An optimization model for developing time-based preventive maintenance strategy for cooling coils in air conditioning systems based on genetic algorithm

Abstract

With the rapid growth of China's air conditioning market, the issue of low energy efficiency in air conditioning usage has become increasingly prominent. One of the main reasons for this challenge is the lack of maintenance.
Fixed-period and corrective maintenance strategies, currently used in air conditioning coil maintenance, are no longer able to meet the actual usage requirements. The enhanced maintenance strategy should have the capability to ensure the operational performance of the air conditioning system, while also reducing costs and energy consumption. In addition, as a composite system, air conditioning comprises various subsystems, each with distinct properties, functions, and energy losses. This implies that various subsystems require distinct maintenance measures and strategies.
To address the aforementioned issues, this study proposes an optimization model for the coil, a subsystem in the air conditioning system. The optimization model can generate a time-based preventive maintenance strategy to fulfill the requirements of owners. The cost factor, operational performance factor, and environmental factor are taken into consideration.
This study addresses the research gap in optimization of coils in air conditioning systems. Furthermore, the study innovatively pointed out that when an enterprise participates in the carbon emission permit trading system, the operational cost of its air conditioning system includes not only the energy cost paid directly to the energy supplier, but also the potential loss in carbon emission permits, which represents an opportunity cost resulting from energy consumption. The study incorporates environmental factors into the cost analysis by including the cost of carbon emission permits to account for the impact of excessive emissions. The study's findings can address owners' needs for an enhanced maintenance strategy and fill the gap in current research on coil maintenance strategies.
Two simulation scenarios were designed to evaluate the model's performance. The genetic algorithm was employed to solve the model's objective function using Python. In contrast to the traditional fixed-period maintenance strategy, the new time-based preventive maintenance strategy generated from the model can ensure the minimization of total relevant cost (TRC) of the air conditioning system while still meeting performance criteria. Experimental simulations can be used to demonstrate the scientific validity and effectiveness of this model.

Date of Award	Jul 2024
Original language	English
Awarding Institution	University of Nottingham
Supervisor	Qingxin Meng (Supervisor) & Zhiang Zhang (Supervisor)