Enhancing Middle Stream of Fluorocarbon Gas Management in Malaysia through AI-Driven Solutions

Abstract

Fluorocarbon (FC) gases, widely used in refrigeration and air-conditioning (RAC) systems, pose
significant environmental risks due to their ozone depletion potential (ODP) and global warming
potential (GWP). In Malaysia, the midstream (usage) phase of FC gas management remains a
critical hotspot for emissions, primarily due to undetected leaks and inefficient maintenance
practices. Traditional reactive approaches to leak detection and maintenance are insufficient to
address these challenges, necessitating a shift toward proactive, AI-driven solutions. This study
explores the potential of digital technologies, including IoT-enabled sensors, predictive
maintenance algorithms, and blockchain-based inventory systems, to enhance FC gas management
in Malaysia. Using a mixed-methods approach, the research employs the DPSIR (Driving Forces,
Pressures, State, Impacts, and Responses) framework and SWOT (Strengths, Weaknesses,
Opportunities, Threats) analysis to identify key challenges and opportunities in the current FC gas
lifecycle. The findings reveal that AI-driven predictive maintenance and real-time leak detection
can significantly reduce emissions, improve energy efficiency, and extend the lifespan of
(refrigeration and air conditioning) RAC equipment. Furthermore, integrating blockchain
technology can enhance transparency and compliance in FC gas inventory management. The study
concludes that adopting these digital solutions, alongside structured training programs for
technicians, can transform Malaysia’s midstream FC gas management, aligning it with global
sustainability goals. This research contributes to the growing body of knowledge on AI-driven
environmental management and offers actionable insights for policymakers and industry
stakeholders.