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Modelling Methane Production and Sulfate Reduction in Anaerobic Granular Sludge Reactor with Ethanol as Electron Donor
In this work, a mathematical model based on growth kinetics of microorganisms and substrates transportation through biofilms was developed to describe methane production and sulfate reduction with ethanol being a key electron donor. The model was calibrated and validated using experimental data from...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5059677/ https://www.ncbi.nlm.nih.gov/pubmed/27731395 http://dx.doi.org/10.1038/srep35312 |
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author | Sun, Jing Dai, Xiaohu Wang, Qilin Pan, Yuting Ni, Bing-Jie |
author_facet | Sun, Jing Dai, Xiaohu Wang, Qilin Pan, Yuting Ni, Bing-Jie |
author_sort | Sun, Jing |
collection | PubMed |
description | In this work, a mathematical model based on growth kinetics of microorganisms and substrates transportation through biofilms was developed to describe methane production and sulfate reduction with ethanol being a key electron donor. The model was calibrated and validated using experimental data from two case studies conducted in granule-based Upflow Anaerobic Sludge Blanket reactors. The results suggest that the developed model could satisfactorily describe methane and sulfide productions as well as ethanol and sulfate removals in both systems. The modeling results reveal a stratified distribution of methanogenic archaea, sulfate-reducing bacteria and fermentative bacteria in the anaerobic granular sludge and the relative abundances of these microorganisms vary with substrate concentrations. It also indicates sulfate-reducing bacteria can successfully outcompete fermentative bacteria for ethanol utilization when COD/SO(4)(2−) ratio reaches 0.5. Model simulation suggests that an optimal granule diameter for the maximum methane production efficiency can be achieved while the sulfate reduction efficiency is not significantly affected by variation in granule size. It also indicates that the methane production and sulfate reduction can be affected by ethanol and sulfate loading rates, and the microbial community development stage in the reactor, which provided comprehensive insights into the system for its practical operation. |
format | Online Article Text |
id | pubmed-5059677 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-50596772016-10-24 Modelling Methane Production and Sulfate Reduction in Anaerobic Granular Sludge Reactor with Ethanol as Electron Donor Sun, Jing Dai, Xiaohu Wang, Qilin Pan, Yuting Ni, Bing-Jie Sci Rep Article In this work, a mathematical model based on growth kinetics of microorganisms and substrates transportation through biofilms was developed to describe methane production and sulfate reduction with ethanol being a key electron donor. The model was calibrated and validated using experimental data from two case studies conducted in granule-based Upflow Anaerobic Sludge Blanket reactors. The results suggest that the developed model could satisfactorily describe methane and sulfide productions as well as ethanol and sulfate removals in both systems. The modeling results reveal a stratified distribution of methanogenic archaea, sulfate-reducing bacteria and fermentative bacteria in the anaerobic granular sludge and the relative abundances of these microorganisms vary with substrate concentrations. It also indicates sulfate-reducing bacteria can successfully outcompete fermentative bacteria for ethanol utilization when COD/SO(4)(2−) ratio reaches 0.5. Model simulation suggests that an optimal granule diameter for the maximum methane production efficiency can be achieved while the sulfate reduction efficiency is not significantly affected by variation in granule size. It also indicates that the methane production and sulfate reduction can be affected by ethanol and sulfate loading rates, and the microbial community development stage in the reactor, which provided comprehensive insights into the system for its practical operation. Nature Publishing Group 2016-10-12 /pmc/articles/PMC5059677/ /pubmed/27731395 http://dx.doi.org/10.1038/srep35312 Text en Copyright © 2016, The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Sun, Jing Dai, Xiaohu Wang, Qilin Pan, Yuting Ni, Bing-Jie Modelling Methane Production and Sulfate Reduction in Anaerobic Granular Sludge Reactor with Ethanol as Electron Donor |
title | Modelling Methane Production and Sulfate Reduction in Anaerobic Granular Sludge Reactor with Ethanol as Electron Donor |
title_full | Modelling Methane Production and Sulfate Reduction in Anaerobic Granular Sludge Reactor with Ethanol as Electron Donor |
title_fullStr | Modelling Methane Production and Sulfate Reduction in Anaerobic Granular Sludge Reactor with Ethanol as Electron Donor |
title_full_unstemmed | Modelling Methane Production and Sulfate Reduction in Anaerobic Granular Sludge Reactor with Ethanol as Electron Donor |
title_short | Modelling Methane Production and Sulfate Reduction in Anaerobic Granular Sludge Reactor with Ethanol as Electron Donor |
title_sort | modelling methane production and sulfate reduction in anaerobic granular sludge reactor with ethanol as electron donor |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5059677/ https://www.ncbi.nlm.nih.gov/pubmed/27731395 http://dx.doi.org/10.1038/srep35312 |
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