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1. Integrating anaerobic digestion, hydrothermal liquefaction, and biomethanation within a power-to-gas framework for dairy waste management and grid decarbonization: a techno-economic assessment

Author

Camilo Lopez, Nazih Kassem, James Hockey, Jefferson W. Tester, Largus T. Angenent, and Laurent Lardon

Subjects
Power to gas, Low-carbon fuel standard, Waste management, Renewable Energy, Sustainability and the Environment, business.industry, 020209 energy, Renewable Fuel Standard, Energy Engineering and Power Technology, 02 engineering and technology, 010501 environmental sciences, 01 natural sciences, Renewable energy, Renewable natural gas, Anaerobic digestion, Hydrothermal liquefaction, Fuel Technology, Biogas, 0202 electrical engineering, electronic engineering, information engineering, Environmental science, business, 0105 earth and related environmental sciences
Abstract

This study highlights the potential of combining energy and dairy waste management systems to maximize resource recovery and reduce dairy farming environmental impacts. A combined anaerobic digestion, hydrothermal liquefaction, and a biomethanation process system was used to generate Renewable Natural Gas (RNG). The biomethanation step is part of a Power-to-Gas (PtG) system incorporating an electrolyzer that generates hydrogen to react with carbon dioxide to produce additional RNG. A distributed system of biorefineries was utilized to evaluate the economic feasibility of producing renewable biomethane for gas pipeline injection in an effort to decarbonize New York's natural gas grid and lower environmental impacts. Considering the waste produced by 397 000 dairy cows in NY State, this distributed biorefinery system was shown to generate 22 million MJ of RNG per year. The RNG selling price, as defined by carbon credit pricing mechanisms, was found to be the most critical factor in determining economic viability. For example, using the Low Carbon Fuel Standard (LCFS) and the Renewable Fuel Standard (RFS) result in a $7 billion 20 year NPV and a competitive effective Levelized Cost of Energy (LCOEe) of $10 per GJ.

Published
2020

2. Contributors

Author

Helena M. Amaro, Lorenzo Ferrari Assú Tessari, Olivier Bernard, Thallada Bhaskar, Bijoy Biswas, Sai Kishore Butti, A. Catarina Guedes, Rashmi Chandra, Jo-Shu Chang, Tawan Chatsungnoen, Yi-Di Chen, Chun-Yen Chen, Yusuf Chisti, Pierre Collet, Jorge Alberto Vieira Costa, Júlio Cesar de Carvalho, Bárbara Catarina Bastos Freitas, Michele Greque Morais, Su-Chiung Fang, F.G. Acién Fernández, Emilio Molina Grima, Arnaud Hélias, Shih-Hsin Ho, I-Chen Hu, Susan Grace Karp, Choon Gek Khoo, Gerhard Knothe, Man Kee Lam, Laurent Lardon, Duu-Jong Lee, Keat Teong Lee, Luiz Alberto Junior Letti, Fei-Yu Liu, Giorgos Markou, Bryan Gregory Mitchell, Florian Monlau, Marjorie Morales, Dillirani Nagarajan, Carlos José Dalmas Neto, Gregory W. O’Neil, M. Prathima Devi, Wen-Ying Qu, K. Rajesh, Christopher M. Reddy, M.V. Rohit, Thaisa Duarte Santos, José María Fernández Sevilla, Kuan-Yeow Show, Carlos Ricardo Soccol, Isabel Sousa-Pinto, Jean-Philippe Steyer, Eduardo Bittencourt Sydney, Alessandra Cristine Novak Sydney, S. Venkata Mohan, G. Venkata Subhash, Francisco Menino Destéfanis Vítola, Yue Wang, F. Xavier Malcata, Yue-Gen Yan, and Hong-Wei Yen

Published
2019

3. Life-cycle assessment of microalgal-based biofuel

Author

Marjorie Morales, Pierre Collet, Laurent Lardon, Arnaud Hélias, Jean-Philippe Steyer, and Olivier Bernard

Subjects
020209 energy, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 010501 environmental sciences, 01 natural sciences, 0105 earth and related environmental sciences
Published
2019

5. 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

6. Life-Cycle Assessment of Microalgal-Based Biofuels

Author

Jean-Philippe Steyer, Laurent Lardon, Pierre Collet, Arnaud Hélias, Olivier Bernard, Daniele Spinelli, Gouzé, Jean-Luc, LBE, 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 d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA), Department of Chemistry [Sienne], Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Biological control of artificial ecosystems (BIOCORE), Institut National de la Recherche Agronomique (INRA)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'océanographie de Villefranche (LOV), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), A. Pandey and D.-J. Lee and Y. Chisti and C. R. Soccol, Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA)-Laboratoire d'océanographie de Villefranche (LOV), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de la Mer de Villefranche (IMEV), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de la Mer de Villefranche (IMEV), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), 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 d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Laboratoire d'océanographie de Villefranche (LOV), Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA), Distributed Object Systems (SOR), Inria Paris-Rocquencourt, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), and Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Engineering, Natural resource economics, 020209 energy, 02 engineering and technology, 010501 environmental sciences, 7. Clean energy, 01 natural sciences, environmental impact, 12. Responsible consumption, Bioenergy, [INFO.INFO-AU]Computer Science [cs]/Automatic Control Engineering, 0202 electrical engineering, electronic engineering, information engineering, Production (economics), Environmental impact assessment, Life-cycle assessment, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, 2. Zero hunger, Waste management, business.industry, [SDE.IE]Environmental Sciences/Environmental Engineering, microalgae, LCA, Global warming, Fossil fuel, [SDE.ES]Environmental Sciences/Environmental and Society, Renewable energy, 13. Climate action, Biofuel, [SDE]Environmental Sciences, biofuel, business, [INFO.INFO-AU] Computer Science [cs]/Automatic Control Engineering
Abstract

International audience; Fossil fuel depletion and attempts of global warming mitigation have motivated the development of biofuels. Several feedstock and transformation pathways into biofuel have been proposed as an alternative to usual fuels. Recently, microalgae have attracted a lot of attention because of the promise of reduced competition with food crop and lowered environmental impacts. Over the last years, several Life Cycle Assessments have been realised to evaluate the energetic benefit and Global Warming Potential reduction of biofuel and bioenergy produced from microalgae. This chapter presents a bibliographic review of fifteen LCA of microalgae production and/or transformation into biofuel. These studies differ often by the perimeter of the study, the functional unit and the production technologies or characteristics. Methods for the environmental impacts assessment and the energy balance computation also diverge. This review aims at identifying the main options and variations between LCAs and concludes by some recommendations and guidelines to improve the contribution of an LCA and to facilitate the comparison between studies.; Cette publication propose une review des travaux d'analyse du cycle de vie (ACV) réalisés sur des microalgues à vocation énergétique. Les principales hypothèses sur lesquelles s'appuient les 15 études sont analysées, et leurs conclusions comparées. Des recommandations sont proposées pour l'étude de l'impact des procédés à base de microalgues.

Published
2018

7. Limites actuelles du cadre de l'analyse du cycle de vie pour évaluer la durabilité : cas de deux technologies immatures pour produire des bio-carburants

Author

Kati Koponen, Laurent Lardon, Pekka Leskinen, Anne Holma, Philippe Roux, Riina Antikainen, Finnish Environment Institute (SYKE), VTT Technical Research Centre of Finland (VTT), 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 la Recherche Agronomique (INRA), Information – Technologies – Analyse Environnementale – Procédés Agricoles (UMR ITAP), 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), 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), Marie Curie's International Research Staff Exchange Scheme (ECO-TOOL Project) [PIRSES-GA-2008-230851], PEER partnership (Partnership of seven of the largest European environmental centres), SUBICHOE-project (Sustainability of biomass utilisation in changing operational environment), TEKES - the Finnish Funding Agency for Technology and Innovation, Finnish Ministry of Employment and the Economy, Finnish Ministry of Agriculture and Forestry, and Doctoral Programme in Energy Efficiency and Systems (EES) of the Academy of Finland

Subjects
Natural resource economics, 020209 energy, Strategy and Management, biodiesel, 02 engineering and technology, 010501 environmental sciences, 7. Clean energy, 01 natural sciences, ENVIRONMENTAL IMPACTS, Industrial and Manufacturing Engineering, MICROALGAE, 12. Responsible consumption, BIODIESEL, greenhouse gas emission, life cycle assessment, 11. Sustainability, SDG 13 - Climate Action, 0202 electrical engineering, electronic engineering, information engineering, forest biomass, Land use, land-use change and forestry, SDG 7 - Affordable and Clean Energy, Life-cycle assessment, SDG 15 - Life on Land, 0105 earth and related environmental sciences, General Environmental Science, 2. Zero hunger, LIFE CYCLE ASSESSMENT, Land use, Renewable Energy, Sustainability and the Environment, microalgae, FOREST BIOMASS, Environmental engineering, environmental impacts, 15. Life on land, Resource depletion, GREENHOUSE GAS EMISSION, 13. Climate action, Biofuel, Greenhouse gas, [SDE]Environmental Sciences, Sustainability, Environmental science, SDG 12 - Responsible Consumption and Production, Renewable resource
Abstract

International audience; The growing need to use biofuel raw materials that do not compete with food and feed have resulted in a growing interest in lignocellulosic materials and microalgae. However, the life cycle environmental benefits of both biofuels have been questioned. The aim of this study was to evaluate how environmental sustainability of forest-based and microalgae biodiesel can be estimated by using the life cycle assessment framework. These biofuel chains were chosen because they are contrasting systems, as the first one is based on a “natural” feedstock production system, while the second one is an entirely anthropogenic system using an artificial infrastructure and external inputs to grow microalgae. This study focuses on life cycle impact categories still under methodological development, namely resource depletion, land use and land use change, water use, soil quality impacts and biodiversity. In addition, climate impacts were quantified in order to exemplify the uncertainty of the results and the complexity of estimating the parameters. This study demonstrates the difficulty to assess the absolute range of the total environmental impacts of the two systems. The results propose that the greenhouse gas emissions of microalgae biodiesel are higher than those of forest residue-based biodiesel, but the results of the microalgae chain are very uncertain due to the early development stage of the technology, and due to assumptions made concerning the electricity mix. On the other hand, the microalgae system has other advantages such as low competition on productive land and low biodiversity impacts. The findings help to recognise the main characteristics of the two production chains, and the main remaining research issues on bioenergy assessment along with the methodological development needs of life cycle approaches.; Le besoin croissant d'utiliser des matières premières de biocarburants qui n'entrent pas en compétition avec la nourriture et les aliments ont entraîné un intérêt croissant pour les matériaux lignocellulosiques et microalgues. Cependant, le cycle de vie des avantages environnementaux des biocarburants deux ont été interrogés. Le but de cette étude était d'évaluer comment la durabilité environnementale de biodiesel à base de forêt et microalgues peut être estimée en utilisant le cadre d'évaluation du cycle de vie. Ces filières de biocarburants ont été choisis parce qu'ils sont les systèmes contrastés, que le premier est basé sur un système de production de matières premières "naturel", tandis que le second est un système entièrement anthropique utilisant une infrastructure artificielle et des apports extérieurs pour se développer microalgues. Cette étude se concentre sur les catégories d'impacts du cycle de vie en cours de développement méthodologique, à savoir l'épuisement des ressources, l'utilisation des terres et les changements d'utilisation des terres, l'utilisation de l'eau, les impacts sur la qualité des sols et de la biodiversité. En outre, les effets du changement climatique ont été quantifiés de manière à illustrer l'incertitude des résultats et de la complexité de l'estimation des paramètres. Cette étude démontre la difficulté d'évaluer la portée absolue des impacts environnementaux globaux des deux systèmes. Les résultats suggèrent que les émissions de GES de biodiesel de microalgues sont plus élevés que ceux du biodiesel à base de résidus forestiers, mais les résultats de la chaîne de microalgues sont très incertaines en raison du stade de développement précoce de la technologie, et en raison des hypothèses retenues concernant l'électricité mélanger. D'autre part, le système de microalgues a d'autres avantages tels que la faible concurrence sur les terres productives et son faible impact sur la biodiversité. Les résultats aident à reconnaître les principales caractéristiques des deux chaînes de production, et les principales questions de recherche restant sur l'évaluation de la bioénergie ainsi que les besoins de développement méthodologique des approches du cycle de vie.

