Application of Hydrothermal Pretreatment for Improving the Fermentation and Anaerobic Digestion Processes for Resource Recovery from Municipal Solid Waste
Bibliographic record
Abstract
This thesis comprehensively investigated the hydrothermal pretreatment (HTP) parameters' impact on the sludge disintegration, volatile fatty acid (VFA), and methane production. It has also studied different process configurations and the development of a novel thermal-hydrolysis and vacuum fermentation for high-grade VFA recovery. Results confirmed the significant impact of HTP on all process parameters and a direct correlation between the retention time, sludge pH, and solid content with process parameters. The highest COD solubilization and solid destruction percentage were observed for the retention time of 30 minutes, pH 10, and solid content (SC) of 10%. It was found that fermentative bacteria prefer consuming sludge with lower solid content while methanogenesis produced higher methane consuming sludge with SC of 16%. The combination of thermal hydrolysis with chemical pretreatments also played a crucial role in resource recovery. Combining free nitrous acid and thermal hydrolysis demonstrated the highest methane production efficiency compared to the combination with typical acetic acid and sodium hydroxide. Moreover, the impact of HTP parameters on mesophilic and thermophilic fermentation was investigated and ultimately, 10-15% higher solubilization and VFA production occurred during thermophilic fermentation compared to mesophilic. Further, different process configurations of HTP with fermentation and anaerobic digestion were examined while hydrothermal pretreatment was found to have superior results for COD solubilization (25.5%), VFA production (65 g Acetate/L), and methane production (230 mL CH /g 4COD added) compared to the hydrothermal inter-treatment (HIT). Mathematical models developed by incorporating more than 100 sample points from studies mentioned above further defined the correlation of HTP and process response parameters. The best conditions of HTP from observations in the first phase were utilized to pretreat TWAS for the next phase. The pretreated TWAS and PS mixture was then used as the substrate of the novel integrated thermal hydrolysis and vacuum fermentation. Vacuum fermentation integrated with HTP demonstrated the highest (45%), VFA yield (0.32 g COD/g VSS added), and methane yield (267 mL CH4/g TCOD added) compared to the systems without pretreatment and vacuum. Microbial community analysis confirmed that HTP and vacuum application significantly impact the bacterial community's diversity. A bigger population of fermenters such as coprothermobacteraeota was observed in vacuum systems and for pretreated samples.
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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".