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Ramaiyan, Kannan

Publications and source records attributed to Ramaiyan, Kannan.

5 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Recent Developments in Sensor Technologies for Enabling the Hydrogen Economy

Efforts to create a sustainable hydrogen economy are gaining momentum as governments all over the world are investing in hydrogen production, storage, distribution, and delivery technologies to develop a hydrogen infrastructure. This involves transporting hydrogen in gaseous or liquid form or using carrier gases such as methane, ammonia, or mixtures of methane and hydrogen. Hydrogen is a colorless, odorless gas and can easily leak into the atmosphere leading to economic loss and safety concerns. Therefore, deployment of robust low-cost sensors for various scenarios involving hydrogen is of paramount importance. Here, we review some recent developments in hydrogen sensors for applications such as leak detection, safety, process monitoring in production, transport and use scenarios. The status of methane and ammonia sensors is covered due to their important role in hydrogen production and transportation using existing natural gas and ammonia infrastructure. This review further provides an overview of existing commercial hydrogen sensors and also addresses the potential for hydrogen as an interferent gas for currently used sensors. This review can help developers and users make informed decisions about how to drive hydrogen sensor technology forward and to incorporate hydrogen sensors into the various hydrogen deployment projects in the coming decade.

08 HYDROGEN↗
DOE OSTI2023

Recent Developments in Sensor Technologies for Enabling the Hydrogen Economy

Efforts to create a sustainable hydrogen economy are gaining momentum as governments all over the world are investing in hydrogen production, storage, distribution, and delivery technologies to develop a hydrogen infrastructure. This involves transporting hydrogen in gaseous or liquid form or using carrier gases such as methane, ammonia, or mixtures of methane and hydrogen. Hydrogen is a colorless, odorless gas and can easily leak into the atmosphere leading to economic loss and safety concerns. Therefore, deployment of robust low-cost sensors for various scenarios involving hydrogen is of paramount importance. Here, we review some recent developments in hydrogen sensors for applications such as leak detection, safety, process monitoring in production, transport and use scenarios. The status of methane and ammonia sensors is covered due to their important role in hydrogen production and transportation using existing natural gas and ammonia infrastructure. This review further provides an overview of existing commercial hydrogen sensors and also addresses the potential for hydrogen as an interferent gas for currently used sensors. This review can help developers and users make informed decisions about how to drive hydrogen sensor technology forward and to incorporate hydrogen sensors into the various hydrogen deployment projects in the coming decade.

08 HYDROGEN↗
DOE OSTI2023

Colorimetric radiation detector

A low cost, rapid, flexible radiation detector uses inorganic metal halide precursors and dyes that respond to self-quenching hybrid scintillation. Remote, high-contrast, laser sensing can be used to determine when exposure of the detector to radiation occurs (even temporally).

Garzon, Fernando Henry↗
DOE OSTI2023

Comparison of Machine Learning Algorithms for Natural Gas Identification with Mixed Potential Electrochemical Sensor Arrays

Mixed-potential electrochemical sensor arrays consisting of indium tin oxide (ITO), La 0.87 Sr 0.13 CrO 3 , Au, and Pt electrodes can detect the leaks from natural gas infrastructure. Algorithms are needed to correctly identify natural gas sources from background natural and anthropogenic sources such as wetlands or agriculture. We report for the first time a comparison of several machine learning methods for mixture identification in the context of natural gas emissions monitoring by mixed potential sensor arrays. Random Forest, Artificial Neural Network, and Nearest Neighbor methods successfully classified air mixtures containing only CH 4 , two types of natural gas simulants, and CH 4 +NH 3 with >98% identification accuracy. The model complexity of these methods were optimized and the degree of robustness against overfitting was determined. Finally, these methods are benchmarked on both desktop PC and single-board computer hardware to simulate their application in a portable internet-of-things sensor package. The combined results show that the random forest method is the preferred method for mixture identification with its high accuracy (>98%), robustness against overfitting with increasing model complexity, and had less than 10 ms training time and less than 0.1 ms inference time on single-board computer hardware.

03 NATURAL GAS↗