Valorization of waste aerobic granular sludge: Exploring the recovery of tyrosine, phenylalanine, and other high-value products
Bibliographic record
Abstract
• Methane content of aerobic granular sludge (AGS)-derived biogas is 56.7–59.5 %. • Phosphorus recovery is via EBPR & phosphorus precipitation within the granule matrix. • Aerobic granules typically contain approximately 20–30 % ALE. • Optimization required for enhanced biosynthesis of tyrosine and phenylalanine. • Efficient and environmentally friendly recovery protocols are required. The aerobic granular sludge (AGS) biotechnology has emerged as a compact, sustainable, resilient, and highly efficient technology for wastewater treatment. Beyond wastewater treatment, AGS provides significant potential for resource recovery, aligning with circular economy principles. This review explores the recovery of six key resources from AGS systems: phosphorus, alginate-like exopolysaccharides (ALE), tryptophan, tyrosine, phenylalanine, and biogas. Phosphorus recovery pathways include enhanced biological phosphorus removal, biologically induced precipitation as hydroxylapatite (Ca 5 (PO 4 ) 3 (OH)), and struvite (MgNH 4 PO 4 ·6H 2 O) formation. ALE extraction techniques and optimization strategies are examined for their industrial applications. Biogas production from waste granules can be improved through co-digestion and pre-treatment methods such as steam explosion. Tryptophan production in the aerobic granule matrix can be enhanced through optimized operational parameters, while extraction and quantification are achieved using high-performance liquid chromatography. Tyrosine and phenylalanine, recently identified in the aerobic granule matrix, have a wide range of industrial applications including feed and food supplement, production of medicines, in agriculture for postharvest preservation, and as a raw material for other chemical products. Their biosynthesis in the aerobic granule matrix can be enhanced via process optimization and approaches such as quorum sensing. Addressing current AGS challenges such as extended start-up times when using low-strength wastewater and operational issues with industrial wastewater is critical for maximizing AGS performance. AGS exemplifies innovative biotechnology for sustainable wastewater treatment and resource recovery, leading the way to attaining a circular economy in wastewater management.
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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".