Comparing sewage sludge vs. digested sludge for starting-up thermophilic two-stage anaerobic digesters: Operational and economic insights
Bibliographic record
Abstract
Despite advances in anaerobic digestion (AD), full-scale implementation faces significant challenges, particularly during the start-up phase, where inoculum selection is crucial. This study examines the impact of inoculum choice on the operational and economic performance of thermophilic digesters during the start-up phase. Methanogenic reactors R3 and R4 were inoculated with digested sludge (DiS) and diluted sewage sludge (DSS), respectively, and fed with hydrolyzed source-sorted organic fraction of municipal solid waste (SS-OFMSW) and thickened sewage sludge, which were processed in R1 and R2, serving as acidogenic reactors. A two-stage AD configuration was employed to mitigate inhibitory effects associated with the undigested inoculum (DSS). This approach enabled the establishment of methanogenic activity in R4 when the AD system is initiated with DSS. However, R3 outperformed R4, achieving 49 % of the feedstock's theoretical methane potential compared to 15 % in R4. Methane production and volatile solids (VS) processing costs in R4 were 18 and 3 times higher than in R3, respectively. R3's superior performance was attributed to DiS's diverse bacterial community, with over 66 % of genera involved in hydrolysis, volatile fatty acid production, and syntrophic methane production. In contrast, DSS was dominated by Trichococcus and Lactococcus (75.4 %), primarily involved in butyrate oxidation and lactate production. This study provides valuable insights into effective inoculum selection for the start-up of full-scale digesters.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".