A Machine Learning-Based Methodology for in-Process Fluid Characterization With Photonic Sensors

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.