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|Title:||Ventilation control and ventilation performance of multi-zone air conditioning systems||Authors:||Sun, Zhongwei||Degree:||Ph.D.||Issue Date:||2010||Abstract:||The indoor air quality (IAQ) and energy consumption of buildings have received increasing concern over the last twenty years. The optimal ventilation control of multi-zone VAV Air-conditioning (HVAC) systems provides great potential in reducing energy consumption while ensuring acceptable IAQ and satisfactory thermal comfort. In the last two decades, a number of ventilation control strategies with different degrees of promise have been developed for multi-zone air-conditioning systems. However, most of them cannot always maintain satisfying ventilation performance due to variable indoor thermal comfort and pollution sources of different ventilation zones. Another important issue is that the modeling of the space ventilation usually uses perfect mixing models in conventional dynamic ventilation simulations to test and evaluate the control of air-conditioning systems. However, the complete-mixing air model fails to consider the impact of non-uniform air temperature stratifications on the ventilation performance. Therefore, the aim of this study is to develop an online ventilation optimal control strategies for multi-zone air-conditioning systems to minimize the overall system energy consumption while maintaining satisfactory IAQ. A CFD-based virtual ventilation test system is also developed to evaluate the dynamic ventilation performance by taking account of indoor air stratification phenomena using a CFD-based space temperature offset model. The aim is achieved through addressing the following objectives. (1) Develop a CO₂-based adaptive Demand Controlled Ventilation (DCV) strategy, (2) Develop an indoor air temperature set point reset strategy for critical zones, (3) Develop a model-based indoor air temperature set point resetting strategy for critical zones, and (4) Develop a model-based outdoor air flow rate optimal control strategy for a full air system with primary air handling units. In addition, a CFD-based ventilation test method is developed to test the indoor air ventilation performance in a simulated environment. The CO₂-based adaptive DCV strategy and the indoor air temperature reset strategy are developed to optimize the outdoor air flow rate to satisfy the coincident ventilation and thermal load requirements of different zones. The DCV strategy is further implemented and validated using an independent Intelligent Building Management and Integration platform (IBmanager) in a super high-rise building in Hong Kong by comparing with the original fixed outdoor air flow rate control strategy. Both the site tests and simulation tests showed that this DCV strategy can help to reduce the system energy cost while still maintaining acceptable indoor air quality.
The model-based indoor air temperature set point resetting strategy for critical zones is developed to reduce the unbalance among the required outdoor air fractions of all the zones and to further optimize the indoor air temperature set point of the critical zones. The thermal comfort, indoor air quality and total energy consumption are considered simultaneously. The indoor air temperature set point is optimized using the Genetic Algorithm (GA) based on the trade-offs among the indoor air CO₂concentration, index of predicted mean vote (PMV) and total system energy consumption in the format of a system response-based cost function. The model-based outdoor air flow rate optimal control strategy is developed to reduce the system energy consumption and to optimize the outdoor air flow rate for the full air system with a primary air handling unit. The energy consumption of the primary fan and the energy consumption for cooling outdoor air are considered simultaneously. The outdoor air flow rate set point is optimized based on the trade-offs among the energy consumption of the primary fan and the energy consumption for cooling outdoor air in the system response-based cost function. A simulation package developed on the Transient Simulation Program (TRNSYS) is used as the simulation platform to validate and evaluate the performance of the proposed different ventilation optimal control strategies. The test results showed that about 1.01%~17.7% energy in the system under investigation can be saved when using these optimal control strategies when compared with the conventional ventilation control strategies. The CFD-based virtual ventilation test method is developed for control and optimization of the indoor environment by combining a ventilated room with a ventilation control system. It is demonstrated to be a feasible evaluation tool for the ventilation systems for investigating the ventilation control performance in a simulated environment. A data-based space temperature offset model is developed using the data generated by the CFD model and then used as a virtual temperature sensor in the CFD-based virtual test method. The validated virtual sensor is used to compensate the air temperature non-uniform stratification in occupied rooms and improve thermal comfort in a mechanical ventilated room efficiently.
|Subjects:||Hong Kong Polytechnic University -- Dissertations
Ventilation -- Control
Air conditioning -- Control
Indoor air quality
|Pages:||xxvi, 246 leaves : ill. ; 30 cm.|
|Appears in Collections:||Thesis|
View full-text via https://theses.lib.polyu.edu.hk/handle/200/5906
Citations as of May 22, 2022
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