Investigation of a sparse autoencoder-based feature transfer learning framework for hydrogen monitoring using microfluidic olfaction detectors
Date
2022
Authors
Mirzaei, Hamed
Ramezankhani, Milad
Earl, Emily
Tasnim, Nishat
Milani, Abbas S.
Hoorfar, Mina
Journal Title
Journal ISSN
Volume Title
Publisher
Sensors
Abstract
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.
Description
Keywords
hydrogen detection, HENG, transfer learning, sparse autoencoder, microfluidic gas sensor
Citation
Mirzaei, H., Ramezankhani, M., Earl, E., Tasnim, N., Milani, A. S., & Hoorfar, M. (2022). “Investigation of a sparse autoencoder-based feature transfer learning framework for hydrogen monitoring using microfluidic olfaction detectors.” Sensors, 22(20), 7696. https://doi.org/10.3390/s22207696