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1. Modelling hydrolysis: Simultaneous versus sequential biodegradation of the hydrolysable fractions

Author

Julie Jimenez, Damien J. Batstone, Ángel Robles, Aurora Seco, Fabrice Béline, Cyrille Charnier, Peter A. Vanrolleghem, Mathieu Spérandio, Eberhard Morgenroth, Eric Latrille, Jérôme Harmand, Dominique Patureau, George Ekama, Mokhles Kouas, Jean-Philippe Steyer, Michel Torrijos, Laboratoire de Biotechnologie de l'Environnement [Narbonne] (LBE), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), BioEnTech, Symbiose : Ecosystèmes microbiens et bioprocédés d’épuration et de valorisation (TBI-SYMBIOSE), Toulouse Biotechnology Institute (TBI), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Optimisation des procédés en Agriculture, Agroalimentaire et Environnement (UR OPAALE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), University of Cape Town, Université Laval [Québec] (ULaval), Universitat Politècnica de València (UPV), Universitat de València (UV), University of Queensland [Brisbane], Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut National de la Recherche Agronomique (INRA), EAWAG SWISS FEDERAL INSTITUTE OF AQUATIC SCIENCE AND TECHNOLOGY DUBENDORF SWE, Partenaires IRSTEA, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), ETH ZURICH INSTITUTE OF ENVIRONMENTAL ENGINEERING ZURICH SWE, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), UNIVERSITY OF CAPE TOWN ZAF, DEPARTAMENT D'ENGINYERIA QUIMICA UNIVERSITAT DE VALENCIA BURJASSOT VALENCIA ESP, and ADVANCED WATER MANAGEMENT CENTRE THE UNIVERSITY OF QUEENSLAND AUS

Subjects
[SDV.BIO]Life Sciences [q-bio]/Biotechnology, Biosolids, SEQUENTIAL EXTRACTION, ANAEROBIC DIGESTION, BIODEGRADATION, 02 engineering and technology, 010501 environmental sciences, TRITICUM AESTIVUM, 01 natural sciences, 7. Clean energy, NUMERICAL MODEL, SLUDGE DIGESTION, Bioreactors, METHANE, BIOLOGICAL MATERIALS, ACTIVATED SLUDGE, 0202 electrical engineering, electronic engineering, information engineering, Anaerobiosis, Sequential model, PRIORITY JOURNAL, Waste Management and Disposal, ComputingMilieux_MISCELLANEOUS, CALIBRATION, Sewage, CONCENTRATION (PARAMETER), Chemistry, FRACTIONATION, ACID HYDROLYSIS, INCUBATION TIME, MODELLING, HYDROLYSIS, CHEMICAL FRACTIONATION, SEQUENTIAL DEGRADATION, Biodegradation, Environmental, WASTE TREATMENT, ORGANIC MATTER, [SDE]Environmental Sciences, ANAEROBIC DIGESTION MODEL, ADM1, SOLID WASTE, 020209 energy, MODELS, Fractionation, CAPACITY, Hydrolysis, DIGESTION, ISOTOPIC FRACTIONATION, NONHUMAN, CHEMICAL OXYGEN DEMAND, ARTICLE, MODEL SELECTION, 0105 earth and related environmental sciences, Chromatography, Models, Theoretical, SUBSTRATES, Biodegradation, SIMULTANEOUS DEGRADATION, HOMOGENEOUS MATERIALS, Anaerobic digestion, WASTE WATER MANAGEMENT, Activated sludge, APPLE, Degradation (geology)
Abstract

Hydrolysis is considered the limiting step during solid waste anaerobic digestion (including co-digestion of sludge and biosolids). Mechanisms of hydrolysis are mechanistically not well understood with detrimental impact on model predictive capability. The common approach to multiple substrates is to consider simultaneous degradation of the substrates. This may not have the capacity to separate the different kinetics. Sequential degradation of substrates is theoretically supported by microbial capacity and the composite nature of substrates (bioaccessibility concept). However, this has not been experimentally assessed. Sequential chemical fractionation has been successfully used to define inputs for an anaerobic digestion model. In this paper, sequential extractions of organic substrates were evaluated in order to compare both models. By removing each fraction (from the most accessible to the least accessible fraction) from three different substrates, anaerobic incubation tests showed that for physically structured substrates, such as activated sludge and wheat straw, sequential approach could better describe experimental results, while this was less important for homogeneous materials such as pulped fruit. Following this, anaerobic incubation tests were performed on five substrates. Cumulative methane production was modelled by the simultaneous and sequential approaches. Results showed that the sequential model could fit the experimental data for all the substrates whereas simultaneous model did not work for some substrates. © 2019

Published
2020

2. Relating Near-Infrared Light Path-Length Modifications to the Water Content of Scattering Media in Near-Infrared Spectroscopy: Toward a New Bouguer–Beer–Lambert Law

Author

Roumiana Tsenkova, Jelena Muncan, Cyrille Charnier, Jean-Michel Roger, Alexandre Mallet, Eric Latrille, Ryad Bendoula, Jean-Philippe Steyer, Laboratoire de Biotechnologie de l'Environnement [Narbonne] (LBE), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), BioEnTech, Kobe University, Information – Technologies – Analyse Environnementale – Procédés Agricoles (UMR ITAP), Agreenium EIR-A program at the Kobe University, National Research Institute for Agriculture, Food and Environment (INRAE), and French Agency of National Research and Technology (ANRT) [grant number 2018/0461]

Subjects
Chemistry, Scattering, 010401 analytical chemistry, Near-infrared spectroscopy, Beer–Lambert law, 010402 general chemistry, 01 natural sciences, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, 0104 chemical sciences, Analytical Chemistry, Computational physics, Chemometrics, Absorbance, symbols.namesake, Path length, symbols, [SPI.GPROC]Engineering Sciences [physics]/Chemical and Process Engineering, Spectroscopy, Water content
Abstract

International audience; In near-infrared spectroscopy (NIRS), the linear relationship between absorbance and an absorbing compound concentration has been strictly defined by the Bouguer-Beer-Lambert law only for the case of transmission measurements of nonscattering media. However, various quantitative calibrations have been successfully built both on reflectance measurements and for scattering media. Although the lack of linearity for scattering media has been observed experimentally, the sound multivariate statistics and signal processing involved in chemometrics have allowed us to overcome this problem in most cases. However, in the case of samples with varying water content, important modifications of scattering levels still make calibrations difficult to build due to nonlinearities. Moreover, even when calibration procedures are successfully developed, many preprocessing methods used do not guarantee correct spectroscopic assignments (in the sense of a pure chemical absorbance). In particular, this may prevent correct modeling and interpretation of the structure of water. In this study, dynamic near-infrared spectra acquired during a drying process allow the study of the physical effects of water content variations, with a focus on the first overtone OH absorbance region. A model sample consisting of aluminum pellets mixed with water allowed us to study this specifically, without any other absorbing interaction terms related to the dry mass-absorbing constituents. A new formulation of the Bouguer-Beer-Lambert law is proposed, by expressing path length as a power function of water content. Through this new formulation, it is shown that a better and simpler prediction model of water content may be developed, with more precise and accurate identification of water absorbance bands.

Published
2021

3. On-site substrate characterization in the anaerobic digestion context: A dataset of near infrared spectra acquired with four different optical systems on freeze-dried and ground organic waste

Author

Pérémé, Margaud, Mallet, Alexandre, Awhangbo, Lorraine, Charnier, Cyrille, Roger, Jean-Michel, Steyer, Jean-Philippe, Latrille, Éric, Bendoula, Ryad, Laboratoire de Biotechnologie de l'Environnement [Narbonne] (LBE), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Ecole Nationale Supérieure de Chimie de Montpellier (ENSCM), Information – Technologies – Analyse Environnementale – Procédés Agricoles (UMR ITAP), BioEnTech, French Agency of National Research and Technology (ANRT) [grant number 2018/0461, and National Research Institute for Agriculture, Food and Environment (INRAE)

Subjects
Q1-390, Compact spectrometers, Science (General), Computer applications to medicine. Medical informatics, Instrument standardization, R858-859.7, [CHIM]Chemical Sciences, [INFO]Computer Science [cs], Chemometrics, Near infrared spectroscopy, Organic waste, Data Article
Abstract

International audience; The near infrared spectra of thirty-three freeze-dried and ground organic waste samples of various biochemical composition were collected on four different optical systems, including a laboratory spectrometer, a transportable spectrometer with two measurement configurations (an immersed probe, and a polarized light system) and a micro-spectrometer. The provided data contains one file per spectroscopic system including the reflectance or absorbance spectra with the corresponding sample name and wavelengths. A reference data file containing carbohydrates, lipid and nitrogen content, biochemical methane potential (BMP) and chemical oxygen demand (COD) for each sample is also provided. This data enables the comparison of the optical systems for predictive model calibration based for example on Partial Least Squares Regression (PLS-R) [1], but could be used more broadly to test new chemometrics methods. For example, the data could be used to evaluate different transfer functions between spectroscopic systems [2]. This dataset enabled the research work reported by Mallet et al. 2021 [3].

