Anaerobic digestion from the viewpoint of microbiological, chemical, and operational aspects — a review
Bibliographic record
Abstract
Anaerobic digestion (AD) is intrinsically a sequential complex chemical and biochemical process, and many factors (microbiological, operational, and chemical) can affect its performance. The great complexity of AD may lead to many serious problems (such as instability, long retention times, low efficiency, and high polluted supernatant) that prevent this technique from being widely used and commercialized. The aim of this paper is to review the present knowledge of the AD process in view of its microbiological, operational, and chemical aspects. Different groups of anaerobic microorganisms with specific growth conditions, physiological properties, and metabolic activities are involved in the AD process. Interactions of anaerobic microorganisms are incredibly complicated, and the effective performance of AD strongly depends on the balance of these relationships. The syntrophic interaction of acetogens and methanogens is the most important relationship in AD because acetogenesis and methanogenesis reactions under thermodynamic standard conditions are endergonic and naturally do not occur. Moreover, operational and chemical factors affect the anaerobes in certain ways. It is believed that there are many ambiguous points in AD that are not yet known. Therefore, by controlling and monitoring each of the microbiological, operational, and chemical parameters, AD performance may be enhanced.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.002 | 0.003 |
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".