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Multi-block SO-PLS approach based on infrared spectroscopy for anaerobic digestion process monitoring

Abstract : Near infrared spectroscopy combined with multivariate calibration such as partial least squares regression is a promising technique for on-line monitoring of anaerobic digesters. Different substrates are used in digesters, depending on their availability and their methanogen potential, to optimize the process. In Europe, the feedstock for anaerobic digesters is dominated by slurry and food waste which are respectively highly biodegradable and fat-containing substrates. The monitoring of the anaerobic digestion process based on digestates coming from these substrates presents some difficulties. The digestion of highly biodegradable substrates comes with the presence of water, which hinders spectroscopic calibration. And fat-containing substrates could lead to the accumulation of long chain fatty acids which are quite difficult to detect in the infrared region. While all existing studies have explored adapted spectroscopic measurements to improve the process monitoring, this study investigated the use of NIRS combined with multi-block analysis to track important anaerobic digestion stability parameters. Infrared measurements can come from several sources in the process monitoring. In addition, sequential and orthogonalized partial least squares have proven their ability of exploiting the underlying relation between several data blocks. These multi-block methods are powerful chemometric tools which can be applied in the monitoring of anaerobic digestion. Polarization light spectroscopy which is also known to improve the comprehension of scattering media like the digestate was also studied. © 2019 Elsevier B.V.
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Submitted on : Thursday, July 21, 2022 - 9:19:19 AM
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Lorraine Awhangbo, R. Bendoula, Jean-Michel J. M. Roger, Fabrice Béline. Multi-block SO-PLS approach based on infrared spectroscopy for anaerobic digestion process monitoring. Chemometrics and Intelligent Laboratory Systems, Elsevier, 2020, 196, pp.11. ⟨10.1016/j.chemolab.2019.103905⟩. ⟨hal-02610133⟩

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