Published
2021

4. Fast at-line characterization of solid organic waste: Comparing analytical performance of different compact near infrared spectroscopic systems with different measurement configurations

Author

Jean-Michel Roger, Margaud Pérémé, Cyrille Charnier, Alexandre Mallet, Ryad Bendoula, Lorraine Awhangbo, Eric Latrille, Jean-Philippe Steyer, Laboratoire de Biotechnologie de l'Environnement [Narbonne] (LBE), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Information – Technologies – Analyse Environnementale – Procédés Agricoles (UMR ITAP), BioEnTech, National Research Institute for Agriculture, Food and Environment (INRAE), Biogaz-RIO platform [FEDER-FSE Languedoc Roussillon 2014-2020], and French Agency of National Research and Technology (ANRT) [grant number 2018/0461]

Subjects
Materials science, Process monitorin, 020209 energy, 02 engineering and technology, 010501 environmental sciences, Solid Waste, 01 natural sciences, 7. Clean energy, g Biochemical methane potential, symbols.namesake, Biogas, Sampling (signal processing), Anaerobic digestion, 0202 electrical engineering, electronic engineering, information engineering, [CHIM]Chemical Sciences, Spectral resolution, Process engineering, Waste Management and Disposal, 0105 earth and related environmental sciences, Biological Oxygen Demand Analysis, [PHYS.PHYS.PHYS-OPTICS]Physics [physics]/Physics [physics]/Optics [physics.optics], Spectroscopy, Near-Infrared, Spectrometer, business.industry, Near-infrared spectroscopy, Biodegradable waste, Characterization (materials science), Compact systems, Fourier transform, Biofuels, symbols, Measurement modes, business, Methane, Near infrared spectroscopy
Abstract

International audience; Fast characterization of solid organic waste using near infrared spectroscopy has been successfully developed in the last decade. However, its adoption in biogas plants for monitoring the feeding substrates remains limited due to the lack of applicability and high costs. Recent evolutions in the technology have given rise to both more compact and more modular low-cost near infrared systems which could allow a larger scale deployment. The current study investigates the relevance of these new systems by evaluating four different Fourier transform near-infrared spectroscopic systems with different compactness (laboratory, portable, micro spectrometer) but also different measurement configurations (polarized light, at distance, in contact). Though the conventional laboratory spectrometer showed the best performance on the various biochemical parameters tested (carbohydrates, lipids, nitrogen, chemical oxygen demand, biochemical methane potential), the compact systems provided very close results. Prediction of the biochemical methane potential was possible using a low-cost micro spectrometer with an independent validation set error of only 91 NmL(CH4).gTS-1 compared to 60 NmL(CH4).gTS-1 for a laboratory spectrometer. The differences in performance were shown to result mainly from poorer spectral sampling; and not from instrument characteristics such as spectral resolution. Regarding the measurement configurations, none of the evaluated systems allowed a significant gain in robustness. In particular, the polarized light system provided better results when using its multi-scattered signal which brings further evidence of the importance of physical light-scattering properties in the success of models built on solid organic waste.

Published
2021

5. The effects of water on scattering: taking into account path-length modifications

Author

Alexandre Mallet, Roumiana Tsenkova, Jelena Muncan, Cyrille Charnier, Eric Latrille, Ryad Bendoula, Jean-Philippe Steyer, Jean Michel Roger, Laboratoire de Biotechnologie de l'Environnement [Narbonne] (LBE), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Information – Technologies – Analyse Environnementale – Procédés Agricoles (UMR ITAP), BioEnTech, Kobe University, and Mallet, Alexandre

Subjects
[STAT]Statistics [stat], [CHIM.MATE] Chemical Sciences/Material chemistry, [CHIM.ANAL] Chemical Sciences/Analytical chemistry, [SPI.OPTI] Engineering Sciences [physics]/Optics / Photonic, [CHIM.ANAL]Chemical Sciences/Analytical chemistry, [SPI.OPTI]Engineering Sciences [physics]/Optics / Photonic, [CHIM.MATE]Chemical Sciences/Material chemistry, ComputingMilieux_MISCELLANEOUS, [STAT] Statistics [stat]
Abstract

International audience

Published
2021

6. Unveiling non-linear water effects in near infrared spectroscopy: A study on organic wastes during drying using chemometrics

Author

Ryad Bendoula, Cyrille Charnier, Jean-Michel Roger, Alexandre Mallet, Eric Latrille, Jean-Philippe Steyer, Laboratoire de Biotechnologie de l'Environnement [Narbonne] (LBE), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Information – Technologies – Analyse Environnementale – Procédés Agricoles (UMR ITAP), and BioEnTech

Subjects
Organic wastes, Materials science, 020209 energy, Analytical chemistry, Context (language use), 02 engineering and technology, 010501 environmental sciences, 01 natural sciences, Water effects, Chemometrics, 0202 electrical engineering, electronic engineering, information engineering, Calibration, Desiccation, Robustness, Waste Management and Disposal, Water content, 0105 earth and related environmental sciences, Drying, Spectroscopy, Near-Infrared, Near-infrared spectroscopy, Water, Biodegradable waste, 6. Clean water, Characterization (materials science), Freeze Drying, 13. Climate action, Principal component analysis, [SPI.OPTI]Engineering Sciences [physics]/Optics / Photonic, Near infrared spectroscopy, [CHIM.CHEM]Chemical Sciences/Cheminformatics
Abstract

International audience; In the context of organic waste management, near infrared spectroscopy (NIRS) is being used to offer a fast, non-destructive, and cost-effective characterization system. However, cumbersome freeze-drying steps of the samples are required to avoid water’s interference on near infrared spectra. In order to better understand these effects, spectral variations induced by dry matter content variations were obtained for a wide variety of organic substrates. This was made possible by the development of a customized near infrared acquisition system with dynamic highly-resolved simultaneous scanning of near infrared spectra and estimation of dry matter content during a drying process at ambient temperature. Using principal components analysis, the complex water effects on near infrared spectra are detailed. Water effects are shown to be a combination of both physical and chemical effects, and depend on both the characteristics of the samples (biochemical type and physical structure) and the moisture content level. This results in a non-linear relationship between the measured signal and the analytical characteristic of interest. A typology of substrates with respect to these water effects is provided and could further be efficiently used as a basis for the development of local quantitative calibration models and correction methods accounting for these water effects.

