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|Title:||Advanced demand-controlled ventilation strategies for ACMV systems||Authors:||Li, Bingxu||Keywords:||Engineering::Electrical and electronic engineering::Control and instrumentation||Issue Date:||2022||Publisher:||Nanyang Technological University||Source:||Li, B. (2022). Advanced demand-controlled ventilation strategies for ACMV systems. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/160965||Abstract:||This thesis presents a detailed investigation into the demand-controlled ventilation (DCV) for ACMV systems. Four advanced DCV strategies have been proposed to improve the ventilation control performance in terms of control accuracy, energy efficiency, and practicability. Several key research issues in this field have been addressed in this study. The main contributions of the thesis are summarized as follows: For occupancy-based DCV (air balancing), we proposed two non-iterative air balancing control methods: The first proposed air balancing method is a data-physical hybrid-driven air balancing (DPH-AB) method for multi-zone ventilation systems. A data-driven model is proposed to establish the relationship among the airflow, path pressure drop, pressure and damper angle in the duct system. Specifically, the airflow-path pressure drop relationship is modeled by the denoising autoencoder neural network while the pressure-angle relationship is obtained by Ridge regression method. Then, the physical information of the duct system is considered together with the proposed data-driven model to find the optimal angle for each damper to minimize the total fan power. With the proposed DPH-AB method, the airflow of all terminals can be accurately regulated to the desired value in one-time adjustment while the energy consumption of the duct system can reach the lowest value. It is easy to identify which damper needs to be fully open to save the energy. The experiments verify the accuracy of the proposed DPH-AB method and its energy saving potential. The experiments also demonstrate that the DPH-AB method is robust against the noise in the actual duct system. The second proposed air balancing method is a min-consensus-based distributed air balancing method. This method consists of two stages: In stage 1, the ratio of the actual supplied airflow to the desired value for each zone achieves the agreement by regulating zone damper angles according to a newly designed min-consensus protocol. The convergence of this protocol is guaranteed by rigorous theoretical analysis. In stage 2, the fan voltage is regulated to bring the supplied airflow of each zone to its respective desired value. The proposed method can achieve fast and accurate tracking of the desired airflow for each zone while satisfying the ASHRAE standard that at least one zone damper should be nearly fully open. The proposed method offers the following advantages: a) It does not require the explicit duct model as well as complicated data collection procedures. b) With the proposed method, the airflow supplied to each zone can be adjusted to the desired value in a short time (less than 4 minutes). c) The proposed method is a distributed control method and thus has the benefit of good scalability and reconfigurability. The effectiveness of the proposed method is validated on an experimental testbed of a real ventilation system. For CO2-based DCV, we proposed two advanced ventilation control methods: The first method is a robust model predictive control (MPC) method for multi-zone CO2-based DCV systems. First, a data-driven model is established for ventilation duct network to represent relationships between airflow and damper angles. Then, with the above model and zone IAQ dynamics models, a two-layer tube-based MPC scheme is designed to efficiently handle uncertainties in actual systems: the first-layer MPC generates nominal state trajectories based on nominal systems without uncertainties while the second-layer MPC generates control actions to direct states of actual uncertain system to nominal trajectories. With the proposed strategy, the optimal trajectories of damper angles and fan pressure can be determined to minimize energy consumption while maintaining satisfying IAQ in the presence of uncertainties. The proposed strategy can provide flexible trade-off between energy-efficiency and robustness against uncertainties based on designers’ interest. The second method is a novel CO2-based demand-controlled ventilation method to limit the spread of COVID-19 in indoor environments. First, we establish the quantitative relationship between the COVID-19 infection risk and the average CO2 level during exposure time in the scenario where the outdoor ventilation rate is unknown and varies with time. Then, with this relationship, we propose a sufficient condition for ensuring the COVID-19 reproduction number is less than 1 under the conservative consideration of the number of infectors: the average CO2 level should be maintained no more than an upper bound while the outdoor ventilation rate should be maintained no less than a lower bound. Finally, a control scheme is designed for the ventilation system to make sure the above sufficient condition can be satisfied. Case studies of different indoor environments have been conducted on a testbed of a real ventilation system to validate the effectiveness of the proposed strategy. The proposed strategy can efficiently maintain the reproduction number less than 1 to limit COVID-19 contagion while saving about 30%-50% of energy compared with the fixed ventilation scheme. The proposed strategy offers more practical values compared with existing studies: it is applicable to scenarios where there are multiple infectors, and the number of infectors varies with time; it does not require occupancy detection; it only requires low-cost CO2 sensors and is suitable for mass deployment in most existing ventilation systems.||URI:||https://hdl.handle.net/10356/160965||DOI:||10.32657/10356/160965||Schools:||Interdisciplinary Graduate School (IGS)||Research Centres:||Energy Research Institute @ NTU (ERI@N)||Rights:||This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||IGS Theses|
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Updated on Dec 5, 2023
Updated on Dec 5, 2023
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