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Record W3018772926 · doi:10.3390/w12041200

A Field Pilot Study on Treating Groundwater Contaminated with Sulfolane Using UV/H2O2

2020· article· en· W3018772926 on OpenAlexafffund
Linlong Yu, Sobhan Iranmanesh, Ian Keir, Gopal Achari

Bibliographic record

VenueWater · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Treatment and Disinfection
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSulfolaneGroundwaterEnvironmental scienceContaminationEnvironmental engineeringHydrogen peroxideEnvironmental chemistryChemistryGeologyGeotechnical engineeringSolvent

Abstract

fetched live from OpenAlex

Sulfolane is an emerging contaminant in the groundwater and soil nearby gas plants, which has attracted much attention from many researchers and regulatory agencies in the past ten years. In this paper, a field pilot-scale ultraviolet (UV)/hydrogen peroxide (H2O2) system was investigated for treating sulfolane contaminated groundwater. Different groundwater, as well as different operational parameters such as influent sulfolane concentration, H2O2 dosage, and water flow rates, were studied. The results showed that a pilot-scale UV/H2O2 system can successfully treat sulfolane contaminated groundwater in the field, although the presence of iron and other groundwater limited the process efficiency. The lowest electrical energy per order of reduction for treating sulfolane in groundwater by using the pilot-scale UV/H2O2 system was 1.4 kWh m−3 order−1. The investigated sulfolane initial concentrations and the water flow rates did not impact the sulfolane degradation. The enhancement of sulfolane degradation in an open reservoir by adding ozone was not observed in this study. Furthermore, an operational cost model was formulated to optimize the dosage of H2O2, and a stepwise procedure was developed to determine the power necessary of the UV unit.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.563
Threshold uncertainty score1.000

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)0.0010.001

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 head0.230
Teacher spread0.195 · 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.

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

Citations13
Published2020
Admission routes2
Has abstractyes

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