Published
2021

7. Histology of Tritia mutabilis gonads: using reproductive biology to support sustainable fishery management

Author

Benjamin Geffroy, Morgane Broyon, Jean-Yves Jouvenel, Nelly Pirot, Alicia Mallet, BioEnTech, Observatoire océanologique de Banyuls (OOB), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), BioCampus (BCM), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche en Cancérologie de Montpellier (IRCM - U1194 Inserm - UM), CRLCC Val d'Aurelle - Paul Lamarque-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM), MARine Biodiversity Exploitation and Conservation (UMR MARBEC), Institut de Recherche pour le Développement (IRD)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), BioCampus Montpellier (BCM), Université Montpellier 1 (UM1)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Sciences, EDP, and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Institut de Recherche pour le Développement (IRD)

Subjects
0106 biological sciences, [SDV]Life Sciences [q-bio], Aquatic Science, Biology, 01 natural sciences, Sex change, Mediterranean sea, Tritia mutabilis, Reproductive biology, 14. Life underwater, Baseline (configuration management), size at first maturity, 010604 marine biology & hydrobiology, Population size, Sustainable fishery, 04 agricultural and veterinary sciences, biology.organism_classification, [SDV] Life Sciences [q-bio], Fishery, fisheries management, 040102 fisheries, gonochoric species, 0401 agriculture, forestry, and fisheries, Fisheries management, gonad histology
Abstract

The mutable nassa, Tritia mutabilis, a marine gastropod that is widely exploited on the Adriatic coast is an important source of income for small-scale fishermen in the Mediterranean Sea, particularly in the Gulf of Lion. However, the lack of knowledge on the ecology and biology of this species limits our capacities to propose and produce an effective management plan. As a result, stocks are currently declining, especially in Italy. In order to optimize a management plan for this fishery, we designed a study to better characterize the reproductive biology of T. mutabilis, using gonad histology and performing a regular monitoring of population size frequency. The average shell height of individuals during the breeding period was 24 ± 2.7 mm for males and 30 ± 3.7 mm for females. The presence of small females (10 mm) and large males (32 mm) in the whole sample challenged previous assumptions regarding protandry (sex change from male to female). The size at first maturity was estimated for males and females at 17.5 mm and 24.4 mm shell height, respectively. In Italy, current management measures include a minimum conservation reference size of 20 mm in shell height. Therefore, it is likely that many individuals that did not reproduce are being caught, which could partly explain the decline observed, despite conservation measures introduced more than ten years ago. Overall, our study provides some baseline information to establish, in consultation with fishermen, management measures for this small-scale fishery in France.

Published
2021

8. Near-Infrared Spectrum Analysis to Determine Relationships between Biochemical Composition and Anaerobic Digestion Performances

Author

Cyrille Charnier, Eric Latrille, Jérémie Miroux, Jean-Michel Roger, Jean-Philippe Steyer, BioEnTech, Laboratoire de Biotechnologie de l'Environnement [Narbonne] (LBE), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Information – Technologies – Analyse Environnementale – Procédés Agricoles (UMR ITAP), and Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)

Subjects
DIGESTION PERFORMANCE, INFRARED DEVICES, Chemical substance, NEAR INFRARED SPECTRUM ANALYSIS, 020209 energy, General Chemical Engineering, chemistry.chemical_element, 02 engineering and technology, 010501 environmental sciences, ORGANIC COMPOUNDS, BIOCHEMICAL COMPOSITION, 01 natural sciences, 7. Clean energy, Oxygen, NEAR INFRARED SPECTROSCOPY, Industrial and Manufacturing Engineering, CHEMOMETRICS, Chemometrics, BIOLOGICAL MATERIALS, Partial least squares regression, 0202 electrical engineering, electronic engineering, information engineering, Organic matter, CHEMICAL OXYGEN DEMAND, ANAEROBIC DIGESTION PROCESS, LEAST SQUARES APPROXIMATIONS, 0105 earth and related environmental sciences, chemistry.chemical_classification, CHEMICAL ANALYSIS, Chemistry, SIGNAL ANALYSIS, General Chemistry, Biodegradation, BIOGEOCHEMISTRY, NEAR-INFRARED WAVELENGTHS, Anaerobic digestion, 13. Climate action, NEAR-INFRARED WAVELENGTH, [SDE]Environmental Sciences, BIODEGRADABILITY, PRINCIPAL COMPONENT ANALYSIS, Composition (visual arts), PARTIAL LEAST SQUARE (PLS), PARTIAL LEAST SQUARES REGRESSION, Biological system
Abstract

International audience; Near-infrared spectrum analysis coupled with partial least squares regression can predict anaerobic digestion performances. Nonetheless, due to the complexity and diversity of organic matter, a detailed assessment of the effects of the composition of organic matter on biogas production remains a great challenge. Based on 275 samples representing a wide diversity of substrates, the application of the partial least square b coefficients to assess the effects of the involved molecules on the performances of anaerobic digestion processes is discussed. In particular, to accurately predict variables, there is a need to account for the whole near-infrared spectrum. The characterization of organic matter involving proteins, carbohydrate and lipid contents, chemical demand in oxygen, biodegradability, methane yield, and methane production kinetics data is demonstrated.

Published
2018

9. Study of rehydration of different organic substrates by means of near-infrared spectroscopy and signal de-mixing techniques

Author

Alexandre Mallet, Bastien Zennaro, Eric Latrille, Cyrille Charnier, Ryad Bendoula, Jean-Philippe Steyer, Jean Michel Roger, Laboratoire de Biotechnologie de l'Environnement [Narbonne] (LBE), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Information – Technologies – Analyse Environnementale – Procédés Agricoles (UMR ITAP), BioEnTech, and Mallet, Alexandre

Subjects
[STAT.AP]Statistics [stat]/Applications [stat.AP], [STAT.AP] Statistics [stat]/Applications [stat.AP], [SPI.GPROC] Engineering Sciences [physics]/Chemical and Process Engineering, [SPI.GPROC]Engineering Sciences [physics]/Chemical and Process Engineering, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2019

10. Caractérisation rapide des digestats de méthanisation par proche infrarouge pour une meilleure valorisation

Author

Zennaro, bastien, Jimenez, Julie, Steyer, Jean-Philippe, Houot, Sabine, Girardin, Cyril, Marchand, Paul, Thoisy-Dur, J.-C., Fisgativa, Henry, Béline, Fabrice, Miroux, Jeremy, Carnier, Cyrille, Budin, Julien, Richard, Charlotte, Le Cadre Loret, Elodie, Karakachian, Geoffrey, Werly, Julien, Rupin, Fabien, Fallot, Fréderic, Accarion, Guillaume, Baudu, Emmanuelle, Latrille, Eric, Laboratoire de Biotechnologie de l'Environnement [Narbonne] (LBE), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Université Paris Saclay (COmUE), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), BioEnTech, and ENGIE

Subjects
[SDV]Life Sciences [q-bio], [SDE]Environmental Sciences
Published
2019

11. Study of the dehydration of different organic substrates by means of near-infrared spectroscopy and chemometrics: first steps towards a correction strategy

Author

Alexandre Mallet, Eric Latrille, Ryad Bendoula, Cyrille Charnier, Jean-Philippe Steyer, Jean-Michel Roger, Mallet, Alexandre, Laboratoire de Biotechnologie de l'Environnement [Narbonne] (LBE), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Information – Technologies – Analyse Environnementale – Procédés Agricoles (UMR ITAP), and BioEnTech

Subjects
[CHIM.ANAL] Chemical Sciences/Analytical chemistry, [SPI.OPTI] Engineering Sciences [physics]/Optics / Photonic, [CHIM.ANAL]Chemical Sciences/Analytical chemistry, [SPI.GPROC] Engineering Sciences [physics]/Chemical and Process Engineering, [SPI.OPTI]Engineering Sciences [physics]/Optics / Photonic, [SPI.GPROC]Engineering Sciences [physics]/Chemical and Process Engineering, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2019

12. Combining pH and electrical conductivity measurements to improve titrimetric methods to determine ammonia nitrogen, volatile fatty acids and inorganic carbon concentrations

Author

Cyrille Charnier, Laurent Lardon, Eric Latrille, Jean-Philippe Steyer, Jérémie Miroux, Laboratoire de Biotechnologie de l'Environnement [Narbonne] (LBE), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and BioEnTech

Subjects
anaerobic digestion, Environmental Engineering, Coefficient of determination, Nitrogen, [SDV]Life Sciences [q-bio], 0208 environmental biotechnology, Analytical chemistry, chemistry.chemical_element, Context (language use), 02 engineering and technology, 010501 environmental sciences, 01 natural sciences, Ammonia, chemistry.chemical_compound, titrimetric methods, Total inorganic carbon, Waste Management and Disposal, 0105 earth and related environmental sciences, Water Science and Technology, Civil and Structural Engineering, Chromatography, electrical conductivity, Chemistry, volatile fatty acids, Ecological Modeling, Electric Conductivity, Hydrogen-Ion Concentration, Fatty Acids, Volatile, Pollution, Carbon, 020801 environmental engineering, Anaerobic digestion, [SDE]Environmental Sciences, Titration, Gas chromatography
Abstract