Published
2013

8. Growth dependence of conjugation explains limited plasmid invasion in biofilms: an individual-based modelling study

Author

Barth F. Smets, Jan-Ulrich Kreft, Brian Merkey, Jose Miguel Seoane, and Laurent Lardon

Subjects
0303 health sciences, 030306 microbiology, In silico, Biofilm, biochemical phenomena, metabolism, and nutrition, Biology, Bacterial growth, Microbiology, Pilus, Cell biology, 03 medical and health sciences, Individual based, Plasmid, Growth rate, Ecology, Evolution, Behavior and Systematics, 030304 developmental biology, Microbial Biofilms
Abstract

Summary Plasmid invasion in biofilms is often surprisingly limited in spite of the close contact of cells in a biofilm. We hypothesized that this poor plasmid spread into deeper biofilm layers is caused by a dependence of conjugation on the growth rate (rela- tive to the maximum growth rate) of the donor. By extending an individual-based model of microbial growth and interactions to include the dynamics of plasmid carriage and transfer by individual cells, we were able to conduct in silico tests of this and other hypotheses on the dynamics of conjugal plasmid transfer in biofilms. For a generic model plasmid, we find that invasion of a resident biofilm is indeed limited when plasmid transfer depends on growth, but not so in the absence of growth dependence. Using sensitivity analysis we also find that para- meters related to timing (i.e. a lag before the transconjugant can transfer, transfer proficiency and scan speed) and spatial reach (EPS yield, conjugal pilus length) are more important for successful plasmid invasion than the recipients' growth rate or the probability of segregational loss. While this study identifies one factor that can limit plasmid invasion in biofilms, the new individual-based frame- work introduced in this work is a powerful tool that enables one to test additional hypotheses on the spread and role of plasmids in microbial biofilms.

Published
2011

9. iDynoMiCS: next-generation individual-based modelling of biofilms

Author

Jan-Ulrich Kreft, Cristian Picioreanu, Barth F. Smets, Brian Merkey, Laurent Lardon, Andreas Dötsch, and Sónia Martins

Subjects
0303 health sciences, 030306 microbiology, business.industry, Ecology, Lag, Denitrification pathway, Biofilm, Response time, Chemostat, Modular design, Biology, Microbiology, 03 medical and health sciences, Denitrifying bacteria, Extracellular polymeric substance, Biochemical engineering, business, Ecology, Evolution, Behavior and Systematics, 030304 developmental biology
Abstract

Summary Individual-based modelling of biofilms accounts for the fact that individual organisms of the same species may well be in a different physiological state as a result of environmental gradients, lag times in responding to change, or noise in gene expression, which we have become increasingly aware of with the advent of single-cell microbiology. But progress in developing and using individual-based modelling has been hampered by different groups writing their own code and the lack of an available standard model. We therefore set out to merge most features of previous models and incorporate various improvements in order to provide a common basis for further developments. Four improvements stand out: the biofilm pressure field allows for shrinking or consolidating biofilms; the continuous-in-time extracellular polymeric substances excretion leads to more realistic fluid behaviour of the extracellular matrix, avoiding artefacts; the stochastic chemostat mode allows comparison of spatially uniform and heterogeneous systems; and the separation of growth kinetics from the individual cell allows condition-dependent switching of metabolism. As an illustration of the model's use, we used the latter feature to study how environmentally fluctuating oxygen availability affects the diversity and composition of a community of denitrifying bacteria that induce the denitrification pathway under anoxic or low oxygen conditions. We tested the hypothesis that the existence of these diverse strategies of denitrification can be explained solely by assuming that faster response incurs higher costs. We found that if the ability to switch metabolic pathways quickly incurs no costs the fastest responder is always the best. However, if there is a trade-off where faster switching incurs higher costs, then there is a strategy with optimal response time for any frequency of environmental fluctuations, suggesting that different types of denitrifying strategies win in different environments. In a single environment, biodiversity of denitrifiers is higher in biofilms than chemostats, higher with than without costs and higher at intermediate frequency of change. The highly modular nature of the new computational model made this case study straightforward to implement, and reflects the sort of novel studies that can easily be executed with the new model.

Published
2011

10. Life-cycle assessment of microalgae culture coupled to biogas production

Author

Monique Ras, Jean-Philippe Steyer, Laurent Lardon, Romy-Alice Goy, Arnaud Hélias, Pierre Collet, 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), Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Naskeo [Narbonne], Naskeo Environnement, Biological control of artificial ecosystems (BIOCORE), Institut National de la Recherche Agronomique (INRA)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'océanographie de Villefranche (LOV), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Laboratoire d'océanographie de Villefranche (LOV), Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA), 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), Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Observatoire océanologique de Villefranche-sur-mer (OOVM), and Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Conservation of Natural Resources, Energy-Generating Resources, Environmental Engineering, [SDV]Life Sciences [q-bio], 020209 energy, Chlorella vulgaris, Bioengineering, 02 engineering and technology, Environment, 010501 environmental sciences, Biology, 7. Clean energy, 01 natural sciences, 12. Responsible consumption, [SPI]Engineering Sciences [physics], [MATH.MATH-GM]Mathematics [math]/General Mathematics [math.GM], Electricity, Biogas, Bioenergy, [INFO.INFO-AU]Computer Science [cs]/Automatic Control Engineering, 0202 electrical engineering, electronic engineering, information engineering, Animals, Anaerobiosis, Waste Management and Disposal, 0105 earth and related environmental sciences, Life Cycle Stages, Biodiesel, Ethanol, Waste management, Renewable Energy, Sustainability and the Environment, business.industry, microalgae, AD, General Medicine, Carbon Dioxide, Lipids, 6. Clean water, Renewable energy, Anaerobic digestion, Algae fuel, Food, 13. Climate action, Biofuel, Biofuels, Fermentation, Gases, business, Methane
Abstract

International audience; Due to resource depletion and climate change, lipid-based algal biofuel has been pointed out as an interesting alternative because of the high productivity of algae per hectare and per year and its ability to recycle CO2 from flue gas. Another option for taking advantage of the energy content of the microalgae is to directly carry out anaerobic digestion of raw algae in order to produce methane and recycle nutrients (N, P and K). In this study, a life-cycle assessment (LCA) of biogas production from the microalgae Chlorella vulgaris is performed and the results are compared to algal biodiesel and to first generation biodiesels. These results suggest that the impacts generated by the production of methane from microalgae are strongly correlated with the electric consumption. Progresses can be achieved by decreasing the mixing costs and circulation between different production steps, or by improving the efficiency of the anaerobic process under controlled conditions. This new bioenergy generating process strongly competes with others biofuel productions.

Published
2011

11. Inclusion of the variability of diffuse pollutions in LCA for agriculture: the case of slurry application techniques

Author

Claudine Basset-Mens, Brigitte Langevin, Laurent Lardon, Information – Technologies – Analyse Environnementale – Procédés Agricoles (UMR ITAP), 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), 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), Fonctionnement agroécologique et performances des systèmes de cultures horticoles (UPR HORTSYS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), 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 la Recherche Agronomique (INRA), Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF), and Institut National de la Recherche Agronomique (INRA)

Subjects
[SPI.OTHER]Engineering Sciences [physics]/Other, Strategy and Management, ANALYSE DU CYCLE DE VIE, Monte Carlo method, Climate change, UNCERTAINTY, 010501 environmental sciences, 01 natural sciences, Industrial and Manufacturing Engineering, SLURRY APPLICATION TECHNIQUE, RELATIVE NITROGEN LOSS FACTORS, Robustness (computer science), Range (statistics), Life-cycle assessment, 0105 earth and related environmental sciences, General Environmental Science, LIFE CYCLE ASSESSMENT, Renewable Energy, Sustainability and the Environment, L01 - Élevage - Considérations générales, Global warming, Impact sur l'environnement, Environmental engineering, 04 agricultural and veterinary sciences, Lisier, FIELD EMISSIONS, Variable (computer science), 13. Climate action, [SDE]Environmental Sciences, 040103 agronomy & agriculture, Slurry, 0401 agriculture, forestry, and fisheries, Environmental science, U30 - Méthodes de recherche, P02 - Pollution
Abstract

LCAs (life cycle assessments) are often based on average data to produce a generic evaluation of a good or service. However, ignoring variability and induced uncertainty of LCA results reduces their significance, especially when dealing with agricultural processes that present high natural fluctuations. The objective of the study was to explore the robustness of LCA results when accounting for variable emissions data, illustrated by the case of slurry application techniques. Four application techniques were compared: band spreading, broadcast spreading, harrowing after surface application and direct injection. On the basis of the normalisation results, acidification, eutrophication and global warming potentials were selected. To estimate field nitrogen emissions, an original approach was developed based on relative nitrogen loss factors for each technique from a literature review. The calculated field emissions from different soil and climate conditions were considered equally probable and were propagated into a range of LCA result using the Monte Carlo method. Injection and harrowing both showed reduced acidification and eutrophication potentials compared to band spreading and broadcast spreading but had larger global warming potentials, which could be particularly important with injection. Harrowing consequently appeared as the best compromise. Despite the large range of LCA results, robust conclusions could be drawn. To achieve a more refined comparison between the techniques, the use of process-based models in contrasted situations is suggested.

