A model-based air balancing method of a ventilation system

In this paper we propose a novel and easily implementable model-based air balancing method for a ventilation system. The proposed method provides a well-defined form of balancing for a ventilation system, which can be effectively solved. The method follows three procedures: (i) mathematical modeling...

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Main Authors: Jing, Gang, Cai, Wenjian, Zhai,Deqing, Liu, Shuai, Cui, Can
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2020
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Online Access:https://hdl.handle.net/10356/141223
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Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-141223
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spelling sg-ntu-dr.10356-1412232020-06-05T02:54:28Z A model-based air balancing method of a ventilation system Jing, Gang Cai, Wenjian Zhai,Deqing Liu, Shuai Cui, Can School of Electrical and Electronic Engineering Centre for System Intelligence and Efficiency Centre for E-City Engineering::Electrical and electronic engineering Ventilation System Air Balancing In this paper we propose a novel and easily implementable model-based air balancing method for a ventilation system. The proposed method provides a well-defined form of balancing for a ventilation system, which can be effectively solved. The method follows three procedures: (i) mathematical modeling for duct system which is based on steady-state pressure balance; (ii) model parameters identification by supervised machine learning; (iii) determination of damper positions based on the model. The performance of this method is validated through testing in a duct testbed with five terminals. The results show that the final flow rates in all terminals match with the desired values with no more than 6.8% relative error. NRF (Natl Research Foundation, S’pore) 2020-06-05T02:54:28Z 2020-06-05T02:54:28Z 2018 Journal Article Jing, G., Cai, W., Zhai, D., Liu, S., & Cui, C. (2018). A model-based air balancing method of a ventilation system. Energy & Building, 174, 506-512. doi:10.1016/j.enbuild.2018.06.044 0378-7788 https://hdl.handle.net/10356/141223 10.1016/j.enbuild.2018.06.044 2-s2.0-85049950184 174 506 512 en Energy & Buildings © 2018 Elsevier B.V. All rights reserved.
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
Ventilation System
Air Balancing
spellingShingle Engineering::Electrical and electronic engineering
Ventilation System
Air Balancing
Jing, Gang
Cai, Wenjian
Zhai,Deqing
Liu, Shuai
Cui, Can
A model-based air balancing method of a ventilation system
description In this paper we propose a novel and easily implementable model-based air balancing method for a ventilation system. The proposed method provides a well-defined form of balancing for a ventilation system, which can be effectively solved. The method follows three procedures: (i) mathematical modeling for duct system which is based on steady-state pressure balance; (ii) model parameters identification by supervised machine learning; (iii) determination of damper positions based on the model. The performance of this method is validated through testing in a duct testbed with five terminals. The results show that the final flow rates in all terminals match with the desired values with no more than 6.8% relative error.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Jing, Gang
Cai, Wenjian
Zhai,Deqing
Liu, Shuai
Cui, Can
format Article
author Jing, Gang
Cai, Wenjian
Zhai,Deqing
Liu, Shuai
Cui, Can
author_sort Jing, Gang
title A model-based air balancing method of a ventilation system
title_short A model-based air balancing method of a ventilation system
title_full A model-based air balancing method of a ventilation system
title_fullStr A model-based air balancing method of a ventilation system
title_full_unstemmed A model-based air balancing method of a ventilation system
title_sort model-based air balancing method of a ventilation system
publishDate 2020
url https://hdl.handle.net/10356/141223
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