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Record W2026735185 · doi:10.1002/cjce.20114

Optimization by response surface methodology (RSM) for toluene adsorption onto prepared acid activated clay

2008· article· en· W2026735185 on OpenAlexvenueno aff
Abdelfattah Amari, Abdelaziz Gannouni, Mohamed Chlendi, Ahmed Bellagi

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2008
Typearticle
Languageen
FieldChemical Engineering
TopicOdor and Emission Control Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsTolueneAdsorptionResponse surface methodologyCentral composite designMaterials scienceRaw materialChemistryChromatographyAnalytical Chemistry (journal)Organic chemistry

Abstract

fetched live from OpenAlex

Abstract Adsorption of toluene onto acid activated clay was carried out. Modified clay was prepared by acid attack (H 2 SO 4 ) on raw material. Response surface methodology based on a 2‐level, 4‐variables central composite orthogonal design was used to evaluate the effects of important parameters on the adsorption of toluene on to activated clay. Temperature (53.8–96.2°C), contact time (0.57–6.93 h), mass ratio of liquid/solid (3.38–7.62) and strength of acid (7.75–57.24%) were chosen as process variables for the optimization. Of these parameters, temperature reaction and time had greater impact on toluene adsorption than did the other parameters. Analysis of variance (ANOVA) shows a good agreement between theoretical analysis and experimental data. The validity of model is verified by an experiment at the optimum conditions. The optimum conditions for the maximum adsorption of toluene onto activated clay are: temperature of 96.2°C, a contact time of 6.93 h, a liquid/solid ratio of 5.98 and strength of acid of 32.94%. Since the predicted values and the actual experimental value obtained for the maximum adsorption of toluene are within 95% confidence intervals, the final model is considered valid and has satisfactory predictive ability.

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)0.003
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 score0.160
Threshold uncertainty score0.604

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
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 head0.034
GPT teacher head0.249
Teacher spread0.215 · 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

Citations21
Published2008
Admission routes1
Has abstractyes

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