Published
2010

12. Life-Cycle Assessment of Biodiesel Production from Microalgae

Author

Laurent Lardon, Olivier Bernard, Arnaud Hélias, Jean-Philippe Steyer, Bruno Sialve, 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), 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), Modeling and control of renewable resources (COMORE), Laboratoire d'océanographie de Villefranche (LOV), Observatoire océanologique de Villefranche-sur-mer (OOVM), Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Observatoire océanologique de Villefranche-sur-mer (OOVM), Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire océanologique de Villefranche-sur-mer (OOVM), and Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM)

Subjects
Energy-Generating Resources, Engineering, [SDV]Life Sciences [q-bio], 020209 energy, Biomass, 02 engineering and technology, 010501 environmental sciences, complex mixtures, 01 natural sciences, 7. Clean energy, 12. Responsible consumption, Diesel fuel, 0202 electrical engineering, electronic engineering, information engineering, Environmental Chemistry, Production (economics), Life-cycle assessment, 0105 earth and related environmental sciences, Biodiesel, Waste management, business.industry, General Chemistry, 13. Climate action, Biofuel, Biodiesel production, [SDE]Environmental Sciences, Chlorella vulgaris, business, Energy source, Gasoline, Biotechnology
Abstract

International audience; This paper provides an analysis of the potential environmental impacts of biodiesel production from microalgae. High production yields of microalgae have called forth interest of economic and scientific actors but it is still unclear whether the production of biodiesel is environmentally interesting and which transformation steps need further adjustment and optimization. A comparative LCA study of a virtual facility has been undertaken to assess the energetic balance and the potential environmental impacts of the whole process chain, from the biomass production to the biodiesel combustion. Two different culture conditions, nominal fertilizing or nitrogen starvation, as well as two different extraction options, dry or wet extraction, have been tested. The best scenario has been compared to first generation biodiesel and oil diesel. The outcome confirms the potential of microalgae as an energy source but highlights the imperative necessity of decreasing the energy and fertilizer consumption. Therefore control of nitrogen stress during the culture and optimization of wet extraction seem to be valuable options. This study also emphasizes the potential of anaerobic digestion of oilcakes as a way to reduce external energy demand and to recycle a part of the mineral fertilizers.

Published
2009

13. Recommendations for Life Cycle Assessment of algal fuels

Author

Pierre Collet, Laurent Lardon, Jean-Philippe Steyer, Olivier Bernard, Arnaud Hélias, Université Nice Sophia Antipolis (... - 2019) (UNS), Université Côte d'Azur (UCA), 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), Pôle ELSA, Environmental Life Cycle and Sustainability Assessment (ELSA), Biological control of artificial ecosystems (BIOCORE), Institut National de la Recherche Agronomique (INRA)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'océanographie de Villefranche (LOV), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'océanographie de Villefranche (LOV), ANR-13-BIME-0004,PURPLE SUN,Partage de photons entre panneaux photovoltaïques et microalgues à vocation énergétique(2013), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Purple Sun ANR-13-BIME-004, 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), Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Observatoire océanologique de Villefranche-sur-mer (OOVM), and Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[SDV]Life Sciences [q-bio], review, Management, Monitoring, Policy and Law, 7. Clean energy, 12. Responsible consumption, [SPI]Engineering Sciences [physics], [INFO.INFO-AU]Computer Science [cs]/Automatic Control Engineering, 11. Sustainability, Production (economics), Land use, land-use change and forestry, Environmental impact assessment, [MATH]Mathematics [math], Unit process, Life-cycle assessment, Scope (project management), business.industry, Mechanical Engineering, microalgae, Environmental engineering, Building and Construction, 15. Life on land, Environmental economics, Life Cycle Assessment (LCA), 6. Clean water, Renewable energy, General Energy, 13. Climate action, Greenhouse gas, recommendations, Environmental science, biofuel, business
Abstract

International audience; Many studies have used the Life Cycle Assessment (LCA) methodology to assess the environmental impacts and energetic suitability of microalgal biofuels. This paper presents a critical review focused on goal and scope, system boundaries, functional unit, Life Cycle Inventories (LCI) and environmental impacts of 41 LCA of algal biofuels. The comparison between these LCA has been made difficult by the heterogeneity of their underlying hypotheses and perimeters. Hence we propose to define methodological guidelines to harmonize results presentation in order to improve the validity of each new contribution and to ease its comparison to other studies. LCA allows detecting pollution transfers between production stages as well as between distinct environmental impacts. At the Life Cycle Inventory (LCI) level, a special attention should be paid to the perimeter of the study (e.g. inclusion of infrastructures) and to the handling of the co-products (allocation or substitution). Moreover the inventory data have to be treated in a consistent way in order to guarantee the comparability of LCI between different studies. Hence we recommend that data of all the production steps should be given at a unit process level, i.e. the smallest element for which input and output data can be quantified. At the Life Cycle Impact Assessment level, other impacts than the greenhouse gases balance have to be taken into account, like impacts related to the use of fertilizers (acidification and eutrophication) and phytosanitary products (human toxicity and ecotoxicity), impacts of direct and indirect land use change, and water consumptions. Finally, as biofuel is aiming at replacing existing energy productions, an energy balance should always be carried out; the Cumulative Energy Demand Ratio offers a convenient framework in that regard.

Published
2015

14. Simulation of field NH3 and N2O emissions from slurry spreading

Author

Laurent Lardon, Brigitte Langevin, Claudine Basset-Mens, and Sophie Genermont

Subjects
Tractor, émission atmosphérique, business.product_category, Field emissions, Spreading techniques, [SDV]Life Sciences [q-bio], 010501 environmental sciences, 01 natural sciences, Soil compaction (agriculture), slurry application, spreading techniques, modeling, ammonia, nitrous oxide, field emissions, media_common, 2. Zero hunger, Ammoniac, Nitrous oxide, U10 - Informatique, mathématiques et statistiques, 04 agricultural and veterinary sciences, Gaz à effet de serre, P02 - Pollution, Pollution, Environmental Engineering, Field (physics), media_common.quotation_subject, Slurry application, Pollution par l'agriculture, Pedotransfer function, Ammonia, Modélisation environnementale, 0105 earth and related environmental sciences, L01 - Élevage - Considérations générales, Environmental engineering, Modeling, Évaluation de l'impact, Impact sur l'environnement, Q70 - Traitement des déchets agricoles, Modèle de simulation, Lisier, Fumier, 13. Climate action, Greenhouse gas, Surface spreading, 040103 agronomy & agriculture, Slurry, 0401 agriculture, forestry, and fisheries, Environmental science, Dioxyde d'azote, business, Agronomy and Crop Science, F04 - Fertilisation
Abstract

International audience; Land application of manures and slurries are a major source of pollution such as water contamination by nitrates and greenhouse gas emissions. NH3 and N2O emissions can be lowered by suitable spreading techniques. However, a comprehensive review of the impact of slurry spreading techniques is lacking. For that we developed a model, named OSEEP, that simulates the effect of slurry incorporation, slurry spatial distribution, and soil compaction on NH3 and N2O emissions. OSEEP integrates a soil compaction model, a hydraulic pedotransfer function, a NH3 volatilization model, and a crop model. We ran OSEEP for five sites in France for 7 years. Four techniques were simulated: broadcast spreading, band spreading, incorporation after surface spreading, and injection. We tested various sizes of slurry tankers and tractors. We calculated NH3 and N2O relative emissions from various spreading techniques with respect to band spreading. Results show good agreement between model calculation and published field data. We found that slurry applied by a self-propelled 15-m3 tanker with extra large tires led to an increase of 20 % in N2O emission, by comparison with a 15-m3 tanker trailed by a tractor. Hence, we show that soil compaction should be taken into account to optimize trade-offs between NH3 and N2O emissions.

Published
2015

15. On-line diagnosis and uncertainty management using evidence theory––experimental illustration to anaerobic digestion processes

Author

Ana Punal, Laurent Lardon, and Jean-Philippe Steyer

Subjects
Engineering, Process state, Process (engineering), business.industry, Context (language use), Modularity, Fuzzy logic, Industrial and Manufacturing Engineering, Fault detection and isolation, Computer Science Applications, Variety (cybernetics), Software, Control and Systems Engineering, Modeling and Simulation, Forensic engineering, Biochemical engineering, business
Abstract

The on-line diagnosis is a key requirement in biological processes. This is particularly true in the case of wastewater treatment processes due to the composition of media, the requirements of operating conditions and the wide variety of possible disturbances that necessitate careful and constant monitoring of the processes. Moreover, because only partial information is available in an online context and because of the technical and biological complexities of the involved processes, specific characteristics are required for diagnosis purposes. Several approaches like quantitative model based, qualitative model based and process history based methods were applied over the years. This paper present a methodological framework based on evidence theory to manage the fault signals generated by conventional approaches (i.e., residuals from hardware and software redundancies, fuzzy logic based modules for process state assessment) and to account for uncertainty. The advantages of using evidence theory like modularity, detection of conflict and doubt in the information sources are illustrated with experimental results from a 1 m 3 fixed bed anaerobic digestion process used for the treatment of industrial distillery wastewater.

Published
2004

16. How to take time into account in the inventory step: a selective introduction based on sensitivity analysis

Author

Jean-Philippe Steyer, Pierre Collet, Laurent Lardon, Arnaud Hélias, 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), 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), ANR-08-BIOE-11, ANR-08-BIOE-0011,SYMBIOSE,Etude et Optimisation du Couplage MicroAlgue-Bactérie Anaérobie pour la Production d'Energie par voie biologique à partir de biomasse primaire et de déchets organiques(2008), and Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)

Subjects
timescale, Engineering, Operations research, Process (engineering), 020209 energy, [SDV]Life Sciences [q-bio], Climate change, 02 engineering and technology, Variation (game tree), 010501 environmental sciences, 01 natural sciences, sensitivity analysis, dynamic LCA, inventaire, 0202 electrical engineering, electronic engineering, information engineering, calendar, Production (economics), Relevance (information retrieval), life cycle impact assessment, Sensitivity (control systems), Representation (mathematics), Life-cycle assessment, 0105 earth and related environmental sciences, General Environmental Science, analyse du cycle de vie, business.industry, calendrier, Industrial engineering, life cycle inventory, inventory, 13. Climate action, perturbation analysis, business, analyse de sensibilité
Abstract

Life cycle assessment is usually an assessment tool, which only considers steady-state processes, as the temporal and spatial dimensions are lost during the life cycle inventory (LCI). This approach therefore reduces the environmental relevance of certain results, as it has been underlined in the case of climate change studies. Given that the development of dynamic impact methods is based on dynamic inventory data, it seems essential to develop a general methodology to achieve a temporal LCI. This study presents a method for selecting the steps, within the whole process network, for which dynamics need to be considered while others can be approximated by steady-state representation. The selection procedure is based on the sensitivity of the impacts on the variation of environmental and economic flows. Once these flows have been identified, their respective timescales are compared to the inherent timescales of the impact categories affected by the flows. The timescales of the impacts are divided into three categories (days, months, years) based on a literature review of the ReCiPe method. The introduction of a temporal dynamic depends on the relationship between the timescale of the environmental and economic flows on the one hand and that of the concerned impact on the other hand. This approach is illustrated by the life cycle assessment of palm methyl ester and ethanol from sugarcane. In both cases, the introduction of a temporal dynamic is limited to a small proportion of the total number of flows: 0.1 % in the sugarcane ethanol production and 0.01 % in the palm methyl ester production. Future developments of time integration in the LCI and in the life cycle impact assessment (LCIA) are also discussed in order to deal with the need of characterization functions and the recurrent problem of waiting times. This work provides a method to select specific flows where the introduction of temporal dynamics is most relevant. It is based on sensitivity analyses and on the relationship between the timescales of the flows and the timescale of the involved impact. The time-distributed LCI generated by using this approach could then be coupled with a dynamic LCIA proposed in the literature.

