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|Title:||Model-based optimal control coupled with experimental and numerical analysis for performance of energy recovery ventilator in HVAC system||Authors:||Huynh, Nam Khoa||Keywords:||DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation||Issue Date:||2018||Source:||Huynh, N. K. (2018). Model-based optimal control coupled with experimental and numerical analysis for performance of energy recovery ventilator in HVAC system. Doctoral thesis, Nanyang Technological University, Singapore.||Abstract:||Recently, the demand for energy conservation management attracts much attention, due to the depletion of energy resources and the environmental impacts caused by the increase in energy consumption, especially in tropical countries, such as Singapore. Generally, building sector, which is one of the most important economic sectors, accounts for approximately 40% of total national energy demand in Singapore. As well known, the heating, ventilation, and air-conditioning (HVAC) systems provide thermal comfort for occupants in buildings. However, they consume up to 50% of the total energy usage in the buildings. Therefore, in order to improve the energy efficiency of the HVAC systems, numerous technologies are developed, including the novel configuration, mechanical design, and advanced control algorithm. In particular, the heat or energy recovery is one of the key energy-efficient technologies, which shows potential to overcome the increase in energy consumption in buildings without reduction of the indoor air quality. However, the literature search reveals that only sensible heat is investigated for the conventional heat recovery system, while the latent heat recovery is not studied well. Furthermore, few studies were carried out for the recovery system in the tropics, where little effort was made on the control aspect, which is potentially a strategy for considerable improvement in the efficiency of energy recovery devices. In order to address the issues mentioned above, the present project is conducted to improve overall energy efficiency in HVAC recovery system by computational fluid dynamic (CFD) analysis, experimental validation, and optimal control scheme on important parameters of the energy recovery ventilation (ERV). Throughout the present project, several novelties are contributed to the relevant community, as detailed below. As the first achievement made in this thesis, a novel model for analysis of ERV as a component of HVAC system is developed both mathematically and experimentally, in order to investigate the performance of ERV sensible and latent energy subject to tropical climate conditions. The three-dimensional ERV model with consideration of a semi-permeable membrane is comprehensively investigated for analysis of critical parameters, including the velocity, temperature, and humidity of supply and exhaust airflows, for the improvement of energy efficiency. Subsequently, in order to examine the predictive capability of the mathematical ERV model, the present numerical results are validated by comparison with the experimental data, in which good agreements are achieved. Through the numerical and experimental results, it is demonstrated that the developed membrane-based recovery system is capable of substantial energy savings in buildings, where the sensible and latent effectiveness could be achieved up to 80% and 70%, respectively. As the second achievement, a control model is developed to optimize the operation of HVAC recovery components via a dynamic grey-box ERV model through MATLAB\Simulink environment, since very few examinations of control-based recovery systems are found in the open literature. In order to enhance the numerical model, the experimental investigation of the prototype system, consisting of the membrane-based ERV and two (fresh/exhaust) airflows as well as a sensor system, is conducted for control model identification process. The temperature and carbon dioxide (CO2) concentration level are incorporated into the control model for development of an optimized model predictive control (MPC) strategy for the total energy consumption of building , which maintains the indoor air quality and thermal comfort for occupants. The results show that the zone temperature is regulated better in MPC controller, and the energy consumption of HVAC with MPC controller is considerably less than that of HVAC with PI controller.||URI:||http://hdl.handle.net/10356/73095||DOI:||10.32657/10356/73095||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||IGS Theses|
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Updated on Nov 25, 2020
Updated on Nov 25, 2020
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