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

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

SUBMITTER: Fisgativa H

PROVIDER: S-EPMC7013330 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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

Fisgativa Henry H Zennaro Bastien B Charnier Cyrille C Richard Charlotte C Accarion Guillaume G Béline Fabrice F

Data in brief 20200131

This article contains the data of 11 organic substrates including physicochemical, biochemical and nutritional characterisations. Additionally, it includes for all substrates the data of organic matter fractionation into easily biodegradable, slowly biodegradable and inert fractions performed with anaerobic respirometry method. Finally, based on physicochemical characterisations and organic matter fractionation, a detailed methodology for the determination of input state variables required for t ...[more]

PMID: 32071987

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Project description:Background:Anaerobic digestion, whose final products are methane and carbon dioxide, ensures energy flow and circulation of matter in ecosystems. This naturally occurring process is used for the production of renewable energy from biomass. Lactate, a common product of acidic fermentation, is a key intermediate in anaerobic digestion of biomass in the environment and biogas plants. Effective utilization of lactate has been observed in many experimental approaches used to study anaerobic digestion. Interestingly, anaerobic lactate oxidation and lactate oxidizers as a physiological group in methane-yielding microbial communities have not received enough attention in the context of the acetogenic step of anaerobic digestion. This study focuses on metabolic transformation of lactate during the acetogenic and methanogenic steps of anaerobic digestion in methane-yielding bioreactors. Results:Methane-yielding microbial communities instead of pure cultures of acetate producers were used to process artificial lactate-rich media to methane and carbon dioxide in up-flow anaerobic sludge blanket reactors. The media imitated the mixture of acidic products found in anaerobic environments/digesters where lactate fermentation dominates in acidogenesis. Effective utilization of lactate and biogas production was observed. 16S rRNA profiling was used to examine the selected methane-yielding communities. Among Archaea present in the bioreactors, the order Methanosarcinales predominated. The acetoclastic pathway of methane formation was further confirmed by analysis of the stable carbon isotope composition of methane and carbon dioxide. The domain Bacteria was represented by Bacteroidetes, Firmicutes, Proteobacteria, Synergistetes, Actinobacteria, Spirochaetes, Tenericutes, Caldithrix, Verrucomicrobia, Thermotogae, Chloroflexi, Nitrospirae, and Cyanobacteria. Available genome sequences of species and/or genera identified in the microbial communities were searched for genes encoding the lactate-oxidizing metabolic machinery homologous to those of Acetobacterium woodii and Desulfovibrio vulgaris. Furthermore, genes for enzymes of the reductive acetyl-CoA pathway were present in the microbial communities. Conclusions:The results indicate that lactate is oxidized mainly to acetate during the acetogenic step of AD and this comprises the acetotrophic pathway of methanogenesis. The genes for lactate utilization under anaerobic conditions are widespread in the domain Bacteria. Lactate oxidation to the substrates for methanogens is the most energetically attractive process in comparison to butyrate, propionate, or ethanol oxidation.

Project description:Although biotic interactions among members of microbial communities have been conceived to be crucial for community assembly, it remains unclear how changes in environmental conditions affect microbial interaction and consequently system performance. Here, we adopted a random matrix theory-based network analysis to explore microbial interactions in triplicate anaerobic digestion (AD) systems, which is widely applied for organic pollutant treatments. The digesters were operated with incremental organic loading rates (OLRs) from 1.0 g volatile solids (VS)/liter/day to 1.3 g VS/liter/day and then to 1.5 g VS/liter/day, which increased VS removal and methane production proportionally. Higher resource availability led to networks with higher connectivity and shorter harmonic geodesic distance, suggestive of more intense microbial interactions and quicker responses to environmental changes. Strikingly, a number of topological properties of microbial network showed significant (P < 0.05) correlation with AD performance (i.e., methane production, biogas production, and VS removal). When controlling for environmental parameters (e.g., total ammonia, pH, and the VS load), node connectivity, especially that of the methanogenic archaeal network, still correlated with AD performance. Last, we identified the Methanothermus, Methanobacterium, Chlorobium, and Haloarcula taxa and an unclassified Thaumarchaeota taxon as keystone nodes of the network.IMPORTANCE AD is a biological process widely used for effective waste treatment throughout the world. Biotic interactions among microbes are critical to the assembly and functioning of the microbial community, but the response of microbial interactions to environmental changes and their influence on AD performance are still poorly understood. Using well-replicated time series data of 16S rRNA gene amplicons and functional gene arrays, we constructed random matrix theory-based association networks to characterize potential microbial interactions with incremental OLRs. We demonstrated striking linkage between network topological features of methanogenic archaea and AD functioning independent of environmental parameters. As the intricate balance of multiple microbial functional groups is responsible for methane production, our results suggest that microbial interaction may be an important, previously unrecognized mechanism in determining AD performance.

Dataset Information

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

Publications

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

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