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Record W4362586752 · doi:10.48084/etasr.5632

Numerical Simulation and Optimization of Methane Steam Reforming to Maximize H2 Production: A Case Study

2023· article· en· W4362586752 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEngineering Technology & Applied Science Research · 2023
Typearticle
Languageen
FieldEngineering
TopicIron and Steelmaking Processes
Canadian institutionsDefence Research and Development Canada
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsMultiphysicsSteam reformingHydrogen productionMethane reformerProcess engineeringMethaneWork (physics)EngineeringHydrogenMechanical engineeringChemistryFinite element method

Abstract

fetched live from OpenAlex

Research in renewable energy, the preservation of the environment, and the reduction of energy generation costs are themes that go hand in hand. In this work, a case study was carried out that aims to maximize the production of hydrogen through Methane Steam Reforming. For this, several numerical simulations, considering a laminar flow regime in a chemical reactor with a catalyst, were developed with COMSOL Multiphysics. After an exploratory study of the data, a systematic optimization was developed using multivariate regression models formed by combinations of input parameters in an idealized reactor. The results showed that the proposed approach is capable of satisfactory optimization.

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.

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)0.002
metaresearch head score (Gemma)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.468

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.007
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 head0.052
GPT teacher head0.364
Teacher spread0.313 · 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