From process fundamentals to engineering perspectives: A technical review on in-situ biogas upgrading via the hydrogenotrophic methanogenesis pathway
Bibliographic record
Abstract
Anaerobic digestion has become a key technology in municipal wastewater treatment plants for achieving neutral or positive energy balance. The anaerobic digestion process converts organic matter in the sludge into biogas containing around 50–75 % of methane (CH 4 ) and 25–50 % of carbon dioxide (CO 2 ), in which CH 4 is used for heat and electricity generation. Upgrading the biogas by removing CO 2 or converting it into CH 4 by injecting hydrogen (H 2 ) offers significant economic and environmental opportunities. Among different biotechnologies, the enrichment of hydrogenotrophic methanogenesis pathway (HMP) promotion offers a promising in-situ upgrading strategy that integrates well with existing anaerobic digestion processes, as it maximizes methane yields without requiring significant process changes. Previous studies have broadly addressed various biogas upgrading technologies, while those focused on HMP have primarily highlighted its benefits and limitations. However, research specifically examining in-situ biogas upgrading via HMP remains limited, despite its importance in reducing additional investments and operational complexity. This paper provides an engineering technical review for the process of in-situ biogas upgrading via HMP promotion. More specifically, it reviews comprehensively the process fundamental and reported improvements, the effect of different operational/environmental parameters on the anaerobic digester performance, and HMP synergy with other technologies/strategies for enhancing anaerobic digester performance. Key challenges such as H 2 supply, mass transfer limitations, and system scalability are also discussed to support future research and practical implementation.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".