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Record W4317434297 · doi:10.3390/pr11020326

Review on Digestibility of Aerobic Granular Sludge

2023· article· en· W4317434297 on OpenAlexaff
Mohamed Sherif Zaghloul, Asmaa M. Halbas, Rania Hamza, Elsayed Elbeshbishy

Bibliographic record

VenueProcesses · 2023
Typearticle
Languageen
FieldEngineering
TopicAnaerobic Digestion and Biogas Production
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsActivated sludgeEnvironmental scienceSewage treatmentBiomass (ecology)Waste managementWastewaterPopulationAerobic digestionSewage sludge treatmentAnaerobic digestionBiodegradationSewage sludgeBiogas productionBiogasPulp and paper industryBiologyEnvironmental engineeringAgronomyEngineeringEcology

Abstract

fetched live from OpenAlex

Full-scale wastewater treatment plants utilizing aerobic granular sludge technology are being built in many countries worldwide. As with all biological wastewater treatment plants, the produced waste biomass must be stabilized to protect the population, wildlife, and the environment. Digestion is usually used to break down the complex organics in the waste sludge; however, the digestibility of aerobic granular sludge still needs to be fully understood compared to the conventional activated sludge. This paper reviews the studies published on the digestibility of waste aerobic granular sludge to date. Studies comparing aerobic granular sludge and activated sludge in terms of composition, properties, and digestibility are highlighted. The impact of biological composition and physical properties on the digestibility of sludge is reviewed in terms of biomethane production and biodegradability. The effect of pre-treatment is also covered. Areas for future research are presented.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.022
GPT teacher head0.252
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations11
Published2023
Admission routes1
Has abstractyes

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