International audience; Volatile fatty acids (VFA), inorganic carbon (IC) and total ammonia nitrogen (TAN) are key variables in the current context of anaerobic digestion (AD). Accurate measurements like gas chromatography and infrared spectrometry have been developed to follow the concentration of these compounds but none of these methods are affordable for small AD units. Only titration methods answer the need for small plant monitoring. The existing methods accuracy was assessed in this study and reveals a lack of accuracy and robustness to control AD plants. To solve these issues, a new titrimetric device to estimate the VFA, IC and TAN concentrations with an improved accuracy was developed. This device named SNAC (System of titration for total ammonia Nitrogen, volatile fatty Acids and inorganic Carbon) has been developed combining the measurement of electrical conductivity and pH. SNAC were tested on 24 different plant samples in a range of 0–0.16 mol.L−1 TAN, 0.01–0.21 mol.L−1 IC and 0–0.04 mol.L−1 VFA. The standard error was about 0.012 mol.L−1 TAN, 0.015 mol.L−1 IC and 0.003 mol.L−1 VFA. The coefficient of determination R2 between the estimated and reference data was 0.95, 0.94 and 0.95 for TAN, IC and VFA respectively. Using the same data, current methods based on key pH points lead to standard error more than 14.5 times higher on VFA and more than 1.2 times higher on IC. These results show that SNAC is an accurate tool to improve the management of AD plant.

Published
2016

13. Sécurisation des performances et optimisation de l’alimentation des unités de méthanisation

Author

Charnier, Cyrille, Ramso, R., Latrille, Eric, Steyer, Jean-Philippe, Miroux, Jérémie, BioEnTech, Laboratoire de Biotechnologie de l'Environnement [Narbonne] (LBE), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and Association Technique Energie Environnement (ATEE). FRA.

Subjects
économie de filière, treatment, méthanisation, valorisation, [SDV]Life Sciences [q-bio], impact environnemental, resource management, valorization, biogaz, procédé anaérobie, environmental impact, gestion des ressources, traitement
Abstract

Sécurisation des performances et optimisation de l’alimentation des unités de méthanisation. Journées Recherche et Industrie biogaz méthanisation - JRI 2017

Published
2017

14. Fast ADM1 implementation for the optimization of feeding strategy using near infrared spectroscopy

Author

Jean-Philippe Steyer, Michel Torrijos, Cyrille Charnier, Eric Latrille, Philippe Sousbie, Julie Jimenez, Jérémie Miroux, Laboratoire de Biotechnologie de l'Environnement [Narbonne] (LBE), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA), BioEnTech, Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and Charnier, Cyrille

Subjects
anaerobic digestion, ADM1, spectroscopy, kinetics, methane potential, modeling, Environmental Engineering, 020209 energy, [SDV]Life Sciences [q-bio], Kinetics, Analytical chemistry, digestion anaérobie, 02 engineering and technology, 010501 environmental sciences, 7. Clean energy, 01 natural sciences, near-infrared, modelling, Bioreactors, Partial least squares regression, 0202 electrical engineering, electronic engineering, information engineering, Biochemical composition, Anaerobiosis, Methane production, Waste Management and Disposal, 0105 earth and related environmental sciences, Water Science and Technology, Civil and Structural Engineering, modélisation, Spectroscopy, Near-Infrared, cinétique, Chemistry, Ecological Modeling, Near-infrared spectroscopy, Pollution, potentiel méthane, Anaerobic digestion, Yield (chemistry), spectroscopie proche infrarouge, Biological system, Anaerobic exercise
Abstract

Optimization of feeding strategy is an essential issue of anaerobic co-digestion that can be greatly assisted with simulation tools such as the Anaerobic Digestion Model 1. Using this model, a set of parameters, such as the biochemical composition of the waste to be digested, its methane production yield and kinetics, has to be defined for each new substrate. In the recent years, near infrared analyses have been reported as a fast and accurate solution for the estimation of methane production yield and biochemical composition. However, the estimation of methane production kinetics requires time-consuming analysis. Here, a partial least square regression model was developed for a fast and efficient estimation of methane production kinetics using near infrared spectroscopy on 275 bio-waste samples. The development of this characterization reduces the time of analysis from 30 days to a matter of minutes. Then, biochemical composition and methane production yield and kinetics predicted by near infrared spectroscopy were implemented in a modified Anaerobic Digestion Model n°1 in order to simulate the performance of anaerobic digestion processes. This approach was validated using different data sets and was demonstrated to provide a powerful predictive tool for advanced control of anaerobic digestion plants and feeding strategy optimization.

Published
2017

15. Fast characterization of solid organic waste content with near infrared spectroscopy in anaerobic digestion

Author

Margaux Lemoine, Jérémie Miroux, Cyrille Charnier, Julie Jimenez, Jean-Claude Boulet, Eric Latrille, Jean-Philippe Steyer, BioEnTech, Laboratoire de Biotechnologie de l'Environnement [Narbonne] (LBE), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Sciences Pour l'Oenologie (SPO), Université Montpellier 1 (UM1)-Institut National de la Recherche Agronomique (INRA)-Université de Montpellier (UM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA), Institut National de la Recherche Agronomique (INRA)-Université de Montpellier (UM)-Université Montpellier 1 (UM1)-Institut de Recherche pour le Développement (IRD [Nouvelle-Calédonie])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA), and Université Montpellier 1 (UM1)-Institut de Recherche pour le Développement (IRD [Nouvelle-Calédonie])-Institut National de la Recherche Agronomique (INRA)-Université de Montpellier (UM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)

Subjects
anaerobic digestion, Municipal solid waste, [SDV]Life Sciences [q-bio], 02 engineering and technology, 010501 environmental sciences, 7. Clean energy, 01 natural sciences, near-infrared, Bioreactors, Reference Values, biochemical composition, 0202 electrical engineering, electronic engineering, information engineering, Anaerobiosis, Waste Management and Disposal, Spectroscopy, Near-Infrared, Waste management, Sewage, Chemistry, Hydrolysis, Chemical oxygen demand, déchet solide, Pulp and paper industry, Nitrogen, Lipids, 6. Clean water, Refuse Disposal, solid waste, Methane, spectroscopy, Chemical substance, composition biochimique, near infrared spectroscopy, 020209 energy, solid wastes, Carbohydrates, chemistry.chemical_element, digestion anaérobie, Context (language use), méthode pls, modèle mathématique, chemical oxygen demand, 0105 earth and related environmental sciences, Biological Oxygen Demand Analysis, Models, Statistical, Substrate (chemistry), Proteins, Biodegradable waste, Sulfuric Acids, demande chimique en oxygène, Anaerobic digestion, Glucose, spectroscopie proche infrarouge, mathematical model
Abstract

The development of anaerobic digestion involves both co-digestion of solid wastes and optimization of the feeding recipe. Within this context, substrate characterisation is an essential issue. Although it is widely used, the biochemical methane potential is not sufficient to optimize the operation of anaerobic digestion plants. Indeed the biochemical composition in carbohydrates, lipids, proteins and the chemical oxygen demand of the inputs are key parameters for the optimisation of process performances. Here we used near infrared spectroscopy as a robust and less-time consuming tool to predict the solid waste content in carbohydrates, lipids and nitrogen, and the chemical oxygen demand. We built a Partial Least Square regression model with 295 samples and validated it with an independent set of 46 samples across a wide range of solid wastes found in anaerobic digestion units. The standard errors of cross-validation were 90 mgO2⋅gTS−1 carbohydrates, 2.5 ∗ 10−2 g⋅gTS−1 lipids, 7.2 ∗ 10−3 g⋅gTS−1 nitrogen and 99 mgO2⋅gTS−1 chemical oxygen demand. The standard errors of prediction were 53 mgO2⋅gTS−1 carbohydrates, 3.2 ∗ 10−2 g⋅gTS−1 lipids, 8.6 ∗ 10−3 g⋅gTS−1 nitrogen and 83 mgO2⋅gTS−1 chemical oxygen demand. These results show that near infrared spectroscopy is a new fast and cost-efficient way to characterize solid wastes content and improve their anaerobic digestion monitoring.

