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Influence of Process Parameters on Anaerobic Digestion Microbiome in Bioenergy Production: Towards an Improved Understanding.

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Title: Influence of Process Parameters on Anaerobic Digestion Microbiome in Bioenergy Production: Towards an Improved Understanding.
Authors: Kundu, Kankana, Sharma, Shilpi1 shilpi@dbeb.iitd.ac.in, Sreekrishnan, T.1
Source: BioEnergy Research. Mar2017, Vol. 10 Issue 1, p288-303. 16p.
Subjects: Anaerobic digestion, Methane analysis, Performance of bioreactors, Biomass energy, Microbiology
Abstract: During the anaerobic digestion (AD) of waste and production of methane as an alternative fuel, unpredictability and instability of bioreactors are a challenge. Leading cause of such failure is the lack of knowledge about the microbial consortia involved in AD under different process conditions or perturbations during field applications. Over the last decade, microbiome in AD has been extensively studied with culture-independent molecular microbiology techniques, which have shed light into their structure and function. Hence, a critical discussion and consolidation of efforts made in these studies towards linking microbiome to reactor performance are required. This review focuses on how such molecular information can provide improved insight in correlation of microbiome structure and reactor performance to attain a stable and efficient AD. Studies describing microbial community dynamics under different process conditions have been briefly discussed along with the molecular approaches used. Furthermore, interesting developments toward the inclusion of diversity to mathematical modeling of AD systems are discussed. Based on the present state of the advancements, the review endeavors to critically evaluate the current knowledge gaps and research questions that must be resolved in the future in order to address the problem of instability associated with AD. [ABSTRACT FROM AUTHOR]
Copyright of BioEnergy Research is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: During the anaerobic digestion (AD) of waste and production of methane as an alternative fuel, unpredictability and instability of bioreactors are a challenge. Leading cause of such failure is the lack of knowledge about the microbial consortia involved in AD under different process conditions or perturbations during field applications. Over the last decade, microbiome in AD has been extensively studied with culture-independent molecular microbiology techniques, which have shed light into their structure and function. Hence, a critical discussion and consolidation of efforts made in these studies towards linking microbiome to reactor performance are required. This review focuses on how such molecular information can provide improved insight in correlation of microbiome structure and reactor performance to attain a stable and efficient AD. Studies describing microbial community dynamics under different process conditions have been briefly discussed along with the molecular approaches used. Furthermore, interesting developments toward the inclusion of diversity to mathematical modeling of AD systems are discussed. Based on the present state of the advancements, the review endeavors to critically evaluate the current knowledge gaps and research questions that must be resolved in the future in order to address the problem of instability associated with AD. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of BioEnergy Research is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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