Gas detection of hydrogen/natural gas blends in the gas industry

Hall, J.; Hooker, Philip; Jeffrey, Kate

doi:10.1016/j.ijhydene.2020.08.200

Cited by 31 publications

(9 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hydrogen is a light and odorless gas; it is challenging to determine the concentration in the mixture of HENG in real time using commercial sensors [ 35 ]. The use of a microfluidic channel for the detection of gases based on adsorption/desorption phenomena provides the apparatus with enough delay in the diffusion time and, consequently, in the response time to differentiate between various gases [ 15 , 28 , 36 ].…”

Section: Experimental Verificationmentioning

confidence: 99%

Investigation of a Sparse Autoencoder-Based Feature Transfer Learning Framework for Hydrogen Monitoring Using Microfluidic Olfaction Detectors

Mirzaei

Ramezankhani

Earl

et al. 2022

Sensors

View full text Add to dashboard Cite

Alternative fuel sources, such as hydrogen-enriched natural gas (HENG), are highly sought after by governments globally for lowering carbon emissions. Consequently, the recognition of hydrogen as a valuable zero-emission energy carrier has increased, resulting in many countries attempting to enrich natural gas with hydrogen; however, there are rising concerns over the safe use, storage, and transport of H2 due to its characteristics such as flammability, combustion, and explosivity at low concentrations (4 vol%), requiring highly sensitive and selective sensors for safety monitoring. Microfluidic-based metal–oxide–semiconducting (MOS) gas sensors are strong tools for detecting lower levels of natural gas elements; however, their working mechanism results in a lack of real-time analysis techniques to identify the exact concentration of the present gases. Current advanced machine learning models, such as deep learning, require large datasets for training. Moreover, such models perform poorly in data distribution shifts such as instrumental variation. To address this problem, we proposed a Sparse Autoencoder-based Transfer Learning (SAE-TL) framework for estimating the hydrogen gas concentration in HENG mixtures using limited datasets from a 3D printed microfluidic detector coupled with two commercial MOS sensors. Our framework detects concentrations of simulated HENG based on time-series data collected from a cost-effective microfluidic-based detector. This modular gas detector houses metal–oxide–semiconducting (MOS) gas sensors in a microchannel with coated walls, which provides selectivity based on the diffusion pace of different gases. We achieve a dominant performance with the SAE-TL framework compared to typical ML models (94% R-squared). The framework is implementable in real-world applications for fast adaptation of the predictive models to new types of MOS sensor responses.

show abstract

Section: Experimental Verificationmentioning

confidence: 99%

Investigation of a Sparse Autoencoder-Based Feature Transfer Learning Framework for Hydrogen Monitoring Using Microfluidic Olfaction Detectors

Mirzaei

Ramezankhani

Earl

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…7 However, some studies have considered higher ranges. [8][9][10] Instead, methane resembles the main constituent of fossil natural gas. Methane can be injected into the natural gas grid infrastructure, which is already in place in many communities, for storage, distribution, and consumption purposes.…”

Section: Co (G) + 4h (G) → Ch (G) + 2h O (L)mentioning

confidence: 99%

An integrated systems-biology platform for power-to-gas technology

Casini

McCubbin

Esquivel-Elizondo

et al. 2022

Preprint

View full text Add to dashboard Cite

Methanogenesis allows methanogenic archaea (methanogens) to generate cellular energy for their growth while producing methane. Hydrogenotrophic methanogens thrive on carbon dioxide and molecular hydrogen as sole carbon and energy sources. Thermophilic and hydrogenotrophic Methanothermobacter spp. have been recognized as robust biocatalysts for a circular carbon economy and are now applied in power-to-gas technology. Here, we generated the first manually curated genome-scale metabolic reconstruction for three Methanothermobacter spp.. We investigated differences in the growth performance of three wild-type strains and one genetically engineered strain in two independent chemostat bioreactor experiments. In the first experiment, with molecular hydrogen and carbon dioxide, we found the highest methane production rate for Methanothermobacter thermautotrophicus delta H, while Methanothermobacter marburgensis Marburg reached the highest biomass growth rate. Systems biology investigations, including implementing a pan-model that contains combined reactions from all three microbes, allowed us to perform an interspecies comparison. This comparison enabled us to identify crucial differences in formate anabolism. In the second experiment, with sodium formate, we found stable growth with an M. thermautotrophicus delta H plasmid-carrying strain with similar performance parameters compared to wild-type Methanothermobacter thermautotrophicus Z-245. Our findings reveal that formate anabolism influences the diversion of carbon to biomass and methane with implications for biotechnological applications of Methanothermobacter spp. in power-to-gas technology and for chemical production.

show abstract

“… 1 , 2 Legal regulatory frameworks typically limit the injection of H 2 into the established natural gas grid to under 10% v/v due to the different physical properties of H 2 and natural gas, which result in limited infrastructure and material compatibility. 1 , 3 , 4 Instead, methane (CH 4 ), the main constituent of natural gas, can be injected into the existing natural gas grid infrastructure to replace fossil natural gas without limitations for storage, distribution, and consumption. 5 , 6 …”

Section: Introductionmentioning

confidence: 99%

An integrated systems biology approach reveals differences in formate metabolism in the genus Methanothermobacter

Casini,

McCubbin,

Esquivel-Elizondo

et al. 2023

iScience

View full text Add to dashboard Cite

Gas detection of hydrogen/natural gas blends in the gas industry

Cited by 31 publications

References 3 publications

Investigation of a Sparse Autoencoder-Based Feature Transfer Learning Framework for Hydrogen Monitoring Using Microfluidic Olfaction Detectors

Investigation of a Sparse Autoencoder-Based Feature Transfer Learning Framework for Hydrogen Monitoring Using Microfluidic Olfaction Detectors

An integrated systems-biology platform for power-to-gas technology

An integrated systems biology approach reveals differences in formate metabolism in the genus Methanothermobacter

Contact Info

Product

Resources

About