Session: 01-03 Advanced Analytics for Condition Monitoring and Asset Reliability

Paper Number: 170575

170575 - Enhancing Methane Monitoring With Soofie® Sensors: Predictive Rul Modeling and Real-Time Calibration of Mos Sensors 

Metal Oxide Semiconductor (MOS) sensors are a widely adopted technology for methane detection due to their exceptional sensitivity and cost-effectiveness. Properly utilizing the MOS sensors for methane detection application is a challenging task. Their performance is influenced by manufacturing variabilities, age-based drift and environmental factors such as temperature, pressure, and humidity, necessitating continuous synchronization and calibration to maintain accuracy. This study introduces a hybrid approach leveraging advanced model-based analytics, data-driven correction models, and AI techniques to predict the Remaining Useful Life (RUL) of MOS sensors deployed globally within the SOOFIE® sensor system.
This approach, developed by the Emissions Technologies R&D group at ChampionX, is part of the ongoing efforts to monitor sensor performance and improve lifecycle predictability. The presentation highlights the development of a Digital Automated MOS Synchronization Algorithm that dynamically calibrates sensors, enabling unified performance across the network. Complementing this, a robust Methane Concentration Model minimizes environmental noise, translating MOS outputs into precise methane measurements. The integration of predictive RUL models will enhance the reliability and longevity of sensor networks while identifying anomalies associated with impact on sensor life, in the form of a Continuous Performance Monitoring (CPM) system. This data-driven and AI-enhanced methodology not only standardizes MOS sensor performance but is also designed to empower the operators to mitigate methane emissions efficiently. By extending sensor lifecycle predictability, the SOOFIE® sensors can enable lean management practices, promoting efficiency and cost-effectiveness. Their improved monitoring accuracy highlights a commitment to integrating technological advancements with the broader goal of fostering environmental sustainability.
This research proposes groundbreaking solutions for industrial and environmental applications, advancing the field of methane monitoring and emission management.

Presenting Author: Sagar Gaur ChampionX

Presenting Author Biography: Dr. Sagar Gaur specializes in Multi-Physics modeling, Data-driven modeling, Adaptive modeling, Simulation-based design, and Mathematics. His work focuses on developing models for cloud-based, real-time Condition Monitoring, Diagnostics & Prognostics, Remaining Useful Life assessment, and Calibration.