Augmented intelligence for industrial chillers

• Main Author: Khoo, Alvin Seng Gee
• Other Authors: Meng-Hiot Lim
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2024
• Subjects: Engineering
• Online Access: https://hdl.handle.net/10356/176517
• Institution: Nanyang Technological University

Air conditioning is commonly used in manufacturing, commercial, and industrial domains. It is common for larger buildings to have heating, ventilation, and air conditioning (HVAC) systems installed. Chillers are a key component of the HVAC system that provides cooling to the building. Innovations in augmented intelligence have a lot of potential for improving the reliability of chiller systems. Algorithms and machine learning techniques can be used to achieve better control and performance monitoring. The maintenance crew can then anticipate and prevent technical problems by distinguishing genuine faults from false alarms. Predictive analysis based on historical data has the potential to improve energy efficiency, reduce maintenance costs, and increase reliability. The goal of this project is to develop a predictive prognostic model that can predict the occurrence of refrigeration malfunctions. The applications and advantages of augmented intelligence in industrial chillers will be discussed in the subsequent sections.