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Record W4382371047 · doi:10.1002/cjce.25035

Simultaneous optimization of carbon yield and iodine adsorption of <scp>ACFs</scp> derived from carpet wastes

2023· article· en· W4382371047 on OpenAlexvenueno aff
Komeil Nasouri, Fatemeh Sadat Mousavi, Simin Ahmadi

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsCarbonizationAdsorptionPyrolysisResponse surface methodologyActivated carbonYield (engineering)IodineCarbon fibersPulp and paper industryMaterials scienceWaste managementChemical engineeringChemistryNuclear chemistryOrganic chemistryChromatographyComposite materialComposite numberEngineering

Abstract

fetched live from OpenAlex

Abstract Recycling post‐consumer carpet waste is a critical consideration for decreasing the environmental hazard of non‐biodegradable plastics. Therefore, the manufacturing of activated carbon fibres (ACFs) from post‐consumer carpet waste treated with KOH was modelled and optimized by the response surface methodology (RSM). The simultaneous effects of carbonization rate, pyrolysis temperature, and activation time on the performance of carpet waste‐based ACFs were studied. Box–Behnken design (BBD) was applied to study the influence of three significant processing parameters on the carbon yield and iodine adsorption of synthesized ACFs. The RSM analysis established that pyrolysis temperature was the most important parameter affecting the ACFs' performance. The optimum settings that led to the production of high carbon yield and iodine adsorption of ACFs were a carbonization rate of 5.4°C/min, pyrolysis temperature of 705.7°C, and activation time of 83 min. The carbon yield and iodine adsorption under the above conditions were obtained at 49.8 ± 0.9 wt.% and 1351 ± 45 mg/g, respectively. BBD has been established to be a powerful approach in modelling and optimization to achieve high‐quality of iodine adsorbents from carpet wastes.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.153
Threshold uncertainty score0.473

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 head0.007
GPT teacher head0.178
Teacher spread0.171 · 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 designSimulation or modeling
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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