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

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.

Programmable and Modularized Gas Sensor Integrated by 3D Printing

“…Specifically, Mirzaei et al developed a microfluidic-based metal oxide semiconductor (MOS) gas sensor comprising a 3Dprinted microfluidic detector integrated with two commercial MOS sensors. 233 The gas detector featured a microchannel with coated walls, allowing for selective gas detection based on the differential diffusion rates of various gases. This framework, leveraging time-series data from the microfluidic-based detector, demonstrated superior performance compared to conventional machine learning models.…”

“…Another study underscores the pivotal role of software in enhancing the accuracy of gas concentration identification. Specifically, Mirzaei et al developed a microfluidic-based metal oxide semiconductor (MOS) gas sensor comprising a 3D-printed microfluidic detector integrated with two commercial MOS sensors . The gas detector featured a microchannel with coated walls, allowing for selective gas detection based on the differential diffusion rates of various gases.…”

Energy efficiency optimization and carbon emission reduction targets of resource-based cities based on BiLSTM-CNN-GAN model

“…Autoencoder is a type of neural network model mainly used to learn high-level feature representation and data compression (Mirzaei et al, 2022). In energy efficiency optimization and carbon emission reduction goals in resource-based cities, the autoencoder method can be applied to building energy consumption prediction, energy management strategy formulation, and energy consumption monitoring, thereby reducing energy consumption and carbon emissions.…”

Drift‐Aware Feature Learning Based on Autoencoder Preprocessing for Soft Sensors

“…[18] Although some researchers have attempted to mitigate sensor baseline drifting through the use of machine learning approaches, the investigation and validation of solutions specifically tailored for long-term sensor usage drift (over one hour) remain unexplored. Drawing inspiration from the successful application of unsupervised learning methods, such as autoencoder (AE), [19][20][21] in detecting sensor faults, there have been notable instances where unsupervised learning methods have been employed to address the drift detection problem. For example, Kim et al utilized a variational AE model to detect semiconductor faults characterized by time-varying process drift.…”

“…[ 20 ] Moreover, Mirzaei et al utilized sparse AE‐based transfer learning to estimate hydrogen gas concentration and address the data distribution shift problem arising from instrumental variation. [ 21 ] However, it is worth noting that these methods primarily focus on generating signals for normal conditions to detect sensor drift. Conversely, the extraction of drift pattern features through the representation learning of AEs becomes crucial for learning the regression relationship with drift correction.…”