Review of full-scale advanced anaerobic digestion in North America
Bibliographic record
Abstract
This study presents a comprehensive analysis of the distribution and performance of advanced anaerobic digestion (AD) technologies across the United States and Canada. The study reveals that temperature-phased anaerobic digestion is the most prevalent technology, with 20 water resource recovery facilities (WRRFs) adopting it, followed by acid-methane AD and thermal hydrolysis process. The distribution analysis indicates that 59% of the projects have a plant capacity of 40-400 million liters per day, and 30% of the projects have more than 20 AD reactors. The biosolids classification shows that Class A biosolids constitute 45%, while Class B biosolids make up 51% of these projects. Case studies from Madison Metropolitan Sewerage District, City of St Petersburg, City of Montpelier WRRF, Metro Water Recovery, and DC Water highlight the financial impacts, including cost savings and increased revenue from high-strength biosolids. The findings underscore the variability in the effectiveness of AD technologies and the importance of cost and operational efficiencies in technology selection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".