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Record W3042318382 · doi:10.3390/en13143662

Anaerobic-Based Water Resources Recovery Facilities: A Review

2020· review· en· W3042318382 on OpenAlexafffund
Ahmed AlSayed, Moomen Soliman, Ahmed Eldyasti

Bibliographic record

VenueEnergies · 2020
Typereview
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSewage treatmentBiogasEnvironmental scienceWaste managementWastewaterAnaerobic exerciseEnergy recoveryAnaerobic digestionBiogas productionActivated sludgeResource recoveryEnvironmental engineeringMethaneEngineeringChemistryEnergy (signal processing)

Abstract

fetched live from OpenAlex

The concept of water resources recovery facilities (WRRFs) has gained more attention as a more sustainable substitute for the conventional activated sludge-based wastewater treatment plant (CAS-WWTPs). Anaerobic treatment is advantageous due to its lower energy use, limited sludge production, and higher recovery of the soluble chemical oxygen demand (sCOD) from the received wastewater. In this article, a critical review of the proposed scheme for the anaerobic-based WRRF (An-WRRFs) is presented which is preceded with discussion of CAS-WWTPs limitations. In addition, the evolution of anaerobic treatment from being viewed as wastewater treatment plant (WWTP) to WRRF is demonstrated. It is attained that, even though anaerobic WWTPs (An-WWTPs) have simple and low energy mainline and very limited sludge handling process, its limited removal and recovery capacity have been widely reported, especially in cold weather. On the other hand, in the An-WRRF, higher energy expenditures are employed by using membranes, dissolved methane recovery unit, and primary treatment (extra sludge handling). Yet, energy recovery in the form of biogas is notably increased, as well as the removal efficiency under moderate residence times. The three key challenges to be overcome are the low value of biogas, reducing the energy use associated with membranes, and maintaining high performance in full-scale, especially in cold weather.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.929
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.006

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 head0.031
GPT teacher head0.269
Teacher spread0.239 · 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; both teacher heads agree on what is shown here.

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

Citations14
Published2020
Admission routes2
Has abstractyes

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