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Journal Articles Chemometrics and Intelligent Laboratory Systems Year : 2020

Multi-block data analysis for online monitoring of anaerobic co-digestion process

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Abstract

Anaerobic digestion is a chemical process whose purpose is to maximize biogas production whilst concomitantly treating organic waste mostly through co-digestion due to the variety of substrates. To avoid failures, the process requires the monitoring of several parameters and / or inhibitors. The existing strategies and methods used in the process monitoring still lack sensitivity and robustness, when taken individually. The current study investigated the use of sequential and orthogonalized partial least squares (SO-PLS) regression to relate these parameters to several blocks of data coming for near infrared spectroscopy, chemical routine analysis and kinetics of biogas production. The models produced were able to extract relevant information from each block's data and discard redundancies. Moreover, to meet biogas plant operators' requirements, variable selection was performed on the infrared blocks using a recent method: SO-CovSel. SO-CovSel is a method resulting from coupling SO-PLS and Covariance Selection (CovSel) method. The method has been demonstrated to be suitable for multi-response calibration purposes with infr ared calibration. It has provided good predictions and an interesting interpretation of wavelengths involved in the monitoring of the relevant parameters of stability in anaerobic co-digestion.
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Dates and versions

hal-02959945 , version 1 (22-08-2022)

Licence

Attribution - NonCommercial - CC BY 4.0

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Lorraine-Fifame Awhangbo, R. Bendoula, Jean-Michel Roger, Fabrice Béline. Multi-block data analysis for online monitoring of anaerobic co-digestion process. Chemometrics and Intelligent Laboratory Systems, 2020, 205, ⟨10.1016/j.chemolab.2020.104120⟩. ⟨hal-02959945⟩
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