This thesis is structured into seven chapters, which can be summarized as follows:
Chapter 1:
In chapter one an introduction to the research topic incorporating the research background and
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highlighting the global environmental problem including the contribution of HVAC system to global warming. An overview about the HVAC system types, structures, design gaps and challenges is part of the chapter. Research problem, significance and aims of the research are highlighted also in the chapter.
Chapter 2:
The chapter will present in detail the literature review of HVAC system mathematical modelling contributions showing the excellence and limitation of previous HVAC system models. The chapter will show at the end the most suitable HVAC mathematical system model representation that is close to HVAC system reality and bridging the gap that has arisen from previous model’s limitations.
Chapter 3:
This chapter will address the literature review of the control strategies employed to regulate the HVAC systems, highlighting the characteristics and advantages of the proposed control strategy in this study over the others previously employed.
Chapter 4:
Obtaining the transfer function matrix extracted from the time domain HVAC system mathematical model as well as reviewing the control techniques theories applications to be employed in the study are highlight in this chapter. The stability criteria for each control technique will be also reviewed in the chapter
Chapter 5:
This section will represent the research methodology and review HVAC system model simulation, responses results and discussions under the open loop system as well as simulation, results and discussions for closed control loop configuration responses for both LE and Direct Nyquist Array control strategies.
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HVAC control strategies comparisons will be undertaken in this chapter demonstrating the closed loop HVAC system responses under both main and alterative control strategies, comparing their performances at the dynamic period characteristics as well as disturbance rejection and control energy dissipation.
Chapter 7:
The chapter will report the research works detailed conclusions, research limitations and recommendations for further future research work.
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Chapter Two
Literature Review
HVAC System Mathematical Modelling
Regardless what type of HVAC system is installed, it consists of many active components that consume significant energy. Consequently, optimizing the energy consumption of HVAC systems is a key measure in achieving building energy efficiency. The “American Society of Heating, Refrigeration, and Air Conditioning Engineers” (ASHRAE) is an internationally recognized organization and has been established to care about developing sustainable technology pertaining to indoor air conditioning, air quality and energy efficiency. There are many measures that have been studied to achieve energy efficient buildings. Apart from the measures used in architectural design and measures of increasing building envelope insulation, integrating the operation of the HVAC system into the Building Management Systems (BMS) so that it is linked with the spaces occupancy has also been approached. However, there are other engineering measures that can be proposed to optimize the operation of HVAC systems within energy efficient buildings. Reliable and accurate HVAC mathematical models as well as a suitable control strategy are part of the efforts to achieve greater efficiency.
Mathematical models are widely used in many disciplines, such as engineering, ecology, agriculture, medicine and economics, aiming to predict and control the performance of the actual related discipline process (Homod, 2013). Furthermore, they are also used for other reasons, such as operator training, simulation and fault diagnosis, as well as for some industrial processes that cannot be constructed within laboratories in order to measure and analyse their behaviour. Typically, HVAC systems are too large for laboratory studies because of the size of components; for instance long airways and large ventilated volumes (Whalley and Abdul-
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Ameer, 2014). A practical solution would be to model the system mathematically. However, the accuracy for the model representation is vital to obtaining reliable predicted results.
Therefore, a modelling process is essential for a system representation by specifying the set of mathematical Equations or input-output relationships describing the system arrangement.
Due to the nonlinearity and the different time lags and inertia, which are inherent characteristics of HVAC systems, it is a challenging exercise to develop an accurate mathematical model describing the real HVAC process over a wide operating range (Mirinejad et al., 2012). In the meanwhile, modelling building envelops as mentioned in Homod (2013) is a complicated procedure when including HVAC system components in the modelling process. The complexity is caused by the fact that modelling any building is not confined to construction components, such as walls, floor, ceiling, windows, etc., but it also includes the consideration of the internal thermal loads, such as lighting, furniture, behaviour and number of people accommodated in the space so that a comprehensive system model can be achieved.
Comprehensive HVAC models can be built by incorporating the principle covering mathematical operation of HVAC processes, which incorporates electrical and mechanical components combined with the thermodynamics and fluid mechanics Equations describing the heat transfer between the building’s envelop and the indoor space; finally, this also includes the thermal load in the final system model. Although complexity increases when such a comprehensive model is considered in the design study, it offers higher system representation accuracy and leads to more reliable system dynamical behaviour analysis. Nonetheless, it is normally difficult for HVAC system models to be completely comprehensive and consequently most of the models built in this domain are fragmented into sub-models reducing the work complexity (Homod, 2013).
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The literature will review most of the contributions presented to model HVAC systems, which has shown that lumped/point wise modelling was the dominant technique. But even when the authors were intending to include the dispersed nature of the system in the model, they have modelled the HVAC system as a series or parallel interconnected point-wise lumped elements employing the mass and energy conservation Equations.
Based on the modelling contributions reviewed for HVAC systems, it has also been noticed that there are three different HVAC mathematical modelling approaches which have been reviewed by many researchers, such as Afram and Janabi-Sharifi (2014a) and Homod (2013).
These approaches are (i) physics-based model or white box, (ii) data driven model or black box and (iii) mixed approach using a data driven and physics-based model or what is called grey box.
One more observation also obtained from the literature is that many researches developed building envelope models only, while others confined the HVAC model’s development to active machinery components, and very few modelled comprehensive HVAC systems that combine the building envelop with the HVAC mechanical and electrical components integrating their thermal interaction with building interior and exterior.