Please use this identifier to cite or link to this item: https://doi.org/10.1002/advs.202003939
Title: Machine-Learning-Assisted Autonomous Humidity Management System Based on Solar-Regenerated Super Hygroscopic Complex
Authors: Zhang, Xueping 
Yang, Jiachen
Qu, Hao 
Yu, Zhi Gen
Nandakumar, Dilip Krishna 
Zhang, Yaoxin 
Tan, Swee Ching 
Keywords: cobalt complexes
humidity management
machine learning
solar regeneration
water harvesting
Issue Date: 1-Feb-2021
Publisher: John Wiley and Sons Inc
Citation: Zhang, Xueping, Yang, Jiachen, Qu, Hao, Yu, Zhi Gen, Nandakumar, Dilip Krishna, Zhang, Yaoxin, Tan, Swee Ching (2021-02-01). Machine-Learning-Assisted Autonomous Humidity Management System Based on Solar-Regenerated Super Hygroscopic Complex. Advanced Science 8 (6) : 2003939. ScholarBank@NUS Repository. https://doi.org/10.1002/advs.202003939
Rights: Attribution 4.0 International
Abstract: High levels of humidity can induce thermal discomfort and consequent health disorders. However, proper utilization of this astounding resource as a freshwater source can aid in alleviating water scarcity. Herein, a low-energy and highly efficient humidity control system is reported comprising of an in-house developed desiccant dehumidifier and hygrometer (sensor), with an autonomous operation capability that can realize simultaneous dehumidification and freshwater production. The high efficiency and energy saving mainly come from the deployed super hygroscopic complex (SHC), which exhibits high water uptake (4.64 g g?1) and facile regeneration. Machine-learning-assisted in-house developed low cost and high precision hygrometers enable the autonomous operation of the humidity management system. The dehumidifier can reduce the relative humidity (RH) of a confined room from 75% to 60% in 15 minutes with energy consumption of 0.05 kWh, saving more than 60% of energy compared with the commercial desiccant dehumidifiers, and harvest 10 L of atmospheric water in 24 h. Moreover, the reduction in RH from 80% to 60% at 32 °C results in the reduction of apparent temperature by about 7 °C, thus effectively improving the thermal comfort of the inhabitants. © 2021 The Authors. Advanced Science published by Wiley-VCH GmbH
Source Title: Advanced Science
URI: https://scholarbank.nus.edu.sg/handle/10635/232509
ISSN: 2198-3844
DOI: 10.1002/advs.202003939
Rights: Attribution 4.0 International
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