Published
2014

17. Contributors

Author

F.G. Acién, Helena M. Amaro, Ricardo Assmann, Bhavya Balagurumurthy, Olivier Bernard, Thallada Bhaskar, Rashmi Chandra, Jo-Shu Chang, Chun-Yen Chen, Feng Chen, Pierre Collet, Jorge Alberto Vieira Costa, Carlos José DalmasNeto, Julio Cesar de Carvalho, Michele Greque de Morais, M. Prathima Devi, Su-Chiung Fang, J.M. Fernández, A. Catarina Guedes, Arnaud Hélias, I-Chen Hu, Man Kee Lam, Laurent Lardon, Duu-Jong Lee, Keat Teong Lee, Jin Liu, F. Xavier Malcata, S. Venkata Mohan, E. Molina-Grima, DolivarCoraucci Neto, Alessandra Cristine Novak, Kuan-Yeow Show, Rawel Singh, Carlos Ricardo Soccol, Isabel Sousa-Pinto, Daniele Spinelli, Jean-Philippe Steyer, G. Venkata Subhash, Zheng Sun, Eduardo Bittencourt Sydney, and Hong-Wei Yen

Published
2014

18. Biodiesel from microalgae – Life cycle assessment and recommendations for potential improvements

Author

Olivier Bernard, Laurent Lardon, Isabelle Lombaert-Valot, Arnaud Hélias, Stéphanie Bricout, Olivier Lépine, Béatrice Perrier, Pierre Collet, Jean-Philippe Steyer, 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), Biological control of artificial ecosystems (BIOCORE), Institut National de la Recherche Agronomique (INRA)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'océanographie de Villefranche (LOV), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), European Aeronautic Defence and Space Company (EADS), PSA Peugeot Citroën (PSA), Alpha Biotech, ANR-06-BIOE-014, ANR-06-BIOE-0014,SHAMASH,Production d'un biocarburant lipidique par des micro-algues(2006), ANR-13-BIME-0004,PURPLE SUN,Partage de photons entre panneaux photovoltaïques et microalgues à vocation énergétique(2013), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Laboratoire d'océanographie de Villefranche (LOV), Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA), 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), Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Observatoire océanologique de Villefranche-sur-mer (OOVM), and Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Engineering, Renewable Energy, Sustainability and the Environment, Natural resource economics, business.industry, microalgae, [SDV]Life Sciences [q-bio], Environmental engineering, Biomass, biodiesel, 7. Clean energy, renewable energy, 12. Responsible consumption, Renewable energy, [SPI]Engineering Sciences [physics], life cycle assessment, 13. Climate action, Biofuel, Greenhouse gas, [INFO.INFO-AU]Computer Science [cs]/Automatic Control Engineering, Electricity, [MATH]Mathematics [math], business, Life-cycle assessment, Productivity, Renewable resource
Abstract

International audience; Microalgae are considered as one of the potential major source of biofuel for the future. However, their environmental benefit is still unclear and many scientific publications provide contradictory results. Here we perform the Life Cycle Assessment of the production and combustion of 1 MJ of algal methylester. The system under consideration uses standard open raceways under greenhouses. Lipid extraction and transesterification are carried out on a humid paste produced by centrifugation. Our environmental and energetic analysis shows that improving the energy balance is clearly the key priority to make microalgal cultivation sustainable and to reduce its greenhouse gas (GHG) emissions. To achieve significant reduction of the GHG emissions, most of the studies of the literature focus on technological breakthroughs, especially at the production step. However, since a large fraction of environmental impacts and especially GHG emissions do not occur directly at the production facility but stem from the production of the electricity required for producing, harvesting and transforming algae, it seems relevant to question the source of electricity as well as algae production technology. We consider a scenario where up to 45% of electricity was produced by a local renewable source and then we compare it to the improvements resulting from technological breakthroughs resulting in higher microalgal productivity or biomass concentration. It turns out that increasing the yield only drastically reduces the climate change for low starting productivity. The climate change is always significantly reduced by the use of local renewable electricity. It is therefore wiser to increase biomass productivity to easily achievable values (10–15 gm−2 d−1), and then radically change improvements pathways by considering the composition of the electricity mix used for example. At least, it must be underlined that the introduction of renewable electricity also affect energetic efficiency, leading to a positive cumulative energy balance due to better energetic ratios.

Published
2014

19. What Scientific Issues in Life Cycle Assessment Applied to Waste and Biomass Valorization? Editorial

Author

Lynda Aissani, Eva Risch, Véronique Bellon-Maurel, Guillaume Junqua, Philippe Roux, Laurent Lardon, Eléonore Loiseau, Cécile Bessou, Centre de Montpellier [IRSTEA], Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Gestion environnementale et traitement biologique des déchets (UR GERE), Performance des systèmes de culture des plantes pérennes (Cirad-Persyst-UPR 34 Système de pérennes), Département Performances des systèmes de production et de transformation tropicaux (Cirad-PERSYST), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), 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), Laboratoire de Génie de l'Environnement Industriel (LGEI), IMT - MINES ALES (IMT - MINES ALES), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Languedoc Roussillon region, Interreg Sudoe, ELSA Group, EcoSD network, Performance des systèmes de culture des plantes pérennes (UPR Système de pérennes), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), 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), Pôle ELSA, Environmental Life Cycle and Sustainability Assessment (ELSA), Université européenne de Bretagne - European University of Brittany (UEB), 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), AgroParisTech, and Interreg Sudoe (Ecotech-Sudoe project)

Subjects
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Engineering, Environmental Engineering, Complex system, [SDV]Life Sciences [q-bio], 020209 energy, Allocation, Decision, Biomass, 02 engineering and technology, 010501 environmental sciences, Life Cycle Assessment, 01 natural sciences, Field (computer science), 12. Responsible consumption, Unit (housing), 0202 electrical engineering, electronic engineering, information engineering, Production (economics), Waste Management and Disposal, Life-cycle assessment, 0105 earth and related environmental sciences, Scope (project management), Waste management, Renewable Energy, Sustainability and the Environment, business.industry, Impact assessment, [SDE.IE]Environmental Sciences/Environmental Engineering, LCA, Q70 - Traitement des déchets agricoles, Spatialization, Risk analysis (engineering), 13. Climate action, Waste, P01 - Conservation de la nature et ressources foncières, business
Abstract

International audience; Whereas Life Cycle Assessment (LCA) is more and more used for assessing the environmental load of waste management systems and of biomass production and valorization systems, various scientific issues are still to be dealt with. The purpose of this paper is to enlighten these scientific issues and to describe the current attempt to overcome them. The method used has been to go through the steps of the LCA standardized framework (ISO 14040) and to outline at each step the points that could be improved and still deserve research efforts. The various identified issues are: in step 1 (goal and scope), the choice of attributional/consequential modelling, the difficult choice of the functional unit due to the highly multi-functional nature of such systems, the allocation choices and the need for spatial differentiation; in step 2 (inventory), the thorny issue of modelling such complex systems and properly estimating field emissions; in step 3 (impact assessment), the lack of appropriate impacts (such as odours) in current LCA impact categories; in step 4 (interpretation and use), research efforts are needed to understand and facilitate the way actors take over and use LCA multi-criteria results. A transversal issue, i.e. uncertainty characterization and reduction, is also analyzed. These various scientific bottlenecks are currently under study; some are handled by this "Waste and Biomass Valorization" special topic, which includes a selection of papers presented in 2011 at the Ecotech&Tools conference (Montpellier, France).

Published
2012

20. Micro-scale spatial expansion of microbial cells and mobile genetic elements

Author

Barth F. Smets, Jan-Ulrich Kreft, Dani Or, Arnaud Dechesne, Gamze Gulez, Laurent Lardon, Brian Merkey, Jose Seoane, and Gang Wang

Subjects
fungi, food and beverages
Abstract

Microbes can actively explore their local spatial environment when sufficiently hydrated pathways are present - mobile gene elements can also travel in local environments when cellular density is sufficient. In this presentation, I will present our efforts at predicting the dynamics of these two processes, and how they are affected by physical and biological constraints, using spatially-explicit agent-based models.

Published
2012

21. L'application de l'analyse de cycle de vie (ACV) aux systèmes biotechniques complexes : quels fronts de science ?

Author

Eva Risch, Catherine Macombe, Eléonore Loiseau, Guillaume Junqua, Philippe Roux, Véronique Bellon-Maurel, Laurent Lardon, Cécile Bessou, Information – Technologies – Analyse Environnementale – Procédés Agricoles (UMR ITAP), 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), 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), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Laboratoire de Génie de l'Environnement Industriel (LGEI), IMT - MINES ALES (IMT - MINES ALES), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), 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), Pôle ELSA, Environmental Life Cycle and Sustainability Assessment (ELSA), and ProdInra, Migration

Subjects
[SDE] Environmental Sciences, Analyse du cycle de vie, Méthodologie, 020209 energy, [SDV]Life Sciences [q-bio], Recherche, 02 engineering and technology, 010501 environmental sciences, 01 natural sciences, Réseau de recherche, Sciences sociales, 0202 electrical engineering, electronic engineering, information engineering, Modélisation environnementale, 0105 earth and related environmental sciences, Méthode statistique, 000 - Autres thèmes, Évaluation de l'impact, Impact sur l'environnement, General Medicine, [SDV] Life Sciences [q-bio], Cycle de développement, [SDE]Environmental Sciences, P01 - Conservation de la nature et ressources foncières, U30 - Méthodes de recherche
Abstract

Actes du colloque organisé par l'AFITE, 2011/11/17; National audience; La dernière décennie a vu l’analyse du cycle de vie (ACV) s’imposer comme cadre méthodologique de référence pour l’évaluation quantitative des impacts environnementaux d’un produit ou d’un système de production. Bien que déjà largement utilisés dans les sphères économiques (éco-conception, responsabilité sociale des entreprises) et politiques (éco-étiquetages), le cadre méthodologique et les outils de l’ACV sont toujours un vaste objet d’étude pour la communauté scientifique. Cet article décrit un grand nombre de fronts de sciences de cette communauté et la manière dont le pôle de recherche français Elsa les aborde.

