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In this study, nuclear magnetic resonance ( 1 H NMR) spectroscopic profiling was used to provide a more comprehensive view of microbial metabolites associated with poor reactor performance in a full-scale 4 MW mesophilic agricultural biogas plant under fully operational and also under inhibited conditions. Multivariate analyses were used to assess the significance of differences between reactors whereas artificial neural networks (ANN) were used to identify the key metabolites responsible for inhibition and their network of interaction. Based on the results of nm-MDS ordination the subsamples of each reactor were similar, but not identical, despite homogenization of the full-scale reactors before sampling. Hence, a certain extent of variability due to the size of the system under analysis was transferred into metabolome analysis. Multivariate analysis showed that fully active reactors were clustered separately from those containing inhibited reactor metabolites and were significantly different. Furthermore, the three distinct inhibited states were significantly different from each other. The inhibited metabolomes were enriched in acetate, caprylate, trimethylamine, thymine, pyruvate, alanine, xanthine and succinate. The differences in the metabolic fingerprint between inactive and fully active reactors observed in this study resembled closely the metabolites differentiating the (sub) acute rumen acidosis inflicted and healthy rumen metabolomes, creating thus favorable conditions for the growth and activity of pathogenic bacteria. The consistency of our data with those reported before for rumen ecosystems shows that 1 H NMR based metabolomics is a reliable approach for the evaluation of metabolic events at full-scale biogas reactors., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

A start-up of 4 MW agricultural biogas plant in Vučja vas, Slovenia, was monitored from 2011 to 2014. The start-up was carried out in 3 weeks with the intake of biomass from three operating full-scale 1-2 MW donor agricultural biogas plants. The samples were taken from donor digesters and from two serial digesters during the start-up over the course of 2.5 years. Bacterial and Archaeal microbial communities progressively diverged from the composition of donor digesters during the start-up phase. The rate of change of Bacterial community decreased exponentially over the first 2.5 years as dynamics within the first 70 days was comparable to that of the next 1.5 years, whereas approximately constant rate was observed for Archaea. Despite rearrangements, the microbial communities remained functionally stable and produced biogas throughout the whole 2.5 years of observation. All systems parameters measured were ordered according to their Kernel density (Gaussian function) ranging from the most dispersed (substrate categories used as cosubstrates, quantities of each cosubstrate, substate dry and volatile matter, process parameters) towards progressively least dispersed (trace metal and ion profiles, aromatic-polyphenolic compounds, biogas plant functional output (energy)). No deficiency was detected in trace metal content as the distribution of metals and elements fluctuated within the suggested limits for biogas over 2.5 year observation. In contrast to the recorded process variables, Bacterial and Archaeal microbial communities exhibited directed changes oriented in time. Variation partitioning showed that a large fraction of variability in the Bacterial and Archaeal microbial communities (55% and 61%, respectively) remained unexplained despite numerous measured variables (n = 44) and stable biogas production. Our results show that the observed reorganization of microbial communities was not directly associated with impact on the full-scale biogas reactor performance. Novel parameters need to be determined to elucidate the variables directly associated with the reorganization of microbial communities and those relevant for sustained function such as the more in-depth interaction between TSOC, trace metal profiles, aromatic-polyphenolic compounds and ionic strength (e.g. electrical conductivity)., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

The primary aim of the study was to develop and validate an in-house upscale of Automatic Methane Potential Test System II for studying real-time inocula and real-scale substrates in batch, codigestion and enzyme enhanced hydrolysis experiments, in addition to semi-continuous operation of the developed equipment and experiments testing inoculum functional quality. The successful upscale to 5L enabled comparison of different process configurations in shorter preparation times with acceptable accuracy and high-through put intended for industrial decision making. The adoption of the same scales, equipment and methodologies in batch and semi-continuous tests mirroring those at full scale biogas plants resulted in matching methane yields between the two laboratory tests and full-scale, confirming thus the increased decision making value of the approach for industrial operations., (Copyright © 2013 Elsevier Ltd. All rights reserved.)