Operation parameter investigation and optimization for electrolyte membrane dehumidifier based on orthogonal experiment and numerical simulation

ABSTRACT

PEM-based electrolyte dehumidification is suitable for small-space and high-precision occasions, but the influencing level of various operating conditions is still unclear. This paper investigated the four factors (air temperature, humidity, flowrate, and applied voltage) that affect the dehumidifier’s performance (moisture removal rate and current density). Through orthogonal experiments, it was found that process air temperature was the most significant parameter affecting performance, followed by air humidity and applied voltage. The moisture removal rate and current density rose almost linearly as the air temperature increased. Through parameter analysis, the best working conditions of the dehumidifier were found as: applied voltage = 3 V, air flowrate = 2.7 × 10⁻² g/s, air temperature = 35°C and air humidity = 90%. Then, by fitting empirical formulas of the PEM conductivity and water content difference on both sides of the membrane, an operation simulation of dehumidification process was established by MATLAB Simulink. Compared with experiments, the deviations (~10%) were acceptable. This simulation used a 100 L box and a 3.5 × 3.5 cm² membrane dehumidifier as a case study, and the optimized parameters derived from the experiment were used. Under typical air conditions in Guangzhou, to reduce the inside humidity to 60%, the dehumidification time for the cabinet was the shortest in autumn, about 5.6 hours. Besides, dehumidification energy consumption was the highest in summer, at 39.4 kJ. And energy consumption was the lowest in winter under the same dehumidification time. This research contributes to the operation optimization of electrolyte dehumidifiers.

KEYWORDS:

• Electrolyte membrane dehumidifier
• orthogonal experiment
• operation simulation
• performance optimization
• energy consumption

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Acknowledgments

This work was supported by the National College Student Innovation and Entrepreneurship Training Program under Grant (202010561051), the National Natural Science Foundation of China (51876067) and the Natural Science Fund for Distinguished Young Scholars of Guangdong Province (2018B030306014). Special thanks to Hongdou LIU and Lijuan HUANG for their help in the experiments.

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [51876067]; National College Student Innovation and Entrepreneurship Training Program [202010561051]; Natural Science Foundation of Guangdong Province for Distinguished Young Scholars [2018B030306014].