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Record W4415703628 · doi:10.2166/wst.2025.155

Review of full-scale advanced anaerobic digestion in North America

2025· article· en· W4415703628 on OpenAlexaffabout
Farokh Laqa Kakar, Ahmed El-Sayed, Andrew H. Marcus, mahdis zareie, John Norton, Kevin A. Jankowski, Christopher Cox, Matthew Seib, Chris Peot, T. F. Morse, diana smillova, Chris Müller, Elsayed Elbeshbishy

Bibliographic record

VenueWater Science & Technology · 2025
Typearticle
Languageen
FieldEngineering
TopicAnaerobic Digestion and Biogas Production
Canadian institutionsToronto Zoo
Fundersnot available
KeywordsBiosolidsAnaerobic digestionSewerageBiogasMetropolitan areaResource recoveryEffluentWater quality

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.250
Threshold uncertainty score0.250

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.217
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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