut.edu.cn (H. Ji).

Abstract


In this study, a simulation-based multi-objective optimization method is developed for optimizing the structural design of hydrodynamic cavitation (HC) reactor and improving the cavitation effect of HC reactor. The developed method integrates simulation technique of computational fluid dynamics (CFD) and optimization techniques of surrogate model and nondominated sorting genetic algorithm II (NSGA-II) into a general framework. The effect of structure parameters and their interactions on the cavitation effect of the self-excited oscillation cavity (SEOC) are analyzed. Results demonstrate that optimization techniques of surrogate model and NSGA-II can effectively improve the structure and the capacity of SEOC. Simulation results show that the internal vapor volume fraction and outlet vapor volume fraction of SEOC (based on the optimized structure) increase by 13.46 and 38.01%, respectively. The optimized structure of SEOC is also verified experimentally through the degradation experiment of methylene blue solution. The degrees of degra-dation before and after optimization respectively are 10.12 and 16.14%, and the degradation capacity increases by 59.5%. This study will play a significantly guiding role on the optimization design of HC reactor for advanced oxidation processes (AOPs) to obtain the preferable cavitation effect.

Keywords: AOPs, CFD simulation, hydrodynamic cavitation, multi-objective optimization, self-excited oscillation cavity, surrogate model


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