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Comparison of existing models to simulate anaerobic digestion of lipid-rich waste
- Source :
- Bioresource Technology, Bioresource Technology, Elsevier, 2017, 226, pp.99-107. ⟨10.1016/j.biortech.2016.12.007⟩
- Publication Year :
- 2017
- Publisher :
- HAL CCSD, 2017.
-
Abstract
- International audience; Models for anaerobic digestion of lipid-rich waste taking inhibition into account were reviewed and, if necessary, adjusted to the ADM1 model framework in order to compare them. Experimental data from anaerobic digestion of slaughterhouse waste at an organic loading rate (OLR) ranging from 0.3 to 1.9kgVSm'3d'1 were used to compare and evaluate models. Experimental data obtained at low OLRs were accurately modeled whatever the model thereby validating the stoichiometric parameters used and influent fractionation. However, at higher OLRs, although inhibition parameters were optimized to reduce differences between experimental and simulated data, no model was able to accurately simulate accumulation of substrates and intermediates, mainly due to the wrong simulation of pH. A simulation using pH based on experimental data showed that acetogenesis and methanogenesis were the most sensitive steps to LCFA inhibition and enabled identification of the inhibition parameters of both steps. © 2016 Elsevier Ltd
- Subjects :
- ADM1
Environmental Engineering
Methanogenesis
020209 energy
Bioengineering
02 engineering and technology
Fractionation
010501 environmental sciences
01 natural sciences
7. Clean energy
Waste Management
0202 electrical engineering, electronic engineering, information engineering
Anaerobiosis
Waste Management and Disposal
0105 earth and related environmental sciences
Waste Products
Renewable Energy, Sustainability and the Environment
Chemistry
Environmental engineering
Experimental data
Reproducibility of Results
General Medicine
Hydrogen-Ion Concentration
Models, Theoretical
Lipids
Anaerobic digestion
Acetogenesis
Simulated data
[SDE]Environmental Sciences
Loading rate
Biological system
Methane
Abattoirs
Biotechnology
Subjects
Details
- Language :
- English
- ISSN :
- 09608524
- Database :
- OpenAIRE
- Journal :
- Bioresource Technology, Bioresource Technology, Elsevier, 2017, 226, pp.99-107. ⟨10.1016/j.biortech.2016.12.007⟩
- Accession number :
- edsair.doi.dedup.....4e9d526968d0277cafeeb38abc104ec4