MétaCan
Menu
Back to cohort
Record W4321782826 · doi:10.1680/jenes.22.00061

Reduction of chemical oxygen demand of vinasse using sugar cane bagasse ash geopolymer

2023· article· en· W4321782826 on OpenAlexvenueno aff
Thabo Falayi

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2023
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsVinasseBagasseAdsorptionChemical oxygen demandChemistrySaturation (graph theory)Volumetric flow rateChromatographyPulp and paper industryWastewaterWaste managementOrganic chemistryFermentation

Abstract

fetched live from OpenAlex

The aim of this study was to determine the use of sugar cane bagasse ash geopolymer (SCBAG) as an adsorbent for organic compounds to reduce the chemical oxygen demand (COD) of vinasse. The effects of solid loading, time and temperature on batch adsorption were investigated, while the effects of bed height and flow rate were investigated through column studies. The adsorption of organic compounds onto SCBAG could be modelled well using the Langmuir isotherm and pseudo-second-order kinetics. The maximum batch adsorption capacity was 738 mg/g at 298.15 K after 5 h of adsorption. The column studies showed that the highest COD reduction of 81% could be achieved using a flow rate of 2.5 ml/min and a bed height of 13 cm. These conditions gave a dynamic uptake of 107 458 g and a breakthrough time of 600 min. The column adsorption could be best described using the Bohart–Adams model, giving a correlation coefficient of 0.98, a Bohart–Adams rate constant of 3.05 × 10 −8 l/(mg min) and a saturation concentration of 6.93 × 10 7 g/l. The SCBAG could be regenerated and be used as an adsorbent in three cycles without significant loss in adsorption capacity.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.336

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 head0.006
GPT teacher head0.203
Teacher spread0.197 · 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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Environmental Engineering and ScienceSame topicEnhanced Oil Recovery TechniquesFrench-language works237,207