Published
2012

22. Three-reaction model for the anaerobic digestion of microalgae

Author

Jean-Philippe Steyer, Francis Mairet, Laurent Lardon, Monique Ras, Olivier Bernard, Benoît Chachuat, Elliot Cameron, Biological control of artificial ecosystems (BIOCORE), Laboratoire d'océanographie de Villefranche (LOV), Observatoire océanologique de Villefranche-sur-mer (OOVM), Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Observatoire océanologique de Villefranche-sur-mer (OOVM), Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA), Anlysis and Mathematical Modeling, Valparaiso, Departamento de Matematica [Valparaiso], Universidad Tecnica Federico Santa Maria [Valparaiso] (UTFSM)-Universidad Tecnica Federico Santa Maria [Valparaiso] (UTFSM), Department of Chemical Engineering [Hamilton, Ontario], McMaster University [Hamilton, Ontario], 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 la Recherche Agronomique (INRA), Imperial College London, Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut National de Recherche en Informatique et en Automatique (Inria), Universidad Tecnica Federico Santa Maria [Valparaiso] (UTFSM), Mc Master University, 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), Fondo Basal, Centro de Modelamiento Matematico - U. de Chile, NSERC, OGS, Institut National de la Recherche Agronomique (INRA)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'océanographie de Villefranche (LOV), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), and Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)

Subjects
0106 biological sciences, Microorganism, MASS-TRANSFER, [SDV]Life Sciences [q-bio], 02 engineering and technology, 7. Clean energy, 01 natural sciences, Applied Microbiology and Biotechnology, ENERGY, [SPI]Engineering Sciences [physics], Bioreactors, [INFO.INFO-AU]Computer Science [cs]/Automatic Control Engineering, 0202 electrical engineering, electronic engineering, information engineering, Microalgae, MICROORGANISMS, Anaerobiosis, Hydrolysis, BIOTECHNOLOGY & APPLIED MICROBIOLOGY, Hydrogen-Ion Concentration, Pulp and paper industry, Biofuel, [SDE]Environmental Sciences, Chlorella vulgaris, Anaerobic exercise, Life Sciences & Biomedicine, Methane, Biotechnology, Methanogenesis, Contois kinetics, 020209 energy, Bioengineering, Biology, dynamic model, Models, Biological, Bacteria, Anaerobic, [MATH.MATH-GM]Mathematics [math]/General Mathematics [math.GM], Bioenergy, Ammonia, 010608 biotechnology, Anaerobic digestion, MD Multidisciplinary, Bioreactor, Computer Simulation, ammonia inhibition, SLUDGE, Science & Technology, business.industry, Kinetics, Biofuels, business
Abstract

Coupling an anaerobic digester to a microalgal culture has received increasing attention as an alternative process for combined bioenergy production and depollution. In this article, a dynamic model for anaerobic digestion of microalgae is developed with the aim of improving the management of such a coupled system. This model describes the dynamics of inorganic nitrogen and volatile fatty acids since both can lead to inhibition and therefore process instability. Three reactions are considered: Two hydrolysis–acidogenesis steps in parallel for sugars/lipids and for proteins, followed by a methanogenesis step. The proposed model accurately reproduces experimental data for anaerobic digestion of the freshwater microalgae Chlorella vulgaris with an organic loading rate of 1 gCOD L−1 d−1. In particular, the three-reaction pathway allows to adequately represent the observed decoupling between biogas production and nitrogen release. The reduced complexity of this model makes it suitable for developing advanced, model-based control and monitoring strategies. Biotechnol. Bioeng. 2012; 109:415–425. © 2011 Wiley Periodicals, Inc.

Published
2011

23. A new dynamic model for bioavailability and cometabolism of micropollutants during anaerobic digestion

Author

Liliana Delgadillo-Mirquez, Dominique Patureau, Jean-Philippe Steyer, Laurent Lardon, 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 Ibague University and COLCIENCIAS (Administrative Department of Science, Technology and Innovation) - Colombia

Subjects
Environmental Engineering, 0207 environmental engineering, Biological Availability, Cometabolism, BIODEGRADATION, 02 engineering and technology, 010501 environmental sciences, SORPTION, 01 natural sciences, Models, Biological, Waste Disposal, Fluid, chemistry.chemical_compound, Bioreactors, PAHs, Anaerobiosis, Polycyclic Aromatic Hydrocarbons, [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology, 020701 environmental engineering, Waste Management and Disposal, 0105 earth and related environmental sciences, Water Science and Technology, Civil and Structural Engineering, Sewage, XENOBIOTIC, Chemistry, Ecological Modeling, Continuous reactor, METHANOGENIC CONDITIONS, TRAITEMENT DES EAUX USEES, Biodegradation, Pollution, 6. Clean water, Bioavailability, Anaerobic digestion, Biodegradation, Environmental, Wastewater, Environmental chemistry, Degradation (geology), Sewage treatment, Water Pollutants, Chemical
Abstract

International audience; Organic micropollutants (OMPs) are present in wastewater and sludge. Their possible impact to the environment contributes to their increasing scientific and social interest. Anaerobic digestion has been shown as a potential biological process for removal of these compounds. An accurate description of OMP distribution in the environmental system can be used to better understand which compartment is used for degradation and to improve their depletion in conventional wastewater treatment technologies. In this work, we proposed a dynamical model with a four-compartment distribution to describe the Polycyclic Aromatic Hydrocarbons (PAHs) fate during anaerobic digestion. The model is calibrated and validated using experimental data obtained from two continuous reactors fed with primary and secondary sludge operated under mesophilic conditions. A non-linear least square method was used to optimize the model parameters. The resulted model is in accordance with the experimental data. The PAH biodegradation rate is well modeled when considering the aqueous fraction (including free and sorbed to dissolved/colloidal matter PAHs) as the bioavailable compartment. It was also demonstrated in the simulations that the PAHs biodegradation is linked to a mechanism of cometabolism. The model proposed is potentially useful to better understand the micropollutant distribution, predict the fate of PAHs under anaerobic condition and help to optimize the operation process for their depletion.

Published
2011

24. Modeling anaerobic digestion of microalgae using ADM1

Author

Francis Mairet, Jean-Philippe Steyer, Laurent Lardon, Olivier Bernard, Monique Ras, Biological control of artificial ecosystems (BIOCORE), Laboratoire d'océanographie de Villefranche (LOV), Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA), Departamento de Matematica, Universidad Tecnica Federico Santa Maria [Valparaiso] (UTFSM), 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 la Recherche Agronomique (INRA), Institut National de la Recherche Agronomique (INRA)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'océanographie de Villefranche (LOV), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA), Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Observatoire océanologique de Villefranche-sur-mer (OOVM), Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-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 Gouzé, Jean-Luc

Subjects
[SDV.BIO]Life Sciences [q-bio]/Biotechnology, [SDV]Life Sciences [q-bio], ANAEROBIC DIGESTION, 02 engineering and technology, 010501 environmental sciences, 01 natural sciences, 7. Clean energy, Methane, chemistry.chemical_compound, [SPI]Engineering Sciences [physics], Bioreactors, [INFO.INFO-AU]Computer Science [cs]/Automatic Control Engineering, 0202 electrical engineering, electronic engineering, information engineering, Anaerobiosis, Waste Management and Disposal, Waste management, [MATH.MATH-GM] Mathematics [math]/General Mathematics [math.GM], General Medicine, Hydrogen-Ion Concentration, Lipids, HYDROLYSIS, 6. Clean water, Sustainable energy, [SDV] Life Sciences [q-bio], Waste treatment, Carbon dioxide, [INFO.INFO-AU] Computer Science [cs]/Automatic Control Engineering, Anaerobic exercise, Biotechnology, ADM1, Environmental Engineering, [SPI] Engineering Sciences [physics], Nitrogen, 020209 energy, ANAEROBIC DIGESTION, KINETIC MODEL, HYDROLYSIS, ADM1, Chlorella vulgaris, Carbohydrates, Bioengineering, CHLORELLA VULGARIS, Biology, Models, Biological, MICROALGAE, Digestion (alchemy), [MATH.MATH-GM]Mathematics [math]/General Mathematics [math.GM], Ammonia, KINETIC MODEL, 0105 earth and related environmental sciences, Biological Oxygen Demand Analysis, Renewable Energy, Sustainability and the Environment, Proteins, Fatty Acids, Volatile, Anaerobic digestion, Solubility, chemistry, 13. Climate action, MICROALGAE, CHLORELLA VULGARIS
Abstract

International audience; The coupling between a microalgal pond and an anaerobic digester is a promising alternative for sustainable energy production by transforming carbon dioxide into methane using solar energy. In this paper, we demonstrate the ability of the original ADM1 model and a modified version (based on Contois kinetics for the hydrolysis steps) to represent microalgae anaerobic digestion. Simulations were compared to experimental data of an anaerobic digester fed with Chlorella vulgaris. The modified ADM1 fits adequately the data for the considered 140 day experiment encompassing a variety of influent load and flow rates. It turns out to be a reliable predictive tool for optimising the coupling of microalgae with anaerobic digestion processes.

Published
2011

25. A Dynamic Model for Anaerobic Digestion of Microalgae

Author

Francis Mairet, Laurent Lardon, Jean-Philippe Steyer, Olivier Bernard, Monique Ras, Biological control of artificial ecosystems (BIOCORE), Institut National de la Recherche Agronomique (INRA)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'océanographie de Villefranche (LOV), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Anlysis and Mathematical Modeling, Valparaiso, Departamento de Matematica [Valparaiso], Universidad Tecnica Federico Santa Maria [Valparaiso] (UTFSM)-Universidad Tecnica Federico Santa Maria [Valparaiso] (UTFSM), 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), Laboratoire d'océanographie de Villefranche (LOV), Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Observatoire océanologique de Villefranche-sur-mer (OOVM), Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), 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), Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), and 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)

Subjects
0106 biological sciences, Waste management, Methanogenesis, [SDV]Life Sciences [q-bio], Chlorella vulgaris, 010501 environmental sciences, Biology, 7. Clean energy, 01 natural sciences, Methane, Anaerobic digestion, chemistry.chemical_compound, [SPI]Engineering Sciences [physics], Digestion (alchemy), [MATH.MATH-GM]Mathematics [math]/General Mathematics [math.GM], chemistry, Biofuel, 010608 biotechnology, [INFO.INFO-AU]Computer Science [cs]/Automatic Control Engineering, Bioprocess, Anaerobic exercise, 0105 earth and related environmental sciences
Abstract

International audience; The coupling between a microalgal pond and an anaerobic digester is a promising alternative for sustainable energy production by transforming carbon dioxide into methane (which is a biofuel). In this paper, a dynamic model for anaerobic digestion of microalgae is developed with the objective of helping in the coupled process management. This model includes the dynamics of ammonium and volatile fatty acids since both can lead to inhibition and process unstability. Three reactions are considered: two hydrolysis-acetogenesis steps in parallel for the sugars-lipids and for the proteins, and a methanogenesis step. Simulation results were compared with experimental data for Chlorella vulgaris digestion. The model fits the data of the considered 140 day experiment.