Published
2017

16. Prédiction des performances de digestion des substrats : apport pour le pilotage du digesteur

Author

Charnier, Cyrille, Pacaud, S., Miroux, Jeremie, Fick, M., Steyer, Jean-Philippe, Laboratoire de Biotechnologie de l'Environnement [Narbonne] (LBE), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA), BioEnTech, Ecole Nationale Supérieure d'Agronomie et des Industries Alimentaires (ENSAIA), Université de Lorraine (UL), Laboratoire Réactions et Génie des Procédés (LRGP), Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and Moletta Méthanisation. FRA.

Subjects
composition biochimique, codigestion, digesteur, [SDV]Life Sciences [q-bio], digestion anaérobie, substrat, amélioration de la performance, sewage digester
Abstract

Prédiction des performances de digestion des substrats : apport pour le pilotage du digesteur. Journées de la Méthanisation : Applications Agricoles et Industrielles

Published
2016

17. Modelling hydrolysis: simultaneous versus sequential biodegradation of the hydrolysable fractions

Author

Jimenez, Julie, CHARNIER, Cyrille, Latrille, Eric, Torrijos, Michel, Harmand, Jérôme, Patureau, Dominique, Sperandio, Mathieu, Morgenroth, Eberhard, Béline, Fabrice, Ekama, George, Vanrolleghem, Peter, ROBLES MARTINEZ, Angel, Seco, Aurora, Batstone, Damien J., Steyer, Jean-Philippe, Laboratoire de Biotechnologie de l'Environnement [Narbonne] (LBE), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA), BioEnTech, Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés (LISBP), Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de la Recherche Agronomique (INRA), Institute of Environmental Engineering, Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology in Zürich [Zürich] (ETH Zürich), Swiss Federal Institute of Aquatic Science and Technology (EAWAG), Optimisation des procédés en Agriculture, Agroalimentaire et Environnement (UR OPAALE), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), University of Cape Town, Université Laval, IIAMA, Universitat Politecnica de Valencia (UPV), Departament d’Enginyeria Química, Universidad de Valencia, Advanced Water Management Centre (AWMC), University of Queensland (UQ), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut National de la Recherche Agronomique (INRA)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Swiss Federal Insitute of Aquatic Science and Technology [Dübendorf] (EAWAG), Université Laval [Québec] (ULaval), Universitat Politècnica de València (UPV), University of Southern Queensland (USQ), International Water Association (IWA). INT., Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)

Subjects
modelling, adm1, hydrolysis, organic matter, fractionation, [SDV]Life Sciences [q-bio], fractionnement, biodégradation, saccharification, modèle mathématique, hydrolyse, matière organique, mathematical model
Abstract

This paper is a discussion proposal of structural model of organic matter hydrolysis (in aerobic and anaerobic bioprocesses) based on laboratory and model results. Indeed, the hydrolysis mechanism is mechanistically not well understood. Consequently, models are sometimes not appropriate to describe experimental results. Where there are multiple fractions, a model that considers simultaneous degradation of the substrates may not have the resolution to separate the different kinetics. In this paper, we assess sequential extractions to evaluate this as an alternative to simultaneous analysis.

Published
2016

18. Analyse rapide des substrats pour la planification des recettes de mélanges

Author

CHARNIER, Cyrille, Latrille, Eric, Jimenez, Julie, Torrijos, Michel, Lemoine, Margaux, Miroux, Jérémie, Steyer, Jean-Philippe, Laboratoire de Biotechnologie de l'Environnement [Narbonne] (LBE), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA), BioEnTech, Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and Association Technique Energie Environnement (ATEE). FRA.

Subjects
modelling, méthanisation, optimisation du procédé, [SDV]Life Sciences [q-bio], modélisation
Abstract

Analyse rapide des substrats pour la planification des recettes de mélanges. Journées Recherche et Industrie Biogaz méthanisation

Published
2016

19. SNAC: Système de titration pour l’ammoNiac, les Acides gras volatils et le Carbone inorganique

Author

CHARNIER, Cyrille, Latrille, Eric, Miroux, Jérémie, Steyer, Jean-Philippe, Laboratoire de Biotechnologie de l'Environnement [Narbonne] (LBE), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), BioEnTech, Association Technique Energie Environnement (ATEE). FRA., and Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)

Subjects
méthanisation, capteur titimétrique, amélioration des procedes, [SDV]Life Sciences [q-bio], erreur d'estimation
Abstract

Dans le contexte actuel de la méthanisation, l’ammonium et les acides gras volatils font partie des principaux inhibiteurs du procédé de digestion anaérobie. Des estimations précises de ces composés sont possibles par infra-rouge ou chromatographie en phase gazeuse par exemple mais ces méthodes sont trop onéreuses pour les unités de taille modeste. Seules les méthodes de titration correspondent aux attentes de ces unités. Malheureusement, les méthodes de titration manquent de précision et de robustesse sur les milieux complexes tels que les échantillons de digesteur à la ferme, d’unité collective ou encore de digesteurs traitant des boues de station d’épuration. Partant de ce constat, un capteur titrimétrique estimant avec le maximum de précision et de robustesse l’’azote ammoniacal (TAN), les acides gras volatils (AGV) et le carbone inorganique (CI) a été développé. Ce capteur, nommé SNAC (System of titration for Nitrogen, volatil fatty Acids and inorganique Carbon) combine la mesure de la conductivité électrique et du pH lors de la titration. SNAC a été testé sur 24 échantillons d’unités différentes dans une gamme de 0-0.16 mol.L-1 TAN, 0.01-0.21 mol.L-1 CI et 0-0.04 mol.L-1 AGV. L’erreur maximale d’estimation a été de 0.026 mol.L-1 TAN, 0.030 mol.L-1 CI et 0.007 mol.L-1 AGV. Le coefficient de détermination R² entre l’estimation SNAC et la valeur de référence a été de 0.95, 0.94 et 0.95 pour TAN, CI et AGV respectivement. En utilisant les mêmes échantillons, les méthodes actuelles basées uniquement sur une analyse du pH en point clés donnent une erreur maximale de 0.124 mol.L-1 AGV et 0.088 mol.L-1 CI et un coefficient de détermination R² de 0.87 CI et 0.05 AGV. Ces résultats montrent que SNAC est un outil précis et adaptés pour améliorer le pilotage des unités de digestion anaérobie. Il est en cours de prototypage afin d’être utilisé pour le suivi de digesteurs au stade industriel.

Published
2016

20. Procédé de configuration d’un modèle de prédiction des caractéristiques d’un substrat de méthaniseur et utilisation de modèles pour l’estimation de la biodégradation d’un substrat

Author

CHARNIER, Cyrille, Latrille, Eric, Jimenez, Julie, Miroux, Jérémie, Steyer, Jean-Philippe, Torrijos, Michel, Sousbie, Philippe, Lemoine, Margaux, Laboratoire de Biotechnologie de l'Environnement [Narbonne] (LBE), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and BioEnTech

Subjects
[SDV]Life Sciences [q-bio], [SDE]Environmental Sciences
Abstract

L'invention concerne un procédé de configuration d'un modèle de prédiction des caractéristiques d'un substrat destiné à être incorporé dans un méthaniseur pour être biodégradé par digestion anaérobie qui met en œuvre une corrélation entre des données spectrales dans le proche infrarouge des caractéristiques expérimentales d'échantillons de substrats. L'invention a aussi pour objet un procédé d'estimation des caractéristiques de la biodégradation d'un substrat par digestion anaérobie dans un méthaniseur.

Published
2016

21. Biochemical composition and methane production correlations

Author

CHARNIER, Cyrille, Latrille, Eric, Moscoviz, Roman, Miroux, Jérémie, Steyer, Jean-Philippe, Laboratoire de Biotechnologie de l'Environnement [Narbonne] (LBE), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), BioEnTech, and International Water Association (IWA). INT.

