Anaerobic Process Intensification With Vacuum and Combined Pretreatment Technologies
Bibliographic record
Abstract
Anaerobic digestion and fermentation are essential processes for transforming wastewater treatment plants into robust energy, nutrient, and water recovery facilities. However, these processes' slow microbial growth rate and low yields limit their application to larger facilities. To improve their efficiency, intensifying anaerobic digestion and fermentation (to a lesser extent) has been the subject of numerous research studies. This dissertation explored the principle of vacuum evaporation for combining sludge thickening, decoupling of hydraulic and retention times, and nutrient recovery into a single treatment unit. Gaps, including the efficiency of vacuum sludge thickening at different solids content, its impact on anaerobic bacteria and the overall physio and biochemical processes, were investigated. In batch fermentation, establishing 400 mbar of absolute pressure led to 49% solubilization of primary sludge in 72 hours compared to the conventional fermentation in which 11% solubilization was achieved. During fermentation of TWAS at 400 mbar, deterioration of solubilization and VFA yield was observed. The vacuum technology was used to decouple the hydraulic and solids retention time (HRT/SRT) in semi-continuous fermentation of mixed sludge. At SRT of 3 days and HRT of 1.5 days, the vacuum-assisted fermenter yielded 660 mg sCOD/g VSS compared to the conventional fermenter which achieved 513 mg sCOD/g VSS with equal HRT and SRT of 3 days. Additionally, 50% of the ammonia produced in the fermenter was extracted in the condensate with no chemical alterations. The results led to the development of IntensiCarb, a patent-pending technology that combines sludge thickening, digestion, and dewatering into a single unit. This dissertation also investigated the use of combined chemical-mechanical and chemical-chemical pretreatment technologies with free nitrous acid (FNA) to enhance the anaerobic digestion of TWAS. FNA is an environment-friendly chemical that can be produced in wastewater treatment facilities. In this study, FNA concentrations from 0.7 to 2.8 mg HNO2-N/L were combined with ultrasonication energy input from 600 to 3,000 kj/kg TS to explore the feasibility of lower energy input for effective sludge disintegration and enhanced anaerobic digestion. The energy input for ultrasonication could be halved from 3,000 kj/kg TS to 1,500 kj/kg TS by adding 2.8 mg HNO2-N /L while improving solubilization by 43% and methane yield by 18%. FNA was also combined with hydrogen peroxide, an oxidizing chemical that has shown effectiveness in improving biodegradability at low pH. The results showed that the pretreatment combination with the lowest chemical concentrations of 0.7 mg HNO2-N /L and 25 mg H2O2/g TS was the most effective for maximizing methane yield, increasing it by 50% compared to the untreated TWAS.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".