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Record W4241294857 · doi:10.32920/ryerson.14663721.v1

Modeling, simulation, and optimization of advanced oxidation technologies for treatment of polymeric wastewater

2021· preprint· en· W4241294857 on OpenAlexaff
Samira Ghafoori

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicWater Quality Monitoring and Analysis
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsBiodegradationEffluentWastewaterRendering (computer graphics)Aqueous solutionSewage treatmentSewageIndustrial wastewater treatmentAdvanced oxidation processEnvironmental scienceChemistryPulp and paper industryEnvironmental chemistryComputer scienceEnvironmental engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Most synthetic water-soluble polymers from industrial effluents are discarded into the aqueous environment. Due to their water solubility they can reach conventional sewage disposal systems and contaminate water resources as they are not amenable to biodegradation. Therefore, the possibility of rendering these materials to biologically manageable chemical compounds is a challenging task. The applications of advanced oxidation technologies (AOTs) for treating wastewater containing recalcitrant and inhibitory organics have risen drastically during the past few decades. AOTs mediate fast degradation or decomposition of the target organic compounds by producing free radical species. In this study, the photooxidative degradation of water-soluble polymers in wastewater is studied using different methods of AOTs. The experimental design, statistical analysis, and optimization of different processes of AOTs are investigated employing various methods of response surface methodology (RSM). Using RSM, the effects of different process parameters on the response function are determined. Also, a detailed mechanistic kinetic model is developed along with photoreactor design and modeling for a laboratory-scale batch recirculating photoreactor. Most synthetic water-soluble polymers from industrial effluents are discarded into the aqueous environment. Due to their water solubility they can reach conventional sewage disposal systems and contaminate water resources as they are not amenable to biodegradation. Therefore, the possibility of rendering these materials to biologically managable chemical compounds is a challenging task. The applications of advanced oxidation technologies (AOTs) for treating wastewater containing recalcitrant and inhibitory organics have risen drastically during the past few decades. AOTs mediate fast degradation or decomposition of the target organic compounds by producing free radical species. In this study, the photooxidative degradation of water-soluble polymers in wastewater is studied using different methods of AOTs. The experimental design, statistical analysis, and optimization of different processes of AOTs are investigated employing various methods of response surface methodology (RSM). Using RSM, the effects of different process parameters on the response function are determined. Also, a detailed mechanistic kinetic model is developed along with photoreactor design and modeling for a laboratory-scale batch recirculating photoreactor

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.030
GPT teacher head0.288
Teacher spread0.257 · 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 designSimulation or modeling
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

Citations0
Published2021
Admission routes1
Has abstractyes

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