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Optimal Control of Multi-Zone Air Conditioning System to Achieve Thermal Comfort and Minimize Energy Consumption

Joleini, Mohammad Ali | 2025

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 57860 (08)
  4. University: Sharif University of Technology
  5. Department: Mechanical Engineering
  6. Advisor(s): Moradi, Hamed
  7. Abstract:
  8. Air conditioning systems are one of the main parts of buildings, and their performance is very important, especially during the peak heat and cold days, both in terms of providing comfort area for the people and in terms of energy consumption. These systems control the temperature and humidity inside one or more target zones using heating, cooling, humidification, and dehumidification processes. One of the most common solutions to optimize the performance of air conditioning systems is the use of control systems. In this study, a nonlinear multi input-multi output dynamic model of a two-zone variable air volume & temperature air handling unit is considered for operation in the summer season. In this model, the temperature, absolute humidity, and carbon dioxide concentration of the zones’ air are controlled by manipulation of the air flow delivered to the zones, the water flow in the coils, and the opening rate of the valves and dampers. The research continues by explaining how to derive the equations and the limitations of the system under study. Then, a performance criterion for the system is defined with respect to the three goals of establishing thermal comfort, keeping the concentration of carbon dioxide within a safe range for human breathing, and minimizing energy consumption. Then, in two consecutive chapters, two control methods for the aforementioned system are designed and investigated. First, a distributed fractional-order PID (DFOPID) control structure was designed. The coefficients of the DFOPID controller were adjusted using the particle swarm optimization algorithm and based on the defined performance criterion on the weather data of Tehran on July 10, 2024 (as a specific instance for investigathion). Then, the robustness of this controller with changed data was evaluated. In the next chapter, a distributed fractional-order model predictive control (DFOMPC) structure was designed by considering the appropriate timing for the prediction horizon. The optimization core of the DFOMPC controller is also the particle swarm optimization algorithm. The robustness of this controller to disturbances was also investigated with the data used for the DFOPID controller. According to the comparison of the results of the two control methods, the performance of the DFOMPC controller is better, especially in optimizing the performance criterion, both with nominal and disturbed data. Among the innovations of this research, we can mention the expansion of the model and the more accurate system equations, the implementation of the distributed fractional order PID and predictive control structures (DFOPID & DFOMPC), and the development of a new scheduling program for the prediction horizon in DFOMPC
  9. Keywords:
  10. Thermal Comfort ; Fractional Order Proportional Integrated Derivative (FOPID)Controller ; Multi-Zone Air Conditioning System ; Distributed Fractional Order Proportional Integrated Derivative (PID)Control ; Distributed Fractional Order Model Predictive Control ; Power Consumption Minimization ; Energy Consumption Reduction

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