Abstract

Heating, ventilating and air-conditioning (HVAC) systems have been extensively used to provide desired indoor environment in buildings. It is well acknowledged that 25-35% of the total energy use is consumed by buildings, and space heating systems account for 50-60% of the building energy consumption. Furthermore, roughly half of the energy consumed goes to operation of heating systems. In the past few years the energy use has shown rapid growth. Therefore, it is necessary to design and operate HVAC systems to reduce energy consumption and improve occupant comfort. To improve energy efficiency, HVAC systems should be optimally controlled and operated. This study focuses on developing advanced control strategies and fault tolerant control (FTC) using information from fault detection and diagnosis (FDD) for hot water heating (HWH) systems. To begin with, HWH system dynamic models are developed based on mass, momentum and energy balance principles. Then, embedded intelligent control strategies: fuzzy logic control and fuzzy logic adaptive control are designed for the overall system to achieve better performance and energy efficiency. Moreover, in designing the advanced control strategies, the parameter uncertainty and noise from measurement and process are taken into account. The extended Kalman filter (EKF) technique is utilized to handle system uncertainty and measurement noise, and to improve system control performance. After that, a supervisory control strategy for the HWH system is designed and simulated to achieve optimal operation. Finally, model-based FDD methods were developed by using fuzzy logic to detect and isolate measurement and process faults occurring in HWH systems. The FDD information was employed to design model-based FTC systems for various faults and to extend the operating range under failure situations. The contributions of this study include the development of a large scale dynamic model of a HWH system for a high-rise building; design of fuzzy logic adaptive control strategies to improve energy efficiency of heating systems and design of model-based FTC systems by using FDD information.