Abstract
Introduction: Methane is a low carbon fuel with multiple advantages over fossil fuels. With the emphasis on reduction of carbon emission, the preference for methane production via anaerobic digestion, using communities of microorganisms has increased. This research sought to understand how these communities respond to changing conditions within anaerobic digesters as a step towards the optimization of methane gas production. The research aimed to monitor microbial community profiles of two anaerobic digester sites over a five-month period; to determine the impact of changing feedstock on the microbial community profile and gas production; and to evaluate the impact of Carbon type and Carbon: Nitrogen ratio on microbial communities digesting starch, xylan and cellulose, and the resultant effect on gas production.Methods: This study is split into three research chapters, each using combinations of 16S rRNA gene sequencing, quantitative polymerase chain reaction (qPCR), and Community Level Physiological Profile (CLPP). The first chapter studies the microbial community of two anaerobic digester sites over a five-month period and incorporates data from the site. The second and third chapters use the laboratory-scale Automated Methane Production Test System (AMPTS II) to monitor gas production and simulate anaerobic digester conditions. Throughout data is analysed using appropriate statistical analysis to correlate microbial community change with gas production and system variables.
Results: The microbial communities were distinct between sites and consistent over a five-month period but shifted with changing feed within digesters. Biodiversity correlated positively with methanogen abundance. The methanogen concentration, in turn correlated positively with gas production rate. Carbon: Nitrogen ratio had significant impact on physiological profile and methane gas production.
Discussion: Results show that both carbon type and Carbon: Nitrogen ratio of the feedstock impact gas production through changes in microbial community structure and functional diversity. Findings provide potential opportunities to optimize gas production by feedstock modifications as well as prediction of AD performance using microbial community-based algorithms.
| Date of Award | 21 Jan 2026 |
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| Original language | English |
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| Supervisor | Caroline Orr (Supervisor) & David Wright (Supervisor) |