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Record W4385231582 · doi:10.11159/rtese23.116

Biodegradability Improvement of Water-Soluble-Polymers in Wastewater in a Continuous UV/H2 O2 Photoreactor

2023· article· en· W4385231582 on OpenAlexaff
Zahra Parsa, Mehrab Mehrvar, Ramdhane Dhib

Bibliographic record

VenueProceedings of the International Conference of Recent Trends in Environmental Science and Engineering · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality Monitoring and Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBiodegradationWastewaterPolymerPulp and paper industryChemistryChemical engineeringWaste managementMaterials scienceEnvironmental scienceProcess engineeringEnvironmental engineeringOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

Water-soluble polymers have a diverse range of applications from industrial raw materials to household product ingredients such as detergent capsules.However, the ubiquitous presence of these chemicals in both industrial and municipal wastewater streams is of concern.Although present in high and low concentrations in industrial and municipal wastewaters, respectively, these polymers are not easily biodegradable mainly due to their complex and lengthy structures.Moreover, their high solubility in water renders them invisible and often overlooked [1].Consequently, their persistence and potential accumulation in the environment could lead to long-term environmental risks.Thus, a better understanding of the environmental fate of non-biodegradable water-soluble polymers is essential for the development of effective management strategies to minimize their impact on the environment.Polyvinyl alcohol (PVA) is a well-known member of the water-soluble polymer family, and its unique physicochemical characteristics have led to its widespread use in various industrial applications, especially in textiles [2].As a representative of this group of chemicals, PVA has been selected for investigation in this study.Although PVA is generally considered non-toxic to humans and is even used in some medical packaging, it can pose significant risks to the environment, particularly in aquatic ecosystems [3].Its high foaming ability can lead to oxygen depletion in open water resources while its capacity to transport chemicals, including heavy metals, from soil to underground water may cause the accumulation of harmful contaminants in underground water resources.Therefore, it is essential to remove PVA from wastewater streams before discharge to mitigate its environmental impact [4].Advanced oxidation processes (AOPs) could effectively degrade a wide range of chemicals including those that are recalcitrant [5].The UV/H 2O2 process is a well-known AOP that has shown great promise for implementation in real wastewater treatment plants, primarily due to its simpler operation compared to other AOPs.The UV/H2O2 process employs UV radiation and hydrogen peroxide to produce extremely reactive hydroxyl radicals capable of decomposing organic compounds [6].The primary objective of this investigation is to assess the effectiveness of a UV/H2O2 photoreactor at a laboratory scale in enhancing the biodegradability of wastewater containing PVA.The ratio of biochemical oxygen demand to chemical oxygen demand (BOD5/COD) is used as a measure of wastewater biodegradability, with the goal of increasing it from 0.1 to over 0.5, resulting in an easily biodegradable effluent [7], [8].Additionally, the impact of key operational parameters, such as inlet PVA concentration, inlet H2O2 concentration, and hydraulic retention time on the BOD5/COD ratio, is investigated.A mathematical model is then developed based on the experimental data to predict and improve the biodegradability of the wastewater as a function of the operational variables.Response surface methodology (RSM) and Box-Behnken design (BBD) were employed to design experiments, develop a prediction model, and optimize process outcomes.The experimental results at the recommended optimal operating conditions validate the findings of the optimization study.Finally, the impact of partial treatment on reducing operating costs of AOP-based wastewater treatment was analyzed.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.328

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.022
GPT teacher head0.231
Teacher spread0.209 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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