Failure Tests in Anaerobic Reactors

The overall goal of WP2 is to generate time-series data from perturbation experiments in continuous-flow anaerobic reactors (ARs) for subsequent identification of early warning signals, and quantification of threshold values and warning times.

• Task 2.1. Perturbation experiments 1: High-protein biomass
  Anaerobic microalgae digestion experiments will be performed in continuous-flow reactors operated under controlled conditions of temperature, HRT, and OLR. The effect of perturbations will be evaluated by running the perturbed ARs in parallel with a control reactor (without any disturbance), which will allow identifying the variation in process performance indicators that will later result in a biogas production decrease. The perturbation experiments will be focused on dissimilar microalgae biomasses (e.g. Phaeodactylum tricornutum and Chlorella sp.) subjected to anaerobic degradation. Both microalgal strains exhibit different macromolecular composition in terms of carbohydrate and protein content, which will affect the population dynamics of anaerobes, and consequently the bioprocess performance. The macromolecular variation in the feedstock fed to the reactor will show an impact on microbial biodiversity and on the synergistic interactions. Hence, a core microbiome adapted to protein-rich biomass degradation will be subjected to a microalgae biomass with low protein content and vice versa. Thereafter, experimental indicators will reveal a process transition from normal steady-state to instability and reporting valuable knowledge for predictive model development.
• Task 2.2. Perturbation experiments 2: organic overload
  Organic loading rate (OLR) is a critical operational parameter that has to be controlled in order to avoid AD failure. In AD process, methanogens are able to produce methane by metabolizing the volatile fatty acids (VFAs) generated during the fermentation stage of AD. Methanogenic archaea are slow-growing microorganisms in comparison with fermentative bacteria. In fact, archaea are characterized by a high sensitivity to process variations. Thus, an organic overload results in a process imbalance between hydrolysis/acidogenesis and methanogenesis steps, giving rise to a VFAs accumulation that causes a pH drop and, consequently, a methane production failure. VFAs accumulation can also occurred when archaea are inhibited by some metabolic products such as NH4+/NH3. The biodegradation of proteinaceous feedstock, which is the case of Chlorella, releases high amount of ammonia nitrogen that disturbs the methanogenic metabolisms. However, methanogenic archaea as well as bacteria involved in the previous AD steps can be adapted to progressive increases of ammonia nitrogen.
• Task 2.3 Perturbation experiments 3: Chemical pesticides
  Chemical additives, such as hypochlorite, are commonly used to mitigate pests in microalgae mass culture systems. However, these chemicals, which can remain in the water, could disrupt the metabolisms of bacteria and archaea when reaching the fermentation stage. This can result as well in a methanogenesis inhibition. The chemical perturbations experiments will be performed by increasing the concentrations of different pesticides in continuous-flow reactors at a laboratory scale. ARs will initially be run under stable conditions for degrading both Phaeodactylum tricornutum and Chlorella sp. Once ARs reach the steady-state, the pesticides will be added to evaluate their effect on process performance in terms of biomethane production. The reactors will be monitored using continuous measurement systems for routine variables as well as analysing discrete variables, which will allow identifying the warning indicators that might evidence process failure along with their threshold values and the warning times of critical state transitions.
• Task 2.4 Bioinformatic analyses from ARs genomics data
  Samples for metabarcoding, metagenomics, and metaproteomics will be collected from the perturbation experiments carried out in the ARs. To calibrate the significance of other microbial groups, metagenomic analyses will be compared against metabarcoding data.
• Task 2.5 Chemical fingerprint
  As stated in Task 1.4extracts of the organic matter dissolved in the aqueous phase of the bioreactors will be analysed for chemical fingerprint using a Fourier-transform ion cyclotron resonance mass spectrometer (FT-ICR-MS; Bruker Daltonics) and in addition with a hyphenated UPLC-Orbitrap-Mass spectrometry system. These analyses will complement those carried out at IMDEA-Energia facilities.