Model-based optimization strategy of chiller driven liquid desiccant dehumidifier with genetic algorithm

Xinli Wang, Wenjian Cai, Jiangang Lu, Youxian Sun and Lei Zhao

Energy, 2015, vol. 82, issue C, 939-948

Abstract: This study presents a model-based optimization strategy for an actual chiller driven dehumidifier of liquid desiccant dehumidification system operating with lithium chloride solution. By analyzing the characteristics of the components, energy predictive models for the components in the dehumidifier are developed. To minimize the energy usage while maintaining the outlet air conditions at the pre-specified set-points, an optimization problem is formulated with an objective function, the constraints of mechanical limitations and components interactions. Model-based optimization strategy using genetic algorithm is proposed to obtain the optimal set-points for desiccant solution temperature and flow rate, to minimize the energy usage in the dehumidifier. Experimental studies on an actual system are carried out to compare energy consumption between the proposed optimization and the conventional strategies. The results demonstrate that energy consumption using the proposed optimization strategy can be reduced by 12.2% in the dehumidifier operation.

Keywords: Liquid desiccant dehumidification system; Energy conservation; Component models; Optimization; Genetic algorithm (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:82:y:2015:i:c:p:939-948

DOI: 10.1016/j.energy.2015.01.103

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