Subjects
solid waste characterization, bioénergie, composition biochimique, valorisation de la matière organique, biochemical methane potential, principal component analysis, [SDV]Life Sciences [q-bio], déchet solide, digestion anaérobie, biochemical composition, kinetic of methane production, chemical oxygen demand, potentiel méthane, demande chimique en oxygène, production de méthane, [SDE]Environmental Sciences
Abstract

International audience; Substrates for anaerobic digestion are composed of heterogeneous and complex organic matter. General parameters of the organic matter can be used to describe its composition such as sugar, protein and lipid contents, Chemical Oxygen Demand (COD), Biochemical Methane Potential (BMP) and kinetic of methane production. These parameters are required for the monitoring of digesters but their characterization are time consuming and expensive; thus, these parameters are rarely assessed all together. We investigated the existing correlations between COD, methane yield, biodegradability, kinetic of methane production and sugar, protein and lipid contents on 222 samples using principal component analysis and Pearson’s correlation coefficient. Our work aims at providing a support to operators when only a partial characterization of the substrates is available. Positive correlation between methane yield and methane production kinetic but also unexpected independence of COD and BMP are shown. Sugars, proteins and lipids modulated differently the anaerobic digestion performances. Sugars are correlated with readily biodegradable organic matter while proteins are slightly anti-correlated with biodegradability. Lipids were correlated with high methane yield but slow methane productions. Finally, partial least square regression points towards more descriptive organic matter characterization that could be used to predict the kinetic of methane production.

Published
2016

22. FTIR spectroscopic discrimination of Saccharomyces cerevisiae and Saccharomyces bayanus strains

Author

Sabine Gognies, Julien BudinJ. Budin, Ganesh D. Sockalingum, A. Belarbi, Christophe Sandt, Achim Kohler, Isabelle Adt, Michel Manfait, Bioingénierie et Dynamique Microbienne aux Interfaces Alimentaires (BIODYMIA), Isara-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon, Norwegian University of Life Sciences (NMBU), BioEnTech, Synchrotron SOLEIL (SSOLEIL), Centre National de la Recherche Scientifique (CNRS), Université de Reims Champagne-Ardenne (URCA), Médicaments : Dynamique Intracellulaire et Architecture Nucléaire (MéDIAN), and Université de Reims Champagne-Ardenne (URCA)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[SDV.BIO]Life Sciences [q-bio]/Biotechnology, Sequence analysis, Immunology, Saccharomyces cerevisiae, Saccharomyces bayanus, Wine, Applied Microbiology and Biotechnology, Microbiology, Saccharomyces, 03 medical and health sciences, Species Specificity, Sequence Homology, Nucleic Acid, Spectroscopy, Fourier Transform Infrared, [SDV.IDA]Life Sciences [q-bio]/Food engineering, Genetics, Food Industry, [SDV.BBM.BC]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biochemistry [q-bio.BM], DNA, Fungal, Mycological Typing Techniques, Molecular Biology, ComputingMilieux_MISCELLANEOUS, Candida, 030304 developmental biology, Gel electrophoresis, Principal Component Analysis, 0303 health sciences, biology, Strain (chemistry), Ascomycota, 030306 microbiology, Sequence Analysis, DNA, General Medicine, biology.organism_classification, Yeast, [SDV.BBM.BP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biophysics, Biochemistry, Genome, Fungal
Abstract

In this study, we tested the potential of Fourier-transform infrared absorption spectroscopy to screen, on the one hand, Saccharomyces cerevisiae and non-S. cerevisiae strains and, on the other hand, to discriminate between S. cerevisiae and Saccharomyces bayanus strains. Principal components analysis (PCA), used to compare 20 S. cerevisiae and 21 non-Saccharomyces strains, showed only 2 misclassifications. The PCA model was then used to classify spectra from 14 Samos strains. All 14 Samos strains clustered together with the S. cerevisiae group. This result was confirmed by a routinely used electrophoretic pattern obtained by pulsed-field gel electrophoresis. The method was then tested to compare S. cerevisiae and S. bayanus strains. Our results indicate that identification at the strain level is possible. This first result shows that yeast classification and S. bayanus identification can be feasible in a single measurement.

Published
2010

23. Robust near-infrared spectroscopic characterization of wet organic wastes: how to avoid water interferences ?

Author

Alexandre Mallet, Bastien Zennaro, Jean Michel Roger, Cyrille Charnier, Ryad Bendoula, Eric Latrille, Jean-Philippe Steyer, Mallet, Alexandre, Laboratoire de Biotechnologie de l'Environnement [Narbonne] (LBE), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Information – Technologies – Analyse Environnementale – Procédés Agricoles (UMR ITAP), and BioEnTech

Subjects
[STAT.AP]Statistics [stat]/Applications [stat.AP], [STAT.AP] Statistics [stat]/Applications [stat.AP], [SPI.GPROC] Engineering Sciences [physics]/Chemical and Process Engineering, [SPI.GPROC]Engineering Sciences [physics]/Chemical and Process Engineering, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

24. Rapid characterization of sulfur and phosphorus in organic waste by near infrared spectroscopy.

Author

Awhangbo L, Severac M, Charnier C, Latrille E, and Steyer JP

Subjects
Sulfur, Carbohydrates, Spectroscopy, Near-Infrared methods, Phosphorus
Abstract

Near-infrared spectroscopy (NIRS) has recently emerged as a valuable tool for monitoring organic waste utilized in anaerobic digestion processes. Over the past decade, NIRS has significantly improved the characterization of organic waste by enabling the prediction of several crucial parameters such as biochemical methane potential, carbohydrate, lipid and nitrogen contents, Chemical Oxygen Demand, and kinetic parameters. This study investigates the application of NIRS for predicting the levels of Sulfur (S) and Phosphorus (P) within organic waste materials. The results for sulfur prediction exhibited a high level of accuracy, yielding an error of 1.21 g/Kg[TS] in an independently validated dataset, coupled with an R-squared value of 0.84. Conversely, the prediction of phosphorus proved to be slightly less successful, showing an error of 1.49 g/Kg[TS] with an R-squared value of 0.70. Furthermore, the disparities in performance seem to stem from the inherent correlation between the spectral data and the sulfur or phosphorus contents. Significantly, a variable selection technique known as CovSel was employed, shedding light on the differing approaches used for sulfur and phosphorus predictions. In the case of sulfur, the prediction was achieved through a direct correlation with wavelengths associated with sulfur-related functional groups (such as R - S(=O) 2  - OH, -SH, and R-S-S-R) present in the NIR spectra. In contrast, phosphorus prediction relied on an indirect correlation with absorption bands related to organic matter (including CH, CH 2 , CH 3 , -CHO, R-OH, C = O, -CO 2 H, and CONH)., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Ltd.)

Published
2024
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25. Determination of the optimal feed recipe of anaerobic digesters using a mathematical model and a genetic algorithm.

Author

Awhangbo L, Schmitt V, Marcilhac C, Charnier C, Latrille E, and Steyer JP

Subjects
Anaerobiosis, Models, Theoretical, Algorithms, Bioreactors, Methane
Abstract

Recently, numerous experimental studies have been undertaken to understand the interactions between different feedstocks in anaerobic digestion. They have unveiled the potential of blending substrates in the process. Nevertheless, these experiments are time-intensive, prompting the exploration of various optimization approaches. Notably, genetic algorithms have gained interest due to their population-based structures allowing them to efficiently yield multiple Pareto-optimal solutions in a single run. This study uses a simplified static anaerobic co-digestion model as the fitness function for a multi-objective optimization. The optimization aims to achieve a methane production set-point while reducing the output ammonia nitrogen and increasing the recipe' profitability. Thus, the study employs genetic algorithms to identify Pareto fronts and constraints confined the solution space within feasible boundaries. It also underscores the influence of economic considerations on the viable solution space. Ultimately, the optimal feed recipe not only ensures stable operations within the digester but also enhances associated profits., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Published
2024
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26. Fast and robust NIRS-based characterization of raw organic waste: Using non-linear methods to handle water effects.

Author

Mallet A, Charnier C, Latrille É, Bendoula R, Roger JM, and Steyer JP

Subjects
Biological Oxygen Demand Analysis, Least-Squares Analysis, Water, Methane, Spectroscopy, Near-Infrared methods
Abstract

Fast characterization of organic waste using near infrared spectroscopy (NIRS) has been successfully developed in the last decade. However, up to now, an on-site use of this technology has been hindered by necessary sample preparation steps (freeze-drying and grinding) to avoid important water effects on NIRS. Recent research studies have shown that these effects are highly non-linear and relate both to the biochemical and physical properties of samples. To account for these complex effects, the current study compares the use of many different types of non-linear methods such as partial least squares regression (PLSR) based methods (global, clustered and local versions of PLSR), machine learning methods (support vector machines, regression trees and ensemble methods) and deep learning methods (artificial and convolutional neural networks). On an independent test data set, non-linear methods showed errors 28% lower than linear methods. The standard errors of prediction obtained for the prediction of total solids content (TS%), chemical oxygen demand (COD) and biochemical methane potential (BMP) were respectively 8%, 160 mg(O 2 ).gTS -1 and 92 mL(CH 4 ).gTS -1 . These latter errors are similar to successful NIRS applications developed on freeze-dried samples. These findings hold great promises regarding the development of at-site and online NIRS solutions in anaerobic digestion plants., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier Ltd.)

