Combining a solid-state submerged fermenter with bioelectrochemically enhanced anaerobic digestion (BEAD) process for enhanced methane (CH4) production from food waste: Effects of the organic loading rates and applied voltages
Bibliographic record
Abstract
This study investigated the performance of a two-stage reactor system comprising a solid-state submerged fermenter (3SF) and a bioelectrochemically enhanced anaerobic digestion (BEAD) process. Initially, the 3SF performance was optimized by evaluating the impact of leachate recirculation rates (LRRs) of 34 L/day, 50 L/day, and 75 L/day. The maximum hydrolysis and acidification yields of 635 ± 10 gsCOD/kgVS added and 531 ± 8 gsCOD SCFA /kgVS added were observed at an LRR of 75 L/day, demonstrating the significance of operating at higher LRRs. Furthermore, the performance of the BEAD was assessed at progressing organic loading rates (OLRs) of 3–12 gsCOD/L/day, with a fixed applied voltage of 1.4 V using FW leachate. At higher OLRs of 7–12 gsCOD/L/day, BEAD outperformed AD-control, achieving better sCOD removal efficiencies and CH 4 yield ranging from 94 % to 88 % and 0.35–0.30 L/gsCOD removed observed compared to 91–81 % and 0.28–0.23 L/gsCOD removed in the AD-control. Although performance at low OLRs of 3–6 gsCOD/L/day was similar across systems, however, BEAD achieves steady-state conditions quickly, demonstrating improved operational stability across a wider OLR range of 3–12 gsCOD/L/day compared to the 3–6 gsCOD/L/day in the AD-control. The impact of the applied voltages of 1 V, 0.6 V, and 0 V was also investigated at OLRs of 3 and 12 gsCOD/L/day. Notably, applied voltage had a more pronounced impact at a higher OLR, enhancing energy gain from 0.09 to 0.27 Wh/gsCOD removed compared to 0.03–0.066 Wh/gsCOD removed at the lower OLR of 3 gsCOD/L/day. Microbial community analysis revealed a higher relative abundance of H₂-producing butyrate-oxidizing bacteria and exoelectrogens in the BEAD system, indicating the coexistence of both conventional and exoelectrogenic CH 4 production pathways. Overall, the proposed 3SF-BEAD system enhanced the CH 4 yield, shortened the operational cycle, and enhanced stability at higher OLRs. • 3SF performance was improved by 12 % at a higher LRR of 75 L/day. • BEAD performance was better than AD-control at higher OLRs of 7–12 gsCOD/L/day. • CH 4 yield in the BEAD was 30–63 % higher than AD-control. • Applied voltage led to 11-fold more energy gain at a higher OLR of 12 gsCOD/L/day. • The operational cycle of 3SF-BEAD was 2-fold shorter than conventional systems.
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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".