Effect of a Profound Feedstock Change on the Structure and Performance of Biogas Microbiomes

Functional conservation of microbial communities determines composition predictability in anaerobic digestion

A major challenge in managing and engineering microbial communities is determining whether and how microbial community responses to environmental alterations can be predicted and explained, especially in microorganism-driven systems. We addressed this challenge by monitoring microbial community responses to the periodic addition of the same feedstock throughout anaerobic digestion, a typical microorganism-driven system where microorganisms degrade and transform the feedstock. The immediate and delayed response consortia were assemblages of microorganisms whose abundances significantly increased on the first or third day after feedstock addition. The immediate response consortia were more predictable than the delayed response consortia and showed a reproducible and predictable order-level composition across multiple feedstock additions. These results stood in both present (16 S rRNA gene) and potentially active (16 S rRNA) microbial communities and in different feedstocks with different biodegradability and were validated by simulation modeling. Despite substantial species variability, the immediate response consortia aligned well with the reproducible CH4 production, which was attributed to the conservation of expressed functions by the response consortia throughout anaerobic digestion, based on metatranscriptomic data analyses. The high species variability might be attributed to intraspecific competition and contribute to biodiversity maintenance and functional redundancy. Our results demonstrate reproducible and predictable microbial community responses and their importance in stabilizing system functions.

Co-substrate composition is critical for enrichment of functional key species and for process efficiency during biogas production from cattle manure

Cattle manure has a low energy content and high fibre and water content, limiting its value for biogas production. Co-digestion with a more energy-dense material can improve the output, but the co-substrate composition that gives the best results in terms of degree of degradation, gas production and digestate quality has not yet been identified. This study examined the effects of carbohydrate, protein and fat as co-substrates for biogas production from cattle manure. Laboratory-scale semi-continuous mesophilic reactors were operated with manure in mono-digestion or in co-digestion with egg albumin, rapeseed oil, potato starch or a mixture of these, and chemical and microbiological parameters were analysed. The results showed increased gas yield for all co-digestion reactors, but only the reactor supplemented with rapeseed oil showed synergistic effects on methane yield. The reactor receiving potato starch indicated improved fibre degradation, suggesting a priming effect by the easily accessible carbon. Both these reactors showed increased species richness and enrichment of key microbial species, such as fat-degrading Syntrophomonadaceae and families known to include cellulolytic bacteria. The addition of albumin promoted enrichment of known ammonia-tolerant syntrophic acetate- and potential propionate-degrading bacteria, but still caused slight process inhibition and less efficient overall degradation of organic matter in general, and of cellulose in particular.

Artificial neural networks to differentiate the composition and pyrolysis kinetics of fresh and long-stored maize

In this study, a novel methodology to determine plant biomass composition using artificial neural networks (ANN) is presented. This study was performed to determine the changes in the composition of fresh and 12 month-long stored biomass samples. The production of biofuels is a common method used to manage agricultural waste. However, owing to the seasonal characteristics of cultivation, storage is necessary in the production chain. The results indicated that cellulose and lignin were stable over time, with a maximum drop of 2.82 pp and 1.72 pp, respectively. Hemicellulose was determined to be less stable, with a drop of up to 9.19 pp after 12 months of storage. Regarding the kinetic parameters, the stored samples required a lower activation energy, but only for the active phase of pyrolysis. The accuracy of the proposed tool was extremely high, with a relative percentage difference as low as 12.9%.