Model-based optimisation and economic analysis to quantify the viability and profitability of an integrated nutrient and energy recovery treatment train
Bibliographic record
Abstract
In order to hasten the implementation of optimal, cost-effective and sustainable treatment trains for resource recovery from biowaste, a new nutrient recovery model (NRM) library has been developed and validated at steady state. It includes physico-biochemical mathematical models for anaerobic digestion, struvite precipitation and ammonia stripping and absorption as ammonium sulfate. The present paper describes the use of the NRM library to establish the operational settings of a sustainable and cost-effective treatment scenario with maximal resource (nutrients and biogas) recovery and minimal energy and chemical requirements. Under the optimised conditions and assumptions made, potential financial benefits for a large-scale anaerobic digestion and nutrient recovery project treating 2700 m3/d of pig manure were estimated at US$2·8–6·5/m3 based on net variable cost calculations, or an average of ∼$2/(m3 year), equivalent to $40/(t total solids year), over 20 years in the best case when also taking into account capital costs. Hence, it is likely that in practice a full-scale zero-cost biorefinery for nutrient and energy recovery from manure can be constructed. As such, this paper demonstrates the potential of the NRM library to facilitate the implementation of sustainable nutrient and energy (biogas) recovery treatment trains for biowaste valorisation.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".