Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/83786
Title: Numerical analysis and model-based control of energy recovery ventilator in HVAC system
Authors: Huynh, Nam Khoa
Li, Hua
Soh, Yeng Chai
Cai, Wenjian
Keywords: Convection
Energy efficiency
Issue Date: 2016
Source: Huynh, N. K., Li, H., Soh, Y. C., & Cai, W. (2016). Numerical analysis and model-based control of energy recovery ventilator in HVAC system. 2016 World Congress on Sustainable Technologies (WCST), 76-77.
Abstract: In recent years, the energy conservation demand attracted much attention due to the depletion of energy resource and environmental impact by increasing energy consumption. In particular, heating, ventilation, and air-conditioning (HVAC) systems in buildings is responsible for significant portion of global energy demand. Heat or energy recovery is one of the key energy-efficient technologies, which reveals to overcome the increase of energy consumption in building without reducing the indoor air quality. However, in the conventional heat recovery system, only the sensible heat was recovered, but the latent heat was ignored. In this work, the energy recovery ventilation (ERV) model is developed with semi-permeable membrane, and the performance of sensible and latent energy subject to tropical climate conditions is investigated by both numerical and experimental methods. The 3D ERV model is comprehensively studied first by CFD simulation for analysis of several important parameters, such as the velocity, temperature, humidity of supply, and exhaust air flow. The building energy simulation is then carried out for a conventional HVAC system coupled with an ERV to study the effects of ERV on annual energy consumption. The CFD simulation results show that the sensible and latent effectiveness could be gained at 75% and 65% respectively. Some preliminary experiment is also carried out to validate the simulation results for the impacts of ERV on energy consumption. The dynamic model of HVAC is then constructed and developed in Simulink. The model predictive control strategy for the control of temperature, humidity and CO2 level will be implemented in this model for optimization of ERV control integrated into the whole HVAC system to achieve much more energy saving.
URI: https://hdl.handle.net/10356/83786
http://hdl.handle.net/10220/42472
DOI: 10.1109/WCST.2016.7886595
Rights: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [https://dx.doi.org/10.1109/WCST.2016.7886595].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers
IGS Conference Papers
MAE Conference Papers

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