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Record W4404447360 · doi:10.1016/j.jece.2024.114765

Prediction of fixed-bed GAC filter consumption for complex VOC mixtures using a parsimonious competitive adsorption model and a single batch test

2024· article· en· W4404447360 on OpenAlexaff
Leonardo Magherini, Serena Barbero, Carlo Bianco, Marios A. Ioannidis, Rajandrea Sethi

Bibliographic record

VenueJournal of environmental chemical engineering · 2024
Typearticle
Languageen
FieldChemical Engineering
TopicOdor and Emission Control Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAdsorptionFilter (signal processing)Consumption (sociology)Process engineeringChemistryEnvironmental scienceChromatographyMaterials scienceChemical engineeringComputer scienceEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

The design of fixed-bed adsorbers for the treatment of drinking water and wastewater contaminated with complex mixtures of organics is a tedious procedure that requires detailed description – using a combination of experiments and modelling - of the individual and competitive adsorption behaviour of many dissolved species. To address the need for an experimental and modelling workflow that is both accurate and parsimonious, this study systematically evaluates the treatment of groundwater contaminated with 13 competing chlorinated hydrocarbons using a fixed-bed of granular activated carbon (GAC). Firstly, a single batch test, interpreted using a competitive Freundlich adsorption model, was demonstrated to be an effective alternative to multiple single-component tests for determining single-compound isotherm parameters. The isotherm parameters of the 13 chlorinated hydrocarbons were estimated from the batch test using both the Ideal Adsorbed Solution Theory (IAST) and the simplified IAST (SIAST) models, with the latter model emerging as more parsimonious and robust based on Akaike’s information criterion. Then the experimental breakthrough and solid phase concentrations of the 13 compounds in a fixed-bed system were accurately predicted by implementing an equilibrium competitive adsorption transport model. The good agreement between the fixed-bed adsorption data and the competitive model confirmed and validated the whole approach. Compared to standard approaches based on mass balance considerations or rapid small-scale column tests, the proposed procedure enables quicker and easier scaling up of a multispecies sorption behaviour from a single batch test, thus reducing the experimental and modelling effort needed for the dimensioning of full-scale adsorbers for treating many-component mixtures of contaminants in aqueous matrices. • A single batch test can estimate adsorption isotherms for 13 chlorinated compounds. • The SIAST model offers a simpler and robust alternative to IAST for GAC adsorbers. • Competitive adsorption in fixed-beds is well-predicted by SIAST transport models. • A new approach enables scaling-up multispecies adsorption from a single batch test. • Workflow reduces efforts for designing full-scale adsorbers for water treatment.

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 score0.541
Threshold uncertainty score0.636

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)0.0000.000

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 head0.031
GPT teacher head0.227
Teacher spread0.196 · 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
Published2024
Admission routes1
Has abstractyes

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