Published
2022
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27. Preprocessing NIR Spectra for Aquaphotomics.

Author

Roger JM, Mallet A, and Marini F

Subjects
Spectroscopy, Near-Infrared methods
Abstract

Even though NIR spectroscopy is based on the Beer-Lambert law, which clearly relates the concentration of the absorbing elements with the absorbance, the measured spectra are subject to spurious signals, such as additive and multiplicative effects. The use of NIR spectra, therefore, requires a preprocessing step. This article reviews the main preprocessing methods in the light of aquaphotomics. Simple methods for visualizing the spectra are proposed in order to guide the user in the choice of the best preprocessing. The most common chemometrics preprocessing are presented and illustrated by three real datasets. Some preprocessing aims to produce a spectrum as close as possible to the absorbance that would have been measured under ideal conditions and is very useful for the establishment of an aquagram. Others, dedicated to the improvement of the resolution of the spectra, are very useful for the identification of the peaks. Finally, special attention is given to the problem of reducing multiplicative effects and to the potential pitfalls of some very popular methods in chemometrics. Alternatives proposed in recent papers are presented.

Published
2022
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28. Agronomic characterization of anaerobic digestates with near-infrared spectroscopy.

Author

Zennaro B, Marchand P, Latrille E, Thoisy JC, Houot S, Girardin C, Steyer JP, Béline F, Charnier C, Richard C, Accarion G, and Jimenez J

Subjects
Anaerobiosis, Biofuels, Carbon, Phosphorus, Potassium, Nitrogen analysis, Spectroscopy, Near-Infrared
Abstract

Anaerobic digestion is an increasingly widespread process for organic waste treatment and renewable energy production due to the methane content of the biogas. This biological process also produces a digestate (i.e., the remaining content of the waste after treatment) with a high fertilizing potential. The digestate composition is highly variable due to the various organic wastes used as feedstock, the different plant configurations, and the post-treatment processes used. In order to optimize digestate spreading on agricultural soils by optimizing the fertilizer dose and, thus, reducing environmental impacts associated to digestate application, the agronomic characterization of digestate is essential. This study investigates the use of near infrared spectroscopy for predicting the most important agronomic parameters from freeze-dried digestates. A data set of 193 digestates was created to calibrate partial least squares regression models predicting organic matter, total organic carbon, organic nitrogen, phosphorus, and potassium contents. The calibration range of the models were between 249.8 and 878.6 gOM.kgDM -1 , 171.9 and 499.5 gC.kgDM -1 , 5.3 and 74.1 gN.kgDM -1 , 2.7 and 44.9 gP.kgDM -1 and between 0.5 and 171.8 gK.kgDM -1 , respectively. The calibrated models reliably predicted organic matter, total organic carbon, and phosphorus contents for the whole diversity of digestates with root mean square errors of prediction of 70.51 gOM.kgDM -1 , 34.84 gC.kgDM -1 and 4.08 gP.kgDM -1 , respectively. On the other hand, the model prediction of the organic nitrogen content had a root mean square error of 7.55 gN.kgDM -1 and was considered as acceptable. Lastly, the results did not demonstrate the feasibility of predicting the potassium content in digestates with near infrared spectroscopy. These results show that near infrared spectroscopy is a very promising analytical method for the characterization of the fertilizing value of digestates, which could provide large benefits in terms of analysis time and cost., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Published
2022
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29. Relating Near-Infrared Light Path-Length Modifications to the Water Content of Scattering Media in Near-Infrared Spectroscopy: Toward a New Bouguer-Beer-Lambert Law.

Author

Mallet A, Tsenkova R, Muncan J, Charnier C, Latrille É, Bendoula R, Steyer JP, and Roger JM

Abstract

In near-infrared spectroscopy (NIRS), the linear relationship between absorbance and an absorbing compound concentration has been strictly defined by the Bouguer-Beer-Lambert law only for the case of transmission measurements of nonscattering media. However, various quantitative calibrations have been successfully built both on reflectance measurements and for scattering media. Although the lack of linearity for scattering media has been observed experimentally, the sound multivariate statistics and signal processing involved in chemometrics have allowed us to overcome this problem in most cases. However, in the case of samples with varying water content, important modifications of scattering levels still make calibrations difficult to build due to nonlinearities. Moreover, even when calibration procedures are successfully developed, many preprocessing methods used do not guarantee correct spectroscopic assignments (in the sense of a pure chemical absorbance). In particular, this may prevent correct modeling and interpretation of the structure of water. In this study, dynamic near-infrared spectra acquired during a drying process allow the study of the physical effects of water content variations, with a focus on the first overtone OH absorbance region. A model sample consisting of aluminum pellets mixed with water allowed us to study this specifically, without any other absorbing interaction terms related to the dry mass-absorbing constituents. A new formulation of the Bouguer-Beer-Lambert law is proposed, by expressing path length as a power function of water content. Through this new formulation, it is shown that a better and simpler prediction model of water content may be developed, with more precise and accurate identification of water absorbance bands.

Published
2021
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30. Fast at-line characterization of solid organic waste: Comparing analytical performance of different compact near infrared spectroscopic systems with different measurement configurations.

Author

Mallet A, Pérémé M, Awhangbo L, Charnier C, Roger JM, Steyer JP, Latrille É, and Bendoula R

Subjects
Biofuels, Biological Oxygen Demand Analysis, Methane analysis, Solid Waste, Spectroscopy, Near-Infrared
Abstract

Fast characterization of solid organic waste using near infrared spectroscopy has been successfully developed in the last decade. However, its adoption in biogas plants for monitoring the feeding substrates remains limited due to the lack of applicability and high costs. Recent evolutions in the technology have given rise to both more compact and more modular low-cost near infrared systems which could allow a larger scale deployment. The current study investigates the relevance of these new systems by evaluating four different Fourier transform near-infrared spectroscopic systems with different compactness (laboratory, portable, micro spectrometer) but also different measurement configurations (polarized light, at distance, in contact). Though the conventional laboratory spectrometer showed the best performance on the various biochemical parameters tested (carbohydrates, lipids, nitrogen, chemical oxygen demand, biochemical methane potential), the compact systems provided very close results. Prediction of the biochemical methane potential was possible using a low-cost micro spectrometer with an independent validation set error of only 91 NmL(CH 4 ).gTS -1 compared to 60 NmL(CH 4 ).gTS -1 for a laboratory spectrometer. The differences in performance were shown to result mainly from poorer spectral sampling; and not from instrument characteristics such as spectral resolution. Regarding the measurement configurations, none of the evaluated systems allowed a significant gain in robustness. In particular, the polarized light system provided better results when using its multi-scattered signal which brings further evidence of the importance of physical light-scattering properties in the success of models built on solid organic waste., Competing Interests: Declaration of Competing Interest The authors declared that there is no conflict of interest., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Published
2021
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31. Unveiling non-linear water effects in near infrared spectroscopy: A study on organic wastes during drying using chemometrics.

Author

Mallet A, Charnier C, Latrille É, Bendoula R, Steyer JP, and Roger JM

Subjects
Calibration, Freeze Drying, Water, Desiccation, Spectroscopy, Near-Infrared
Abstract

In the context of organic waste management, near infrared spectroscopy (NIRS) is being used to offer a fast, non-destructive, and cost-effective characterization system. However, cumbersome freeze-drying steps of the samples are required to avoid water's interference on near infrared spectra. In order to better understand these effects, spectral variations induced by dry matter content variations were obtained for a wide variety of organic substrates. This was made possible by the development of a customized near infrared acquisition system with dynamic highly-resolved simultaneous scanning of near infrared spectra and estimation of dry matter content during a drying process at ambient temperature. Using principal components analysis, the complex water effects on near infrared spectra are detailed. Water effects are shown to be a combination of both physical and chemical effects, and depend on both the characteristics of the samples (biochemical type and physical structure) and the moisture content level. This results in a non-linear relationship between the measured signal and the analytical characteristic of interest. A typology of substrates with respect to these water effects is provided and could further be efficiently used as a basis for the development of local quantitative calibration models and correction methods accounting for these water effects., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Published
2021
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32. Comprehensive determination of input state variables dataset required for anaerobic digestion modelling (ADM1) based on characterisation of organic substrates.

