School of Engineering, University of British Columbia Okanagan Campus, Kelowna, BC V1V 1V7, Canada.
Department of Mechanical Engineering, University of Victoria, Victoria, BC V8P 5C2, Canada.
Sensors (Basel). 2022 Oct 11;22(20):7696. doi: 10.3390/s22207696.
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.
替代燃料来源，如富氢天然气（HENG），受到全球各国政府的高度追捧，以降低碳排放。因此，氢气作为一种有价值的零排放能源载体的认可度不断提高，导致许多国家试图用氢气来丰富天然气；然而，由于氢气的易燃性、燃烧性和在低浓度（4 体积%）下的爆炸性等特性，人们越来越关注 H2 的安全使用、储存和运输，这需要高度敏感和选择性的传感器进行安全监测。基于微流控的金属氧化物半导体（MOS）气体传感器是检测较低水平的天然气元素的有力工具；然而，它们的工作机制导致缺乏实时分析技术来识别当前气体的精确浓度。目前的先进机器学习模型，如深度学习，需要大量的数据集进行训练。此外，这些模型在数据分布偏移（如仪器变化）方面表现不佳。为了解决这个问题，我们提出了一种基于稀疏自编码器的迁移学习（SAE-TL）框架，用于使用来自与两个商业 MOS 传感器耦合的 3D 打印微流控探测器的有限数据集来估计 HENG 混合物中的氢气浓度。我们的框架基于从经济高效的基于微流控的探测器收集的时间序列数据来检测模拟 HENG 的浓度。这个模块化的气体探测器在带有涂层壁的微通道中放置金属氧化物半导体（MOS）气体传感器，根据不同气体的扩散速度提供选择性。与典型的 ML 模型相比（94%R-squared），我们的 SAE-TL 框架实现了卓越的性能。该框架可在实际应用中实现，以快速适应新类型的 MOS 传感器响应的预测模型。
