Start-Up Strategies for Thermophilic Semi-Continuous Anaerobic Digesters: Assessing the Impact of Inoculum Source and Feed Variability on Efficient Waste-to-Energy Conversion
Bibliographic record
Abstract
Anaerobic digestion (AD) has gained broad interest as a sustainable organic waste management and resource recovery method. However, the complexity of the AD process could pose serious risks in real-scale applications. One of the most critical phases in the operation of AD systems is the start-up phase, including the seeding strategy of the digesters. This study aims to assess the effect of digestate post-treatment before seeding on the start-up of thermophilic AD systems. Two anaerobic digesters (R1 and R2) were started using two different thermophilic inocula and were kept operational for 17 weeks under identical conditions. Lab digesters were seeded with digestates sampled from a thermophilic full-scale reactor (R2) and a post-treatment mesophilic tank (R1). The start-up strategies exhibited satisfactory stability and high productivity, achieving mean weekly methane-based biodegradability rates of 61 and 64% of the feed’s theoretical biomethane potential (BMP), respectively, in R1 and R2. However, R2 showed greater resilience to high and sudden organic loads applications, making it more suitable for rapid and aggressive start-ups. These results are expected to assist full-scale anaerobic digester operators in selecting an appropriate inoculum based on the characteristics of its source.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".