Published
2011

26. Experimental study on a coupled process of production and anaerobic digestion of Chlorella vulgaris

Author

Nicolas Bernet, Monique Ras, Sialve Bruno, Laurent Lardon, Jean-Philippe Steyer, 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 Naskeo Environnement

Subjects
Time Factors, Environmental Engineering, [SDV.BIO]Life Sciences [q-bio]/Biotechnology, 020209 energy, Chlorella vulgaris, ANAEROBIC DIGESTION, Chlorophyceae, Biomass, Bioengineering, 02 engineering and technology, Chlorophyta, CHLORELLA VULGARIS, 010501 environmental sciences, 01 natural sciences, 7. Clean energy, MICROALGAE, Bioreactors, Digestion (alchemy), Ammonia, 0202 electrical engineering, electronic engineering, information engineering, Bioreactor, Anaerobiosis, Waste Management and Disposal, 0105 earth and related environmental sciences, Waste management, biology, Renewable Energy, Sustainability and the Environment, METHANE PRODUCTION, Hydrolysis, NITROGEN MINERALISATION, Temperature, General Medicine, Hydrogen-Ion Concentration, Pulp and paper industry, biology.organism_classification, Oxygen, Anaerobic digestion, Calibration, Fermentation, Feasibility Studies, Methane, Water Pollutants, Chemical
Abstract

International audience; The main goal of this present study is to investigate the feasibility of coupling algae production (Chlorella vulgaris) to an anaerobic digestion unit. An intermediate settling device was integrated in order to adapt the feed-flow concentration and the flow rate. Digestion of C. vulgaris was studied under 16 and 28 days hydraulic retention times (HRT), with a corresponding organic loading rate of 1 g(COD) L-1. Increasing the HRT achieved 51% COD removal with a methane production measured at 240 mL g(VSS)(-1). Performing different HRTs and dynamic monitoring during degradation highlighted differential hydrolysis of microalgae compartments. However, 50% of the biomass did not undergo anaerobic digestion, even under long retention times. This points out the interest for further studies on pre-treatment performances and more generally speaking on the need for intensifying microalgae biomass digestion.

Published
2011

27. Time and Life Cycle Assessment: how to take time into account in the inventory step?

Author

Jean-Philippe Steyer, Pierre Collet, Laurent Lardon, Arnaud Hélias, 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 d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), 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), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and Matthias Finkbeiner (Editeur)

Subjects
Steady state (electronics), sustainable development, Operations research, sustainable management, Computer science, 020209 energy, [SDV]Life Sciences [q-bio], 02 engineering and technology, 010501 environmental sciences, environmental control, gestion durable, 01 natural sciences, Term (time), Tree (data structure), cycle de vie, Sustainable management, protection de l'environnement, développement durable, 0202 electrical engineering, electronic engineering, information engineering, Relevance (information retrieval), Representation (mathematics), Life-cycle assessment, Selection (genetic algorithm), 0105 earth and related environmental sciences
Abstract

Mention d'édition : 1st; Life cycle assessment is usually an assessment tool which only considers steady state processes: the temporal and spatial properties of extractions, usage and emissions are lost during the life cycle inventory step. This approach significantly reduces the environmental relevance of some results. As the development of dynamic impact methods is based on dynamic inventory data, it seems essential to develop a general methodology to achieve a temporal life cycle inventory. This study presents a method to select steps, in the whole network tree, for which dynamics have to be considered while the others are approximated by steady state representation. The selection procedure is based on the main contributors in term of impact. The approach is illustrated by the life cycle assessment of simplified rapeseed oil production as biofuel system.

Published
2011

28. Anaerobic Digestion of Microalgae: Identification for Optimization and Control

Author

Olivier Bernard, Elliot Cameron, Francis Mairet, Laurent Lardon, Monique Ras, Jean-Philippe Steyer, Benoît Chachuat, Department of Chemical Engineering [Hamilton, Ontario], McMaster University [Hamilton, Ontario], Biological control of artificial ecosystems (BIOCORE), Laboratoire d'océanographie de Villefranche (LOV), Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA), 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), International Federation of Automatic Control (IFAC). Laxenburg, AUT., Institut National de la Recherche Agronomique (INRA)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'océanographie de Villefranche (LOV), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA), Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Observatoire océanologique de Villefranche-sur-mer (OOVM), Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), and 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)

Subjects
0106 biological sciences, Engineering, coordination, [SDV]Life Sciences [q-bio], Chlorella vulgaris, 01 natural sciences, 7. Clean energy, parameter and state estimation, 03 medical and health sciences, [SPI]Engineering Sciences [physics], Digestion (alchemy), [MATH.MATH-GM]Mathematics [math]/General Mathematics [math.GM], Biogas, Bioenergy, 010608 biotechnology, [INFO.INFO-AU]Computer Science [cs]/Automatic Control Engineering, scheduling, Bioprocess, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, 0303 health sciences, Biodiesel, Waste management, business.industry, Renewable energy, Anaerobic digestion, modeling and identification, Biochemical engineering, business, optimization
Abstract

Coupling an anaerobic digester to a microalgal culture is currently considered one of the most promising avenues towards the production of renewable bioenergy, either in the form of biodiesel or biogas. Accurate mathematical models are crucial tools to assess the potential of such coupled biotechnological processes and help optimize their design, operation and control. This paper focuses on the compartment of anaerobic digestion of microalgae. Using experimental data for the anaerobic digestion of Chlorella vulgaris, a grey-box model is developed that allows good prediction capabilities and retains low complexity. The proposed methodology proceeds in two steps, namely a structural and a parametric identification steps. The fitted model is then used to conduct preliminary optimization for the production of biogas from Chlorella vulgaris. The results provide some insight into the potential for bioenergy production from the digestion of microalgae and, more generally, the coupled process.

Published
2011

29. L'utilisation de modèles pour prendre en compte la variabilité des données en agriculture

Author

Claudine Basset-Mens, Laurent Lardon, Brigitte Langevin, Information – Technologies – Analyse Environnementale – Procédés Agricoles (UMR ITAP), Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF), Pôle ELSA, 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), Institut National de la Recherche Agronomique (INRA), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), 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), 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), Solagro, 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 la Recherche Agronomique (INRA), Fonctionnement agroécologique et performances des systèmes de cultures horticoles (UPR HORTSYS), Matthias Finkbeiner (Editeur), and Irstea Publications, Migration

Subjects
[SDE] Environmental Sciences, Operations research, Recyclage des déchets, [SDV]Life Sciences [q-bio], Distribution (economics), 010501 environmental sciences, 01 natural sciences, Lead (geology), Range (statistics), Econometrics, sort, Point estimation, Modélisation environnementale, 0105 earth and related environmental sciences, Ammoniac, business.industry, U10 - Informatique, mathématiques et statistiques, Oxyde nitreux, Rank (computer programming), L01 - Élevage - Considérations générales, Impact sur l'environnement, Experimental data, Q70 - Traitement des déchets agricoles, 04 agricultural and veterinary sciences, Lisier, Pollution, Geography, 13. Climate action, [SDE]Environmental Sciences, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Probability distribution, P01 - Conservation de la nature et ressources foncières, business, Gaz à effet de serre
Abstract

LCA outputs are often presented as point estimates measuring potential impacts although average impacts values may be misleading to rank different options, especially in the case of agricultural products. In an LCA study comparing different slurry application techniques, NH3 and N2O emissions have been estimated through two approaches, experimental data collected from the literature and mathematical simulations over different soil and climate conditions. Both approaches lead to similar ranges of emissions; however the simulation-based approach allows us to construct a probability distribution of emissions whereas the limited number of experimental studies leads only to the definition of a range of emissions. A better knowledge of the variability of emissions helps the practitioner to sort alternatives and to detect situations where they are not discernable. Moreover the knowledge of the distribution and of its most impacting sources of variability leads to the definition of more informative and significant typologies., Sorties ACV sont souvent présentées comme des estimations ponctuelles de mesure des impacts potentiels, bien que les valeurs moyennes des impacts peuvent être trompeuses pour classer les différentes options, en particulier dans le cas des produits agricoles. Dans une étude ACV comparant les différentes techniques d'application du lisier, les émissions de NH3 et de N2O ont été estimés selon deux approches, les données expérimentales recueillies dans la littérature et des simulations mathématiques sur un sol différent et les conditions climatiques. Les deux approches conduisent à des plages d'émissions similaires, mais l'approche basée sur la simulation nous permet de construire une distribution de probabilité des émissions alors que le nombre limité d'études expérimentales ne mène qu'à la définition d'une gamme d'émissions. Une meilleure connaissance de la variabilité des émissions permet au praticien de tri des alternatives et de détecter les situations où ils ne sont pas discernables. Par ailleurs la connaissance de la distribution et de la plupart de ses sources impact de la variabilité conduit à la définition de typologies plus informatif et significatif.

Published
2011

30. A new extant respirometric assay to estimate intrinsic growth parameters applied to study plasmid metabolic burden

Author

Jose Miguel Seoane, Gürkan Sin, Laurent Lardon, Krist V. Gernaey, Barth F. Smets, Department of Environmental Engineering, Technical University of Denmark [Lyngby] (DTU), Department of Chemical and Biochemical Engineering (IVC-SEP), Laboratoire de Biotechnologie de l'Environnement [Narbonne] (LBE), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and 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)

Subjects
PARAMETER ESTIMATION, [SDV]Life Sciences [q-bio], ved/biology.organism_classification_rank.species, Bioengineering, Computational biology, Applied Microbiology and Biotechnology, Plasmid maintenance, 03 medical and health sciences, Respirometry, Plasmid, Bioreactors, Oxygen Consumption, Sensitivity (control systems), Model organism, 030304 developmental biology, 0303 health sciences, biology, IDENTIFIABILITY, 030306 microbiology, business.industry, ved/biology, Pseudomonas putida, Respiration, RESPIROMETRY, Robustness (evolution), biology.organism_classification, Biotechnology, PLASMID BURDEN, [SDE]Environmental Sciences, Identifiability, Biological Assay, BIOMASS GROWTH, business, CONTROLE, Plasmids
Abstract

Start-up phenomena in microbial biokinetic assays are not captured by the most commonly used growth-related equations. In this Study we propose a new respirometric experimental design to estimate intrinsic growth parameters that allow us to avoid these limitations without data omission, separate mathematical treatment, or wake-up pulses prior to the analysis.. Identifiability and sensitivity analysis were performed to confirm the robustness of the new approach for obtaining unique and accurate estimates of growth kinetic parameters. The new experimental design was applied to establish the. metabolic burden caused by the carriage of a pWW0 TOL plasmid in the model organism Pseudomonas putida KT2440; The metabolic,burden associated was manifested as a reduction in the yield and the specific growth rate of the host, with both plasmid maintenance and the over-expression of recombinant proteins from the plasmid contributing equally to the overall effect.

