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Record W4229878662 · doi:10.32920/ryerson.14648301

Combined UV-C/H2O2/VUV processes for the treatment of actual slaughterhouse wastewater

2021· preprint· en· W4229878662 on OpenAlexaff
Kambiz Vaezzadeh Naderi

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicWater Quality Monitoring and Analysis
Canadian institutionsToronto Metropolitan University
FundersSharif University of Technology
KeywordsResponse surface methodologyWastewaterEffluentResidualTotal organic carbonBox–Behnken designChemistrySewage treatmentPulp and paper industryEnvironmental engineeringEnvironmental scienceChromatographyMathematicsEnvironmental chemistryEngineeringAlgorithm

Abstract

fetched live from OpenAlex

In this study, a three-factor, three-level Box-Behnken design with response surface methodology and quadratic programming were used to maximize the total organic carbon (TOC) removal and minimize the H2O2 residual in the effluent of the combined UV-C/H2O2/VUV system for the treatment of actual slaughterhouse wastewater. The initial TOC concentration (TOCo), the initial concentration of H2O2, and the irradiation time were the three independent variables studied in the design of experiments. The multiple response approach was used to obtain desirability response surfaces at the optimum factor settings. Thus, the optimum conditions to achieve a maximum TOC removal of 46.19% and a minimum H2O2 residual of 1.05% were TOCo of 213 mg/L, H2O2,o of 450 mg/L, and irradiation time of 9 min. The obtained optimal operating conditions were validated with an additional test. Consequently, maximum TOC removal of 45.68% and minimum H2O2 residual of 1.03% were obtained experimentally, confirming the reliability of the statistical model. Keywords: Slaughterhouse Wastewater; Wastewater Treatment; UV-C/H2O2/VUV, Combined Processes; Design of Experiments; Optimization.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.806
Threshold uncertainty score0.635

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.040
GPT teacher head0.274
Teacher spread0.235 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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