Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/151205
Title: Model-based optimization strategy for a liquid desiccant cooling and dehumidification system
Authors: Ou, Xianhua
Cai, Wenjian
He, Xiongxiong
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: Ou, X., Cai, W. & He, X. (2019). Model-based optimization strategy for a liquid desiccant cooling and dehumidification system. Energy and Buildings, 194, 21-32. https://dx.doi.org/10.1016/j.enbuild.2019.04.019
Journal: Energy and Buildings
Abstract: In this paper, a model-based optimization strategy for a liquid desiccant cooling and dehumidification (LDCD) system is proposed to improve system energy efficiency. The energy models of the LDCD system are established to predict system energy consumption under different operating conditions. To minimize the system energy consumption while maintaining the system thermal performance, the system energy consumption and thermal performance indicators are normalized by introducing a weight factor in cost function, then an optimization problem considering the interactions between components and system constraints is formulated. An improved self-adaptive firefly algorithm with fast convergence rate is proposed to solve the optimization problem and obtain the optimal set-points for control settings. Tests on an experimental apparatus are carried out to verify the energy saving potential of optimal control strategy under different weight factors and operating conditions. The results indicate that the energy consumption of LDCD system in the proposed optimization strategy is reduced by 12.49% over the conventional strategy. Meanwhile, the energy saving potential of the optimal control strategy is more remarkable for high cooling and dehumidification load. The proposed optimal control strategy can work well for applications in control and energy efficiency improvement of the existing dehumidification systems.
URI: https://hdl.handle.net/10356/151205
ISSN: 0378-7788
DOI: 10.1016/j.enbuild.2019.04.019
Rights: © 2019 Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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