Anaerobic digestion system
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA629428 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR11649175_1.fastq.gz | Fastqsanger.gz | |||
| SRR11649175_2.fastq.gz | Fastqsanger.gz | |||
| SRR11649176_1.fastq.gz | Fastqsanger.gz | |||
| SRR11649176_2.fastq.gz | Fastqsanger.gz | |||
| SRR11649177_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 28 |
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Project description:<p>The impact of ammonia on anaerobic digestion performance and microbial dynamics has been extensively studied, but the concurrent effect of anions brought by ammonium salt should not be neglected. This paper studied this effect using metabolomics and a time-course statistical framework. Metabolomics provides novel perspectives to study microbial processes and facilitates a more profound understanding at the metabolic level. The advanced statistical framework enables deciphering the complexity of large metabolomics data sets. More specifically, a series of lab-scale batch reactors were set up with different ammonia sources added. Samples of 9 time points over the degradation were analyzed with liquid chromatography-mass spectrometry. A filtering procedure was applied to select the promising metabolomic peaks from 1262 peaks, followed by modeling their intensities across time. The metabolomic peaks with similar time profiles were clustered, evidencing the correlation of different biological processes. Differential analysis was performed to seek the differences in metabolite dynamics caused by different anions. Finally, tandem mass spectrometry and metabolite annotation provided further information on the molecular structure and possible metabolic pathways. For example, the consumption of 5-aminovaleric acid, a short-chain fatty acid obtained from l-lysine degradation, was slowed down by phosphates. Overall, by investigating the effect of anions on anaerobic digestion, our study demonstrated the effectiveness of metabolomics in providing detailed information in a set of samples from different experimental conditions. With the statistical framework, the approach enables capturing subtle differences in metabolite dynamics between samples while accounting for the differences caused by time variations.</p>
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Project description:Continuous cultures in chemostats have proven their value in microbiology, microbial ecology, systems biology and bioprocess engineering, among others. In these systems, microbial growth and ecosystem performance can be quantified under stable and defined environmental conditions. This is essential when linking microbial diversity to ecosystem function. Here, a new system to test this link in anaerobic, methanogenic microbial communities is introduced. Rigorously replicated experiments or a suitable experimental design typically require operating several chemostats in parallel. However, this is labor intensive, especially when measuring biogas production. Commercial solutions for multiplying reactors performing continuous anaerobic digestion exist but are expensive and use comparably large reactor volumes, requiring the preparation of substantial amounts of media. Here, a flexible system of Lab-scale Automated and Multiplexed Anaerobic Chemostat system (LAMACs) with a working volume of 200 mL is introduced. Sterile feeding, biomass wasting and pressure monitoring are automated. One module containing six reactors fits the typical dimensions of a lab bench. Thanks to automation, time required for reactor operation and maintenance are reduced compared to traditional lab-scale systems. Several modules can be used together, and so far the parallel operation of 30 reactors was demonstrated. The chemostats are autoclavable. Parameters like reactor volume, flow rates and operating temperature can be freely set. The robustness of the system was tested in a two-month long experiment in which three inocula in four replicates, i.e., twelve continuous digesters were monitored. Statistically significant differences in the biogas production between inocula were observed. In anaerobic digestion, biogas production and consequently pressure development in a closed environment is a proxy for ecosystem performance. The precision of the pressure measurement is thus crucial. The measured maximum and minimum rates of gas production could be determined at the same precision. The LAMACs is a tool that enables us to put in practice the often-demanded need for replication and rigorous testing in microbial ecology as well as bioprocess engineering.
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Project description:The deinking pulp (DIP) is a main resource for paper making, and the wastewater from DIP process needs to be treated. Anaerobic biochemical technique has been widely applied in DIP wastewater treatment, due to the remarkable capability in reducing high chemical oxygen demand (COD). In this study, a mathematical simulation model was established to investigate the performance of a full-scale anaerobic biochemical system for treating DIP wastewater. The model was based on Anaerobic Digestion Model No. 1 (ADM1), which was modified according to the specific anaerobic digestion process for DIP wastewater treatment. The hydrodynamic behavior of a full-scale anaerobic biochemical system was considered in this model. The characteristics of the influent DIP wastewater were assessed, and then, the substrate COD proportion was divided successfully for the necessity of ADM1 applying. The Monte Carlo technique was implemented to distinguish the most sensitive parameters that influenced the model output indicators comprising effluent COD and biogas production. The sensitive parameters were estimated and optimized. The optimized value of k _m_pro is 12.02, K _S_pro is 0.35, k _m_ac is 4.26, K _S_ac is 0.26, k _m_h2 is 16.62, and K _S_h2 is 3.21 × 10-5. The model was calibrated with 150 days operation values measured in the field. The subsequent 100 days on-site values were used to validate the model, and the results obtained by the simulations were in good agreement. This study provides a meaningful and theoretical model guidance for full-scale wastewater anaerobic biochemical treatment simulation.
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