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Record W4406850783 · doi:10.1002/cjce.25608

Modelling and kinetics of the toluene/methylcyclohexane‐based hydrogen storage system

2025· article· en· W4406850783 on OpenAlexvenueno aff
Jiahui Wang, Peiya Li, Shiyuan Wang, Shuhan Lu, Qinchuan Shi, Bin Wang, Xiang Gong, Fusheng Yang, Tao Fang

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2025
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen Storage and Materials
Canadian institutionsnot available
FundersNatural Science Basic Research Program of Shaanxi ProvinceShanxi Provincial Key Research and Development ProjectChina Postdoctoral Science Foundation
KeywordsMethylcyclohexaneTolueneKineticsHydrogen storageHydrogenThermodynamicsChemistryEnvironmental scienceChemical engineeringProcess engineeringOrganic chemistryPhysicsEngineering

Abstract

fetched live from OpenAlex

Abstract The coupling of toluene and methylcyclohexane is one of the most promising liquid organic hydrogen carriers. In this study, the kinetics models of toluene hydrogenation and methylcyclohexane dehydrogenation are established. The reactions were carried out in fixed bed reactors, and the kinetics parameters were fitted with the obtained data. In this experiment, hydrogenation and dehydrogenation reactions were carried out under ambient pressure. Ni/SGA_C n TES catalyst was employed for toluene hydrogenation, the fitting activation energy is 36.27 kJ/mol, and the preexponential factor is 1212 s −1 . Meanwhile, the catalyst of Pt/MgAl 2 O 4 was for methylcyclohexane dehydrogenation, the fitting activation energy is 83.92 kJ/mol, and the preexponential factor is 7.28 × 10 6 s −1 . Aspen Plus was used to simulate on a larger scale and determine the optimal process conditions: the optimum reaction conditions for toluene hydrogenation were determined to be 185°C and 0.3 MPa, and the optimized hydrogen flow rate is 3970 kg/d (The molar ratio is 5.88). Additionally, the optimal conditions for methylcyclohexane dehydrogenation are 325°C and 0.15 MPa.

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 score0.227
Threshold uncertainty score0.256

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 head0.009
GPT teacher head0.190
Teacher spread0.181 · 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
Published2025
Admission routes1
Has abstractyes

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