Different aspects of biochar addition on semi-dry anaerobic digestion of organic fraction of municipal solid waste in continuous mode
Bibliographic record
Abstract
This study investigated the use of biochar, derived from a wood gasifier, in semi-dry anaerobic digestion of organic fraction of municipal solid waste (OFMSW). The experiment was conducted in three phases, without biochar and changing the hydraulic retention time (HRT) from 50 to 15 days until first acidification condition (on pH = 6.5), with biochar at an optimal concentration of 30 g/L in HRT= 20–10 day. Also, countermeasures for the acidified reactor with biochar during the dormancy period were investigated. The results demonstrated that adding biochar led to a rapid recovery of the acidified reactor, improved stability parameters, and removed foaming as a disturbance. Biochar addition (30 g/L) enhanced the organic loading rate (OLR) up to 11 kgVS/m3.day with an HRT of 20 days leading to specific methane production of 383 L/kgVS and a volumetric production increase of biomethane by 85 %. However, at higher OLRs with HRT of 10 days, acidification condition resurfaced leading to homogeneous foaming. Excess adding of biochar did not have significant treatment effects but necessitated a no-feeding period (about 45 days) and gentle stirring with long intervals for stable conditions. Overall, the use of biochar along with the OFMSW biogas plant was demonstrated to enhance production efficiency. • Biochar adding explored in semi-dry anaerobic digestion of OFMSW in a CSTR. • Biochar improves stability and 85 % increase in the volumetric methane production. • Increasing OLR to 11 kgVS/m 3 .day at HRT of 20 days. • Biochar addition had therapeutic properties and restored severe VFA inhibition. • Lowering HRT to 10 days caused homogeneous foaming with biochar.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".