Sensor Fusion for Methane Emission Quantification

Methane emissions pose significant environmental and safety risks, necessitating accurate and reliable detection methods. This paper presents a novel approach to methane emission quantification through sensor fusion, integrating data from multiple gas leakage sensors and robotic platforms. By combining diverse sensor modalities, our method overcomes the limitations of traditional single-sensor systems, enhancing detection precision and robustness. We employ autonomous data collection using robotics, followed by advanced data processing through machine learning and computer vision techniques. Experimental validation involved testing various sensor configurations under controlled leak scenarios to identify the optimal fusion strategy. Comparative analysis against conventional methods demonstrated superior performance in both detection sensitivity and quantification accuracy, particularly at low emission rates. Our findings highlight the potential of sensor fusion to revolutionize methane monitoring by enabling earlier and more reliable leak detection. This integrated approach offers a scalable, field-deployable solution for emission management, contributing to safer operations and reduced environmental impact in the energy sector.