Advances in the biological removal of sulphides from aqueous phase in anaerobic processes: A review
Bibliographic record
Abstract
In this paper, we review the latest developments in biological methods used in the removal of hydrogen sulphide, present in the liquid phase in anaerobic reactors. The toxicity of H2S to methane-forming microorganisms and the problems caused by the presence of this compound in the biogas generated during this process, as well as the main causes of hydrogen sulphide generation in anaerobic processes of wastes are also reviewed. We especially discuss the fundamentals in applying micro-aerobic conditions to remove dissolved hydrogen sulphide from the aqueous phase of an anaerobic reactor. The alternative technology of simultaneous removal of sulphide, nitrate, and organic matter is under recent investigation. Therefore, this review paper includes a study and analysis of the microbiological basis of this technology, the physical and chemical factors that influence the process and the potential application of this technology on different types of wastewaters and situations. Also considered are the fundamentals of both biofilm reactors and microbial fuel cells desulphurization. Because relatively few studies on modeling desulphurisation processes are available, we discuss the advances made in that area.
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 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".