Multi-objective optimization of air dehumidification membrane module based on response surface method and genetic algorithm

The pressure driven membrane-based air dehumidification technology is a promising energy-efficient technology for the air conditioning systems. To promote the engineering application of membrane dehumidification technology, factor significance analysis and multi-objective optimization of dehumidification membrane module were numerically investigated. Response surface method (RSM) was adopted to design the desired simulation cases and analyze the significance of five key factors, i.e., feed velocity, water vapor permeability coefficient, filling rate, fiber length, fiber diameter, on the membrane dehumidification characteristics. The second-order regression models of the dimensionless dehumidification amount per unit area (m_a*) and the dehumidification rate (γ) were established based on the analysis of variance (ANOVA). It was found that the water vapor permeability coefficient has the most significant effect on γ and m_a*. However, the dehumidification coefficient of performance (COP) and the frictional coefficient (f*Re) are hardly affected by the five independent factors. An average COP of 2.523 implies a good dehumidification efficiency of membrane module. As a multi-objective optimization method, the genetic algorithm was used for the membrane module optimization. The optimal solution represented by Pareto frontier is finally obtained in terms of γ and m_a*. The optimized factor-level combination can be selected from the Pareto solution set according to the actual requirements of energy consumption and dehumidification efficiency.