Techno-economic analysis of nutrient recovery from urine: Centralized treatment of hydrolyzed urine vs. decentralized treatment of fresh urine
Bibliographic record
Abstract
The centralized process integrating “Thermal NH 3 stripping → Na-chabazite adsorption → Struvite precipitation” has been proposed for nutrient recovery from hydrolyzed urine. Meanwhile, a decentralized approach involving Na-chabazite and biochar adsorption has been suggested for fresh urine, followed by urea hydrolysis and the subsequent centralized integration of struvite precipitation and thermal stripping. However, a systematic comparison of nutrient recovery processes for fresh and hydrolyzed urine, evaluating both technical viability and financial feasibility, is lacking. This study addresses the gap by thoroughly examining both scenarios over a 30-year project, using Université Laval as a case study. It provides a comprehensive roadmap for techno-economic assessment, offering guidance for evaluating nutrient recovery processes prior to scaling up. The decentralized process achieved higher recovery efficiencies for nitrogen and phosphorus, at 89.4 % and 98.7 %, respectively. Financially, the decentralized scenario demonstrated its advantage in the lower initial investment requirement, thereby generating higher gross profits compared to the centralized scenario. As a result, it is projected to reach the break-even point in the 21st year, demonstrating its potential economic feasibility. Sensitivity analysis indicated that a 20 % increase in urine inflow rate and the price of urea-enriched biochar could further enhance the economic viability of both processes. Beyond financial considerations, both scenarios have the potential to reducing the contaminant loading rate in the downstream wastewater treatment plants and promote nutrient recovery and recycling. • Compared centralized/decentralized WRRFs in synthetic & real urine • Urea recovery from fresh urine was compared to NH 4 + recovery from hydrolyzed urine. • Decentralized WRRFs requires less capital expenditure than centralized WRRFs. • WRRFs reduced the contaminant loading rate in downstream WWTPs. • Sensitivity analysis: the impact of biochar capacity & price on economic viability
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".