Experimental study and parameter optimization of desiccant wheel-assisted atmospheric water harvesting system based on NSGA-II

IF 9.9 1区工程技术 Q1 ENERGY & FUELS

Energy Conversion and Management Pub Date : 2025-05-23 DOI:10.1016/j.enconman.2025.119943

Fanzhao Kong , Zhongbao Liu , Chenghu Lin , Zepeng Wang , Wei Wang , Shuyi Yao , Wei Zhang , Junxiong Zhang

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引用次数: 0

Abstract

The desiccant wheel-assisted atmospheric water harvesting system (DW-AWHS) effectively mitigates the performance degradation of a heat pump-based atmospheric water harvesting system (HP-AWHS) in arid desert climates by elevating the air dew point temperature. Comparative multi-environmental performance experiments between the HP-AWHS and DW-AWHS revealed that the DW-AWHS exhibits reduced susceptibility to environmental fluctuations. The water harvesting rate (WHR) and dew point temperature rise (ΔT_DP) are the core metrics for evaluating the DW-AWHS performance. However, an inherent trade-off exists between these two objectives during system optimization. This study proposes a hybrid optimization framework integrating response surface methodology (RSM) and the non-dominated sorting genetic algorithm-II (NSGA-II) to resolve this multi-objective optimization challenge. First, four critical design variables were identified: treated air fan speed (n_tr), regeneration air fan speed (n_re), condensation temperature (T_co), and regeneration power (W_he). A Box-Behnken experimental design was implemented to construct regression models. Analysis of variance (ANOVA) validated the adequacy and reliability of the regression models. Response surface analysis elucidated the interactive effects between paired design parameters. Subsequently, the Pareto optimal frontier was derived using NSGA-II. Sensitivity analysis demonstrated that WHR is predominantly influenced in the order W_he > T_co > n_re > n_tr, while ΔT_DP is governed by T_co > W_he > n_re > n_tr. Under optimized conditions (20 °C, 40 % RH), the system achieved a WHR of 0.2164 kg/h and a ΔT_DP of 23.72 °C, corresponding to operational parameters of n_tr = 3.43 krpm, n_re = 2.99 krpm, T_co = 17.89 °C, and W_he = 0.993 kW. Experimental validation confirmed the prediction accuracy, with deviations between simulated and measured results below 3 %, thereby substantiating the robustness of the proposed methodology.

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基于NSGA-II的干燥剂轮辅助大气集水系统试验研究及参数优化

干燥剂轮辅助大气集水系统（DW-AWHS）通过提高空气露点温度，有效地缓解了干旱沙漠气候条件下基于热泵的大气集水系统（HP-AWHS）的性能下降。HP-AWHS与DW-AWHS的多环境性能对比实验表明，DW-AWHS对环境波动的敏感性较低。集水速率（WHR）和露点温升（ΔTDP）是评价DW-AWHS性能的核心指标。然而，在系统优化期间，这两个目标之间存在固有的权衡。本研究提出了一种结合响应面法（RSM）和非支配排序遗传算法（NSGA-II）的混合优化框架来解决这一多目标优化挑战。首先，确定了四个关键设计变量：处理风机转速（ntr）、再生风机转速（nre）、冷凝温度（Tco）和再生功率（wh）。采用Box-Behnken实验设计构建回归模型。方差分析（ANOVA）验证了回归模型的充分性和可靠性。响应面分析阐明了成对设计参数之间的交互作用。随后，利用NSGA-II推导出Pareto最优边界。灵敏度分析表明，WHR受影响的顺序依次为：Tco的在负阻元件比;而ΔTDP则由Tco >；当在负阻元件比;正常。关系在优化条件（20℃，40% RH）下，系统的WHR为0.2164 kg/h， ΔTDP为23.72℃，对应的运行参数为ntr = 3.43 krpm, nre = 2.99 krpm, Tco = 17.89℃，Whe = 0.993 kW。实验验证证实了预测的准确性，模拟结果与测量结果之间的偏差低于3%，从而证实了所提出方法的稳健性。

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来源期刊

Energy Conversion and Management 工程技术-力学

CiteScore

19.00

自引率

11.50%

发文量

1304

审稿时长

17 days

期刊介绍： The journal Energy Conversion and Management provides a forum for publishing original contributions and comprehensive technical review articles of interdisciplinary and original research on all important energy topics. The topics considered include energy generation, utilization, conversion, storage, transmission, conservation, management and sustainability. These topics typically involve various types of energy such as mechanical, thermal, nuclear, chemical, electromagnetic, magnetic and electric. These energy types cover all known energy resources, including renewable resources (e.g., solar, bio, hydro, wind, geothermal and ocean energy), fossil fuels and nuclear resources.