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Record W4220971746 · doi:10.2166/wst.2022.093

Energy and reliability analysis of wastewater treatment plants in small communities in Ontario

2022· article· en· W4220971746 on OpenAlexafffundabout
Rania Hamza, Mohamed F. Hamoda, Mohammad Elassar

Bibliographic record

VenueWater Science & Technology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Reuse
Canadian institutionsToronto Metropolitan University
FundersMitacs
KeywordsRotating biological contactorEnvironmental sciencePerformance indicatorWastewaterEnvironmental engineeringEnergy consumptionSewage treatmentPollutantBenchmarkingPopulationActivated sludgeReliability (semiconductor)EngineeringAerationWaste managementEcologyBiologyBusiness

Abstract

fetched live from OpenAlex

/d. Five different treatment technologies were investigated, namely, rotating biological contactor (RBC), sequencing batch reactor (SBR), membrane bioreactor (MBR), lagoon, and extended aeration activated sludge process (EAAS). Energy benchmarking based on key performance indicators (KPIs) was used to quantify the specific consumption of energy in WWTPs per unit of the pollutant removed. The overall annual electrical energy consumption was correlated to the volume of treated wastewater, the population equivalent, and the amounts of TSS and BOD removed. The RBC plants showed a distinctive advantage for all energy KPIs assessed, while SBR plants yielded the highest values of energy KPIs. Analyses of the expected percentage of compliance with discharge standards and the coefficient of reliability (COR) based on the WWTPs' performance records showed that few WWTPs were able to achieve reliability levels over 95%, considering the mandated discharge standards under the current operating and maintenance conditions. Within each technology, the treatment train, operating conditions, maintenance level, and age of infrastructure were important elements that contributed to the large variability observed.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.548
Threshold uncertainty score0.756

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.013
GPT teacher head0.197
Teacher spread0.184 · 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 designObservational
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

Citations17
Published2022
Admission routes3
Has abstractyes

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