Published
2009

31. Mass action models describing extant horizontal transfer of plasmids: inferences and parameter sensitivities

Author

Barth F, Smets and Laurent, Lardon

Subjects
Genetics, Microbial, Gene Transfer, Horizontal, Models, Genetic, Computer Simulation, Software, Plasmids
Abstract

Predicting the fate of horizontally transmissible elements in extant microbial communities might be facilitated by the availability of suitable mathematical models. Since the mid-1970s, mass action models have been introduced to describe the transfer of conjugal and mobilizable genetic elements. This chapter will summarize and explain the assumptions behind spatially homogenous models, and show the predictions by these models under typical scenarios, such as evaluating existence conditions of conjugal plasmids under chemostat or seasonal growth conditions. Special attention is given to the sensitivity of the outcomes to the various plasmid dynamic parameters. For our analysis, we developed a set of user-friendly MatLab routines, which are deposited in the public domain. We hope that the availability of these routines will encourage the computationally untrained microbiologist to make use of these mathematical models. Finally, further permutations, as well as limitations of these mass action models in view of the structured complexity of most microbial systems are addressed.

Published
2009

32. Mass Action Models Describing Extant Horizontal Transfer of Plasmids: Inferences and Parameter Sensitivities

Author

Laurent Lardon and Barth F. Smets

Subjects
Mathematical model, Action (philosophy), Extant taxon, Ecology, Horizontal gene transfer, Biochemical engineering, Sensitivity (control systems), Biology, Set (psychology), Public domain
Abstract

Predicting the fate of horizontally transmissible elements in extant microbial communities might be facilitated by the availability of suitable mathematical models. Since the mid-1970s, mass action models have been introduced to describe the transfer of conjugal and mobilizable genetic elements. This chapter will summarize and explain the assumptions behind spatially homogenous models, and show the predictions by these models under typical scenarios, such as evaluating existence conditions of conjugal plasmids under chemostat or seasonal growth conditions. Special attention is given to the sensitivity of the outcomes to the various plasmid dynamic parameters. For our analysis, we developed a set of user-friendly MatLab routines, which are deposited in the public domain. We hope that the availability of these routines will encourage the computationally untrained microbiologist to make use of these mathematical models. Finally, further permutations, as well as limitations of these mass action models in view of the structured complexity of most microbial systems are addressed.

Published
2009

34. Data mining to support anaerobic WWTP monitoring

Author

Laurent Lardon, Maurice Dixon, Jerome V. Healy, Simon Lambert, Jean-Philippe Steyer, Julian R. Gallop, London Metropolitan University, Council for the Central Laboratory of the Research Councils (CCLRC), STFC Rutherford Appleton Laboratory (RAL), Science and Technology Facilities Council (STFC)-Science and Technology Facilities Council (STFC), 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), and 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)

Subjects
0209 industrial biotechnology, Engineering, [SDV]Life Sciences [q-bio], PREDICTION INTERVAL, 02 engineering and technology, REACTOR STATES, 010501 environmental sciences, Remote monitoring and control, 01 natural sciences, REACTOR MODELLING, 020901 industrial engineering & automation, Electrical and Electronic Engineering, DIGESTION ANAEROBIE, 0105 earth and related environmental sciences, Data processing, Waste management, business.industry, Applied Mathematics, WATER POLLUTION, 6. Clean water, Computer Science Applications, Waste treatment, TELEMAC, Anaerobic digestion, WASTE TREATMENT, Control and Systems Engineering, [SDE]Environmental Sciences, Sewage treatment, Anaerobic wastewater treatment, business, Anaerobic exercise, CONTROLE
Abstract

International audience; The stable and efficient operation of anaerobic wastewater treatment plants (WWTPs) is a major challenge for monitoring and control systems. Support for distributed anaerobic WWTPs through remotely monitoring their data was investigated in the TELEMAC framework. This paper describes how the accumulating filtered sensor data was mined to contribute to the refining of expert experience for insights into digester states. Visualisation techniques were used to present cluster analyses of digester states. A procedure for determining prediction intervals is described together with its application for volatile fatty acid concentrations; this procedure enables prediction risk assessment.

Published
2007

35. Modular expert system for the diagnosis of operating conditions of industrial anaerobic digestion plants

Author

Laurent Lardon, A. Puñal, Jean-Philippe Steyer, Justino Martínez, 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), Agralco S. Coop, Partenaires INRAE, and ProdInra, Migration

Subjects
EAU USEE, Engineering, Environmental Engineering, Process (engineering), Heuristic (computer science), media_common.quotation_subject, 0207 environmental engineering, UNCERTAINTY, 02 engineering and technology, 010501 environmental sciences, computer.software_genre, 01 natural sciences, Modularity, DEMPSTER-SHAFER THEORY, Quality (business), [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology, 020701 environmental engineering, ON-LINE DIAGNOSIS, 0105 earth and related environmental sciences, Water Science and Technology, media_common, INDUSTRIAL PLANT, Waste management, TELEMONITORING, business.industry, Modular design, 6. Clean water, Expert system, Anaerobic digestion, Knowledge base, [SDU.STU.HY] Sciences of the Universe [physics]/Earth Sciences/Hydrology, Biochemical engineering, business, computer
Abstract

International audience; Anaerobic digestion (AD) plants are highly efficient wastewater treatment processes with possible energetic valorisation. Despite these advantages, many industries are still reluctant to use them because of their instability in the face of changes in operating conditions. To the face this drawback and to enhance the industrial use of anaerobic digestion, one solution is to develop and to implement knowledge base (KB) systems that are able to detect and to assess in real-time the quality of operating conditions of the processes. Case-based techniques and heuristic approaches have been already tested and validated on AD processes but two major properties were lacking: modularity of the system (the knowledge base system should be easily tuned on a new process and should still work if one or more sensors are added or removed) and uncertainty management (the assessment of the KB system should remain relevant even in the case of too poor or conflicting information sources). This paper addresses these two points and presents a modular KB system where an uncertain reasoning formalism is used to combine partial and complementary fuzzy diagnosis modules. Demonstration of the interest of the approach is provided from real-life experiments performed on an industrial 2,000 m3 CSTR anaerobic digester.

Published
2005

36. On-line supervision and control of an aerobic SBR process

Author

Jérôme Harmand, Laurent Lardon, F. Ciappelloni, D. Mazouni, 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 la Recherche Agronomique (INRA), and ProdInra, Migration

Subjects
Quality Control, Engineering, Environmental Engineering, Nitrogen, SEQUENCING BATCH REACTOR, 02 engineering and technology, 010501 environmental sciences, Fault (power engineering), Waste Disposal, Fluid, 01 natural sciences, Fault detection and isolation, Bioreactors, Software, 020401 chemical engineering, Forensic engineering, Biomass, DECISION SUPPORT SYSTEM, 0204 chemical engineering, MONITORING, [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology, MATLAB, FAULT DETECTION AND ISOLATION, 0105 earth and related environmental sciences, Water Science and Technology, computer.programming_language, business.industry, Process (computing), Carbon, Reliability engineering, Bacteria, Aerobic, Data set, Pilot plant, [SDU.STU.HY] Sciences of the Universe [physics]/Earth Sciences/Hydrology, PRINCIPAL COMPONENT ANALYSIS, Safety, business, computer, Model building, CONTROLE
Abstract

This paper presents a new software developed in MATLAB for analyzing on-line data of an aerobic SBR, detecting faults and, in this case, proposing the most probable causes of fault. Process diagnosis is achieved using a statistical method divided in two main phases: off-line model building and on-line data diagnosis. The off-line model identifies the correct working conditions of the system (standard operative conditions). It includes the characterization of the deviation of the system from these standard conditions in the case of changing in the biomass properties or carbon and nitrogen load characteristics. The on-line diagnosis aims at collecting and analyzing all the available data available through industrial sensors, and at classifying the behavior of each treatment cycle. The diagnosis performance of the proposed method is tested using a data set of an aerobic SBR pilot plant.

Published
2004

37. Sensors network diagnosis in anaerobic digestion processes using evidence theory

Author

Jean-Philippe Steyer, Olivier Bernard, Laurent Lardon, ProdInra, Migration, 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), and 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)

Subjects
[SDE] Environmental Sciences, Time Factors, Environmental Engineering, Computer science, [SDV]Life Sciences [q-bio], Context (language use), Biosensing Techniques, Fault (power engineering), Waste Disposal, Fluid, 7. Clean energy, Fault detection and isolation, Bacteria, Anaerobic, Bioreactors, Software, 0502 economics and business, Point (geometry), Instrumentation (computer programming), Water Science and Technology, Models, Statistical, business.industry, 05 social sciences, Process (computing), Signal Processing, Computer-Assisted, Control engineering, TRAITEMENT DES EAUX USEES, Modular design, [SDV] Life Sciences [q-bio], Multivariate Analysis, [SDE]Environmental Sciences, Equipment Failure, 050211 marketing, business, 050203 business & management
Abstract

Instrumentation defines a sensors network on a process. Hardware sensors indeed allow one to get different information sources that can be often cross-checked to provide reliable data. However, each of these sources of information contains some uncertainties, either due to the hardware sensors' measurement principles, to their possible fouling, to the estimated parameters of the models used in software sensors and/or to the specific structures of the software sensors. This paper demonstrates that, in this context, the evidence theory is a very well suited formalism for fault detection and diagnosis. This theory indeed allows one to take into account the exact knowledge supported by each source of information and to combine them in order to detect the occurring faults. Moreover, this combination guarantees the best fault isolability from a practical point of view and is suitable for multiple faults occurring at the same time. Finally, the evidence theory is a highly modular formalism since new information sources can be very easily added and old ones can be removed. Validation is performed using real-life experiments from a 1 m3 anaerobic digestion fixed bed process used applied to the treatment of winery wastewaters.

