Impact of height-to-diameter ratio on anaerobic digestion performance via stratified sludge bed analysis
Bibliographic record
Abstract
Effective reactor geometry design is crucial for optimizing anaerobic digestion of organic municipal solid waste. However, most previous studies have focused on overall performance or particle-scale characteristics, and the influence of height-to-diameter (H:D) ratios on vertical stratification of metabolic activities and microbial communities in sludge beds remains poorly understood. This study evaluates two reactors with H:D ratios of 3:1 (R T ) and 1:1 (R S ) to investigate how geometric design (H:D ratios) influences methane production, hydrolysis kinetics, and microbial community structure. Both reactors demonstrated stable operation. The R S reactor achieved a significantly higher methane yield of 504 ± 16 mL CH 4 /g VS, which was approximately 14 % higher than that of R T (441 ± 26 mL CH 4 /g VS) at an OLR of 3.45 ± 0.17 g VS/L/d ( P < 0.05). Hydrogenotrophic methanogenesis predominated in both reactors, with R S maintaining robust activity levels between 410 ± 37 and 364 ± 40 mg CH 4 -COD/(g VSS·d), whereas R T exhibited a decline from 473 ± 24 to 278 ± 13 mg CH 4 -COD/(g VSS·d) along the sludge bed from top to bottom. Hydrolysis rate constants in R S were consistent at both the upper (R S _high: 0.18 ± 0.02 day −1 ) and lower (R S _low: 0.17 ± 0.05 day −1 ) sections. In contrast, R T showed significant stratification, with the highest rate at the top (0.22 ± 0.02 day −1 ), decreasing by over 50 % to 0.11 ± 0.02 day −1 in the lower and 0.09 ± 0.004 day −1 in the middle sections. Microbial community analysis revealed shifts in dominant species with changes in sludge bed depth, affecting overall process efficiency. Overall, these quantitative differences highlight that lower H:D ratios promoted more uniform activity and higher methane production under current conditions, emphasizing the importance of reactor geometry optimization for enhanced anaerobic digestion performance. These findings provide practical guidance for reactor design and contribute new understanding the impact of spatial variations shaped by reactor configuration, bridging the gap between microbial ecology and engineering applications. • Low H:D (1:1) gave 14 % higher methane yield via better substrate accessibility • High H:D (3:1) showed a 50 % drop in hydrolysis rates from top to bottom layers • H:D ratio drove shifts of microbial community structure along sludge depth • Optimizing H:D ratio can improve spatial balance and overall reactor performance
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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) | 0.001 | 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".