An Air Balancing Method Using Artificial Neural Networks for the Ventilation System
- Resource Type
- Conference
- Authors
- Zheng, Kai; Li, Mingwen; Zhang, Tao; Zhang, Xin; Cai, Wenjian; Jing, Gang
- Source
- IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society Industrial Electronics Society (IECON), 2020 The 46th Annual Conference of the IEEE. :4799-4804 Oct, 2020
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineering Profession
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Shock absorbers
Atmospheric modeling
Predictive models
Ventilation
Mathematical model
Ducts
Training
air balancing
ventilation
multilayer perceptron
artificial neural network
optimization
- Language
- ISSN
- 2577-1647
Physical model of ventilation system requiring exact knowledge of all component parameters is usual unavailable in practice. Based on this issue, the objective of this study is to develop an air balancing method to predict the damper position provided the desired airflow rate without requiring a physical model. In the study reported here, the proposed air balancing method consists of a multilayer perceptron model and a damper control method. The multilayer perceptron model is constructed and trained to simulate the non-linear relationship between pressure drop and airflow rate at the damper, and the damper position control method is used to relate pressure drop to operating position of the damper. Experimental tests are carried out to validate the performance of the proposed method. The results show that the proposed method is powerful to balance the ventilation system.