# A Machine Learning Approach for the Classification of Refrigerant Gases
## 1 Introduction  
The rapid progress of the Internet of Things (IoT) has resulted in an abundance of interconnected devices, generating vast amounts of data with substantial potential to enhance system and process performance [1]. Among the promising applications is mobile gas detection and identification [2], where integrating IoT with machine learning techniques can provide a more precise and dependable method for distinguishing various gaseous substances, including refrigerants. These refrigerants pose a significant environmental challenge as they contribute to the greenhouse effect, and their widespread use in Heating, Ventilation, Air Conditioning and Refrigeration (HVAC-R) systems demands strict regulatory measures. Therefore, achieving accurate detection and classification of these gases is of utmost importance for reasons related to safety, environmental preservation, and economic considerations. Within the realm of IoT, gas sensors can be strategically deployed in a networked setup, facilitating continuous monitoring and real-time analysis of the surrounding environment [3]. The data collected from these sensors can then undergo processing and analysis through machine learning algorithms, either locally on edge devices or remotely on cloud-based platforms, depending on the specific needs of the application [4]. Through the synergistic blend of IoT and machine learning [5], the development of robust gas detection systems becomes feasible. These systems possess the capability toautomatically identify and differentiate various gases, such as R32 and R134, with exceptional precision and minimal delay. Such a setup empowers these systems to provide crucial information for effective decision-making, ensuring the safe and efficient operation of HVAC-R systems while simultaneously reducing their environmental impact. Machine learning algorithms have emerged as a transformative tool for gas classification in the context of IoT. By harnessing the vast datasets obtained from interconnected gas sensors, these algorithms can learn intricate patterns and features indicative of various refrigerant gases. The application of machine learning techniques has been demonstrated to significantly improve the accuracy, efficiency, and real-time capabilities of gas classification systems [6]. With the ability to process data either locally on edge devices or remotely on cloud-based platforms, machine learning offers unparalleled flexibility to cater to diverse application requirements. Climate change, primarily driven by the burning of fossil fuels and greenhouse gas (GHG) emissions, results in rising average temperatures, melting ice masses, and extreme climate events globally. Since the 1900s, surface temperatures have increased by 1.65 \({}^{\circ}\)C, with an extreme rise of 0.18 \({}^{\circ}\)C reported in June 2023 [7]. Fluorinated greenhouse gases (FGGs), with their high global warming potential (GWP), are significant contributors to this issue [6]. Historically, chlorofluorocarbons (CFCs), used in packaging materials, aerosol solvents, and refrigerants, were recognized for their ozone-depleting properties, leading to their phase-out under the 1987 Montreal Protocol and subsequent replacement with hydrofluorocarbons (HFCs) [8; 9]. However, HFCs have GWPs ranging from 500 to 5000, prompting the 1997 Kyoto Protocol to address the six largest GHGs, including carbon dioxide, methane, and HFCs, aiming to reduce their emissions [10]. Environmental regulations have spurred the development of greener refrigerants, necessitating accurate detection of both new and older gases to ensure system efficacy and proper disposal. Different countries have varying laws regarding used refrigerants: some classify them as hazardous waste requiring immediate destruction, while others permit resale by licensed processors after recycling.