Enhanced Primary Sludge Fermentation and Anaerobic Digestion for Integrated Carbon Upgrade and Resource Recovery
Bibliographic record
Abstract
The emerging paradigm shift towards renewable resource recovery, and energy minimization in municipal wastewater treatment plants (WWTPs) coupled with increased concern over nutrients-related eutrophication accelerated the development of biosolids treatment technologies for simultaneous waste minimization, resource recovery, and carbon upgrade. Biological nutrient removal (BNR) processes, often need excess carbon source to meet stringent quality standards. Despite successful use of sludge fermentation liquid to enhance the BNR, different techniques, are applied to improve the low conversion yield of fermentation and optimize resource recovery. In this context, insights on the impact of two commonly used primary treatment techniques (i.e. conventional primary clarifier and the emerging rotating belt filtration (RBF) technology) on the single and integrated anaerobic fermentation and digestion of wastewater biosolids was investigated in this study. Techno-economic assessment and optimization to simultaneously maximize volatile fatty acids (VFAs), and biomethane recoveries, and further application of internal carbon source to enhance the BNR process using a plant-wide approach, were also among the main objectives of this project.\nThe fate of cellulose study revealed that roughly 80% of the raw wastewater cellulose was removed in either of the primary treatment options, while represented 35%, and 17% of the total suspended solids (TSS) in the RBF and primary clarifier sludges, respectively. Cellulose was biodegradable irrespective of the biological process configuration and tested Solids retention times (SRTs), with effluent concentrations of about 2-3 mg/L.\npH-controlled fermentation was effective in improving the VFA yields by up to 93% and 72% at pH 9, for RBF and primary sludges, respectively. Furthermore, pH 6 was proposed as optimum considering significant enhancement in VFA production, while also lowering the amount of consumed chemicals. Interrelated impact of enzyme, temperature, and SRT on the enhancement of primary and RBF sludges fermentation showed a positive impact of enzyme dose as well as temperature and SRT on the VFA and soluble COD production. Cellulase increased the VFA yields by up to 36% and 86% for primary and RBF sludges, respectively. Response surface methodology (RSM) model depicted the existence of an optimum in the high-enzyme (1%-1.5%), long-SRT (3d-4d) range. The economic viability of fermentation at full scale was confirmed by proving that VFA recovery could save up to 7.2±2.0% (RBF), and 7.6±2.7% (PS) of the overall sludge disposal costs. Integration of fermentation and anaerobic digestion negatively impacted the biogas production of the residual fermented solids by 8.4% and 12.7%, compared to fresh primary and RBF sludges due to the VFA recovery, respectively; but still economically outperformed the single stage digestion under all tested scenarios.\nBoth primary and RBF sludge fermented liquid (SFL) were effective in enhancing BNR. Removal efficiencies in the rectors were reached up to 57% (total nitrogen) and 92% (total phosphorus), upon supplementation with the SFL. Effluent nitrogen and phosphorus of the reactors were closely matched for the two trains in the range of 15± 6 mg N/L, and 0.5 ± 0.3 mg P/L, respectively. A case study incorporating experimental results into a plant-wide model showed a moderate (3.4%-8.5%) improvement in the effective COD:N and COD:P ratios (compared to the original feed); but a significant increase in readily biodegradable (rbCOD) and VFAs (2.5-6.1 times) in the combined feed could be achieved by utilization of fermeners.
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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".