Dr. Patrick O'Driscoll is a Machine Learning Algorithm Development Engineer for Rebellion Photonics Houston, Texas. His research background is in machine learning methods for pattern recognition in large, complex, functional and temporal datasets. He currently research and develops real-time gas, fire, and intrusion detection methods for Rebellion Photonic's Gas Cloud Imaging safety system. He holds a B.S. in Chemical and Biomolecular Engineering, and Applied Mathematics and Statistics from Johns Hopkins University, USA, and an M.S. & Ph.D. in Applied Physics from Rice University, USA.
With increasing in regulatory requirement for fugitive methane emissions, cost-effective long-term real-time methane detection and quantification methodologies are desired. Among the various existing methodologies for methane detection, passive hyperspectral camera based detection is favored due to the flexibility in monitoring and the overall cost-effectiveness for long-term monitoring. Rebellion Photonics have delivered the first real-time passive hyperspectral camera product that can both detect and quantify fugitive methane in real-time. Initially, based on a pure physics based approach, the Gas Cloud Imaging system (GCI) system have been demonstrated to be capable of monitoring methane pipelines and other industrial facilities 24/7. In this presentation we present the latest development of methane detection algorithms at Rebellion Photonics, including our latest Physics Enhanced AI Real-time Logic (PEARL) gas analytics product.