Author

Fisgativa H, Zennaro B, Charnier C, Richard C, Accarion G, and Béline F

Abstract

This article contains the data of 11 organic substrates including physicochemical, biochemical and nutritional characterisations. Additionally, it includes for all substrates the data of organic matter fractionation into easily biodegradable, slowly biodegradable and inert fractions performed with anaerobic respirometry method. Finally, based on physicochemical characterisations and organic matter fractionation, a detailed methodology for the determination of input state variables required for the anaerobic digestion model N°1 (ADM1) was presented and the dataset for all substrates is provided. An example of calculation for one substrate illustrates the methodology for the determination of these variables. Data provided in this article could be useful to any person interested in modelling anaerobic digestion and particularly co-digestion. Data could be also used for implementation of a database for anaerobic digestion modelling., (© 2020 The Author(s).)

Published
2020
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33. Modelling hydrolysis: Simultaneous versus sequential biodegradation of the hydrolysable fractions.

Author

Jimenez J, Charnier C, Kouas M, Latrille E, Torrijos M, Harmand J, Patureau D, Spérandio M, Morgenroth E, Béline F, Ekama G, Vanrolleghem PA, Robles A, Seco A, Batstone DJ, and Steyer JP

Subjects
Anaerobiosis, Biodegradation, Environmental, Bioreactors, Hydrolysis, Methane, Models, Theoretical, Sewage
Abstract

Hydrolysis is considered the limiting step during solid waste anaerobic digestion (including co-digestion of sludge and biosolids). Mechanisms of hydrolysis are mechanistically not well understood with detrimental impact on model predictive capability. The common approach to multiple substrates is to consider simultaneous degradation of the substrates. This may not have the capacity to separate the different kinetics. Sequential degradation of substrates is theoretically supported by microbial capacity and the composite nature of substrates (bioaccessibility concept). However, this has not been experimentally assessed. Sequential chemical fractionation has been successfully used to define inputs for an anaerobic digestion model. In this paper, sequential extractions of organic substrates were evaluated in order to compare both models. By removing each fraction (from the most accessible to the least accessible fraction) from three different substrates, anaerobic incubation tests showed that for physically structured substrates, such as activated sludge and wheat straw, sequential approach could better describe experimental results, while this was less important for homogeneous materials such as pulped fruit. Following this, anaerobic incubation tests were performed on five substrates. Cumulative methane production was modelled by the simultaneous and sequential approaches. Results showed that the sequential model could fit the experimental data for all the substrates whereas simultaneous model did not work for some substrates., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Published
2020
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34. Fast ADM1 implementation for the optimization of feeding strategy using near infrared spectroscopy.

Author

Charnier C, Latrille E, Jimenez J, Torrijos M, Sousbie P, Miroux J, and Steyer JP

Subjects
Anaerobiosis, Kinetics, Bioreactors, Spectroscopy, Near-Infrared
Abstract

Optimization of feeding strategy is an essential issue of anaerobic co-digestion that can be greatly assisted with simulation tools such as the Anaerobic Digestion Model 1. Using this model, a set of parameters, such as the biochemical composition of the waste to be digested, its methane production yield and kinetics, has to be defined for each new substrate. In the recent years, near infrared analyses have been reported as a fast and accurate solution for the estimation of methane production yield and biochemical composition. However, the estimation of methane production kinetics requires time-consuming analysis. Here, a partial least square regression model was developed for a fast and efficient estimation of methane production kinetics using near infrared spectroscopy on 275 bio-waste samples. The development of this characterization reduces the time of analysis from 30 days to a matter of minutes. Then, biochemical composition and methane production yield and kinetics predicted by near infrared spectroscopy were implemented in a modified Anaerobic Digestion Model n°1 in order to simulate the performance of anaerobic digestion processes. This approach was validated using different data sets and was demonstrated to provide a powerful predictive tool for advanced control of anaerobic digestion plants and feeding strategy optimization., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Published
2017
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35. Fast characterization of solid organic waste content with near infrared spectroscopy in anaerobic digestion.

Author

Charnier C, Latrille E, Jimenez J, Lemoine M, Boulet JC, Miroux J, and Steyer JP

Subjects
Anaerobiosis, Biological Oxygen Demand Analysis, Carbohydrates chemistry, Glucose chemistry, Hydrolysis, Lipids chemistry, Models, Statistical, Nitrogen, Proteins chemistry, Reference Values, Sulfuric Acids chemistry, Bioreactors, Methane metabolism, Refuse Disposal methods, Sewage chemistry, Solid Waste analysis, Spectroscopy, Near-Infrared methods
Abstract

The development of anaerobic digestion involves both co-digestion of solid wastes and optimization of the feeding recipe. Within this context, substrate characterisation is an essential issue. Although it is widely used, the biochemical methane potential is not sufficient to optimize the operation of anaerobic digestion plants. Indeed the biochemical composition in carbohydrates, lipids, proteins and the chemical oxygen demand of the inputs are key parameters for the optimisation of process performances. Here we used near infrared spectroscopy as a robust and less-time consuming tool to predict the solid waste content in carbohydrates, lipids and nitrogen, and the chemical oxygen demand. We built a Partial Least Square regression model with 295 samples and validated it with an independent set of 46 samples across a wide range of solid wastes found in anaerobic digestion units. The standard errors of cross-validation were 90mgO 2 ⋅gTS -1 carbohydrates, 2.5∗10 -2 g⋅gTS -1 lipids, 7.2∗10 -3 g⋅gTS -1 nitrogen and 99mgO 2 ⋅gTS -1 chemical oxygen demand. The standard errors of prediction were 53mgO 2 ⋅gTS -1 carbohydrates, 3.2∗10 -2 g⋅gTS -1 lipids, 8.6∗10 -3 g⋅gTS -1 nitrogen and 83mgO 2 ⋅gTS -1 chemical oxygen demand. These results show that near infrared spectroscopy is a new fast and cost-efficient way to characterize solid wastes content and improve their anaerobic digestion monitoring., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Published
2017
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36. Combining pH and electrical conductivity measurements to improve titrimetric methods to determine ammonia nitrogen, volatile fatty acids and inorganic carbon concentrations.

Author

Charnier C, Latrille E, Lardon L, Miroux J, and Steyer JP

Subjects
Carbon, Electric Conductivity, Fatty Acids, Volatile, Hydrogen-Ion Concentration, Ammonia, Nitrogen
Abstract

Volatile fatty acids (VFA), inorganic carbon (IC) and total ammonia nitrogen (TAN) are key variables in the current context of anaerobic digestion (AD). Accurate measurements like gas chromatography and infrared spectrometry have been developed to follow the concentration of these compounds but none of these methods are affordable for small AD units. Only titration methods answer the need for small plant monitoring. The existing methods accuracy was assessed in this study and reveals a lack of accuracy and robustness to control AD plants. To solve these issues, a new titrimetric device to estimate the VFA, IC and TAN concentrations with an improved accuracy was developed. This device named SNAC (System of titration for total ammonia Nitrogen, volatile fatty Acids and inorganic Carbon) has been developed combining the measurement of electrical conductivity and pH. SNAC were tested on 24 different plant samples in a range of 0-0.16 mol.L(-1) TAN, 0.01-0.21 mol.L(-1) IC and 0-0.04 mol.L(-1) VFA. The standard error was about 0.012 mol.L(-1) TAN, 0.015 mol.L(-1) IC and 0.003 mol.L(-1) VFA. The coefficient of determination R(2) between the estimated and reference data was 0.95, 0.94 and 0.95 for TAN, IC and VFA respectively. Using the same data, current methods based on key pH points lead to standard error more than 14.5 times higher on VFA and more than 1.2 times higher on IC. These results show that SNAC is an accurate tool to improve the management of AD plant., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Published
2016
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