This paper proposes a novel methodology for run-time fluid characterization through the application of machine learning techniques. It aims to integrate sophisticated multi-dimensional photonic sensors inside the chemical processes, following the Industry 4.0 paradigm. Currently, this analysis is done offline in laboratory environments, which increases the decision-making times. As an alternative, the proposed method tunes the spectral-based machine learning solutions to the requirements of each case enabling the integration of compound detection systems at the computing edge. It includes a novel feature selection strategy that combines filters and wrappers, namely Wavelength-based Hybrid Feature Selection, to select the relevant information of the spectrum (i.e., the relevant wavelengths). This technique allows providing different trade-offs involving the spectrum dimensionality, complexity, and detection quality. In terms of execution time, the provided solutions outperform the state-of-the-art up to 61.78 times using less than 99% of the wavelengths while maintaining the same detection accuracy. Also, these solutions were tested in a real-world edge platform, decreasing up to 68.57 times the energy consumption for an ethanol detection use case.

中文翻译：

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基于机器学习的光子传感器过程中流体表征方法

本文提出了一种通过应用机器学习技术来表征运行时流体的新方法。它旨在遵循工业 4.0 范式，将复杂的多维光子传感器集成到化学过程中。目前，这种分析是在实验室环境中离线完成的，这增加了决策时间。作为替代方案，所提出的方法根据每种情况的要求调整基于光谱的机器学习解决方案，从而能够在计算边缘集成复合检测系统。它包括一种新颖的特征选择策略，它结合了滤波器和包装器，即基于波长的混合特征选择，以选择光谱的相关信息（即相关波长）。该技术允许提供涉及频谱维度、复杂性和检测质量的不同权衡。在执行时间方面，所提供的解决方案在保持相同检测精度的情况下，使用不到 99% 的波长比最先进的解决方案性能高出 61.78 倍。此外，这些解决方案在现实世界的边缘平台上进行了测试，将乙醇检测用例的能耗降低了 68.57 倍。