Published
2004

38. An integrated system to remote monitor and control anaerobic wastewater treatment plants through the internet

Author

P. Lemaire, G. Ruiz, O. Schoefs, Enrique Roca, P. Ratini, V. González Álvarez, R. Farina, S. Frattesi, Peter A. Vanrolleghem, F. Esandi, Denis Dochain, Jorge Rodríguez, M. Dixon, H. Fibrianto, J. F. Lavigne, Arnaud Hélias, K. Lievens, P. Neveu, K. De Neve, V. Alcaraz Gonzalez, A. Franco, Justino Martínez, Benoît Chachuat, O. Duclaud, Olivier Bernard, U. Zaher, Dirk J.W. De Pauw, Laurent Lardon, Jean-Philippe Steyer, Julian R. Gallop, B. Le Dantec, A. Quintaba, Simon Lambert, Juan M. Lema, Bruno Sialve, Modeling and control of renewable resources (COMORE), Laboratoire d'océanographie de Villefranche (LOV), Observatoire océanologique de Villefranche-sur-mer (OOVM), Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Observatoire océanologique de Villefranche-sur-mer (OOVM), Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), GEIE-ERCIM, Partenaires INRAE, 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), Analyse des Systèmes et Biométrie (ASB), Institut National de la Recherche Agronomique (INRA), STFC Rutherford Appleton Laboratory (RAL), Science and Technology Facilities Council (STFC), Societa' Progettazione Elettronica Software, Universidade de Santiago, BIOMATH, Universiteit Gent = Ghent University [Belgium] (UGENT), Centre for systems engineering and applied mechanics [Louvain] (CESAME), Université Catholique de Louvain = Catholic University of Louvain (UCL), Italian National Agency for New Tecnologies Energy and Sustainable Economic Development (ENEA), Universidad de Guadalajara, Agrupacion Alcoholera de Bodega Cooperativa, Tequila Sauza S.A., Allied Domecq, Pierre Lemaire, Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), and Universiteit Gent = Ghent University (UGENT)

Subjects
Engineering, Environmental Engineering, Systems Analysis, Automatic control, ANAEROBIC DIGESTION, Industrial Waste, AUTOMATION, 02 engineering and technology, 010501 environmental sciences, 01 natural sciences, Civil engineering, Waste Disposal, Fluid, 12. Responsible consumption, Bacteria, Anaerobic, Bioreactors, Biogas, 0202 electrical engineering, electronic engineering, information engineering, [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology, Process engineering, FAULT DETECTION AND ISOLATION, 0105 earth and related environmental sciences, Water Science and Technology, Internet, business.industry, REMOTE MONITORING, MODELLING, Automation, 6. Clean water, WINERY, TELEMAC, Systems analysis, Wastewater, 020201 artificial intelligence & image processing, Sewage treatment, The Internet, business, CONTROLE, Software
Abstract

International audience; The TELEMAC project brings new methodologies from the Information and Science Technologies field to the world of water treatment. TELEMAC offers an advanced remote management system which adapts to most of the anaerobic wastewater treatment plants that do not benefit from a local expert in wastewater treatment. The TELEMAC system takes advantage of new sensors to better monitor the process dynamics and to run automatic controllers that stabilise the treatment plant, meet the depollution requirements and provide a biogas quality suitable for cogeneration. If the automatic system detects a failure which cannot be solved automatically or locally by a technician, then an expert from the TELEMAC Control Centre is contacted via the internet and manages the problem.

39. Modeling the fate of micropollutant organics during anaerobic digestion of contaminated sewage sludge

Author

Delgadillo Mirquez, Liliana Rocio, 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 la Recherche Agronomique (INRA), Université Montpellier 2 (Sciences et Techniques), Dominique Patureau, and Laurent Lardon

Subjects
sorption, nonylphenol, [SDV]Life Sciences [q-bio], polycyclic aromatic hydrocarbons, biodégradation, biodegradation, environnement, méthanisation, methanisation, hydrocarbures aromatiques polycycliques, co-métabolisme, nonylphénol, co-metabolism, GENIE DES PROCEDES, dépollution, [SDE]Environmental Sciences, modélisation
Abstract

Beaucoup de micropolluants organiques sont présents dans les boues. Leur possible impact sur l'environnement contribue à accroître leur intérêt scientifique et social. La digestion anaérobie présente un potentiel certain pour dégrader ces composés. Dans ce travail, il a été développé un modèle dynamique pour décrire le devenir de micropolluants hydrophobes au cours de la digestion anaérobie de boues contaminées. Le modèle est basé sur une distribution des composés dans quatrecompartiments et il a démontré que la transformation des micropolluants est bien simulée si l’on considère une cinétique de co-métabolisme pour la dégradation et si la phase aqueuse constitue le compartiment biodisponible. Dans ce modèle, la sorption des micropolluants hydrophobes est envisagée sur deux phases différentes: la matière particulaire et la matière dissoute/colloïdale (DCM), car la sorption sur le compartiment DCM peut influencer la disponibilité des composés et donc leur biodégradation. Il a été conclu que le transfert de micropolluants hydrophobes ne limite pas leur biodégradation, et que leur devenir est régi par l'état d'équilibre de sorption-désorption. Afin d’identifier quelle(s) étape(s) de la digestion permet le co-métabolisme, de nouvelles expérimentations ont été menées en utilisant des inhibiteurs des Méthanogènes. Elles suggèrent que la dégradation anaérobie des micropolluants implique principalement des microorganismes non-méthanogènes. En effet, la transformation co-métabolique des micropolluants serait principalement liée à la population acidogène, comme le montre le modèle avancé proposé. Le modèle proposé est potentiellement utile pour mieux comprendre la distribution des micropolluants, prédire leur devenir dans des conditions anaérobies et aider à optimiser le processus de fonctionnement pour leur abattement., Many organic micropollutants are present in sludge. Their possible impact on the environment contributes to their increasing scientific and social interests. Anaerobic digestion has been shown as a potential biological process for removing these compounds. In this work, a dynamical fate model is developed for hydrophobic micropollutant under anaerobic digestion of contaminated sludge. The model is based on a four-compartment distribution and demonstrated that the micropollutant transformation is well simulated if considering a co-metabolic kinetic and the aqueous phase as the bioavailable compartment. In this model, the sorption of hydrophobic micropollutants is considered on two different phases: particulate matter and dissolved/colloidal matter (DCM). Indeed, the sorption onto DCM can influence the availability of compounds for biodegradation. It was concluded that hydrophobic micropollutant transfer does not limit their biodegradation, and that their fate is governed by sorption-desorption equilibrium state. In order to evaluate which step of the anaerobic pathway is implied in the co-metabolism of micropollutants, experimental set-ups were designed using different way to inhibit the Methanogens. The experimental inhibition of methanogenic activity suggests that the anaerobic degradation of micropollutants mainly involves non-methanogenic microorganisms. Indeed, the co-metabolic transformation of micropollutants would be mainly linked to the acidogenic population as it was shown through the proposed advanced model. This latter is potentially useful to better understand the micropollutant distribution, predict their fate under anaerobic condition and help to optimize the operation process for their depletion.

Published
2011

40. Modélisation du devenir des micropolluants organiques au cours de la digestion anaérobie de boues contaminées

Author

Delgadillo Mirquez, Liliana Rocio, 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 la Recherche Agronomique (INRA), Université Montpellier 2 (Sciences et Techniques), Dominique Patureau, and Laurent Lardon

Subjects
sorption, nonylphenol, [SDV]Life Sciences [q-bio], polycyclic aromatic hydrocarbons, biodégradation, biodegradation, méthanisation, methanisation, hydrocarbures aromatiques polycycliques, co-metabolism, [SDE]Environmental Sciences, GENIE DES PROCEDES, nonylphénol, co-métabolisme
Abstract

Thèse soutenue le 2 décembre 2011 Diplôme : Dr. d'Université; Many organic micropollutants are present in sludge. Their possible impact on the environment contributes to their increasing scientific and social interests. Anaerobic digestion has been shown as a potential biological process for removing these compounds. In this work, a dynamical fate model is developed for hydrophobic micropollutant under anaerobic digestion of contaminated sludge. The model is based on a four-compartment distribution and demonstrated that the micropollutant transformation is well simulated if considering a co-metabolic kinetic and the aqueous phase as the bioavailable compartment. In this model, the sorption of hydrophobic micropollutants is considered on two different phases: particulate matter and dissolved/colloidal matter (DCM). Indeed, the sorption onto DCM can influence the availability of compounds for biodegradation. It was concluded that hydrophobic micropollutant transfer does not limit their biodegradation, and that their fate is governed by sorption-desorption equilibrium state. In order to evaluate which step of the anaerobic pathway is implied in the co-metabolism of micropollutants, experimental set-ups were designed using different way to inhibit the Methanogens. The experimental inhibition of methanogenic activity suggests that the anaerobic degradation of micropollutants mainly involves non-methanogenic microorganisms. Indeed, the co-metabolic transformation of micropollutants would be mainly linked to the acidogenic population as it was shown through the proposed advanced model. This latter is potentially useful to better understand the micropollutant distribution, predict their fate under anaerobic condition and help to optimize the operation process for their depletion.; Beaucoup de micropolluants organiques sont présents dans les boues. Leur possible impact sur l'environnement contribue à accroître leur intérêt scientifique et social. La digestion anaérobie présente un potentiel certain pour dégrader ces composés. Dans ce travail, il a été développé un modèle dynamique pour décrire le devenir de micropolluants hydrophobes au cours de la digestion anaérobie de boues contaminées. Le modèle est basé sur une distribution des composés dans quatrecompartiments et il a démontré que la transformation des micropolluants est bien simulée si l’on considère une cinétique de co-métabolisme pour la dégradation et si la phase aqueuse constitue le compartiment biodisponible. Dans ce modèle, la sorption des micropolluants hydrophobes est envisagée sur deux phases différentes: la matière particulaire et la matière dissoute/colloïdale (DCM), car la sorption sur le compartiment DCM peut influencer la disponibilité des composés et donc leur biodégradation. Il a été conclu que le transfert de micropolluants hydrophobes ne limite pas leur biodégradation, et que leur devenir est régi par l'état d'équilibre de sorption-désorption. Afin d’identifier quelle(s) étape(s) de la digestion permet le co-métabolisme, de nouvelles expérimentations ont été menées en utilisant des inhibiteurs des Méthanogènes. Elles suggèrent que la dégradation anaérobie des micropolluants implique principalement des microorganismes non-méthanogènes. En effet, la transformation co-métabolique des micropolluants serait principalement liée à la population acidogène, comme le montre le modèle avancé proposé. Le modèle proposé est potentiellement utile pour mieux comprendre la distribution des micropolluants, prédire leur devenir dans des conditions anaérobies et aider à optimiser le processus de fonctionnement pour leur abattement.

Published
2011

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