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Record W4313414806 · doi:10.3390/pr11010075

Feasibility Study of Applying Anaerobic Step-Feeding Mode for the Treatment of High-Strength Wastewater in Granular Sequencing Batch Reactors (GSBRs)

2022· article· en· W4313414806 on OpenAlexafffund
Elsayed Mady, Jan A. Oleszkiewicz, Qiuyan Yuan

Bibliographic record

VenueProcesses · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSequencing batch reactorGranulationAnaerobic exerciseNutrientNitrateWastewaterNitrificationPulp and paper industryChemistryNitriteAmmoniumDenitrificationNitrogenEnvironmental engineeringMaterials scienceEnvironmental scienceBiologyComposite material

Abstract

fetched live from OpenAlex

This study investigated the feasibility of applying an anaerobic step-feeding strategy to enhance the performance of granular sequencing batch reactors (GSBRs) in terms of operational stability of the cultivated mature granules and nutrient removal efficiencies. Two identical 5 L reactors were operated with a total cycle time of 8 h. GSBRs were operated with high-strength synthetic wastewater (COD = 1250 ± 43, ammonium (NH4-N) = 115.2 ± 4.6, and orthophosphate (PO4-P) = 17.02 ± 0.9 mg/L) for 360 days through three stages: (1) Cultivation, 125 days (>2.1 mm); (2) Maturation, 175 days (>3 mm); (3) alternate feed loading strategy for R2 only for 60 days (anaerobic step-feeding). The granulation process, the physical properties of the granules, the nutrients, and the substrate removal performance were recorded during the entire operational period. For the cultivation and maturation stages, both reactors followed the fast single feeding mode followed by anaerobic mixing, and the results indicated a strong correlation between R1 and R2 due to the same working conditions. During the cultivation stage, adopting high organic loading rate (OLR) at the reactor start-up did not accelerate the formation of granules. Removal efficiency of PO4-P was less than 76% during the maturation period, while it exceeded 90% for COD, and was higher than 80% for NH4-N without effect of nitrite or nitrate accumulations due to simultaneous nitrification–denitrification. After changing filling mode for R2 only, there was unexpected deterioration in the performance and a rapid disintegration of the matured granules (poor settleability) accompanied by poor effluent quality due to high content of suspended solids because of applying selection pressure of short settling time. Consequently, GSBRs operation under the effect of fast single feeding mode followed by anaerobic mixing favors stable long-term granule stability.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.034
GPT teacher head0.263
Teacher spread0.229 · 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 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

Citations5
Published2022
Admission routes2
Has abstractyes

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