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Record W3207355979 · doi:10.3390/catal10091056

The Demonstration of the Superiority of the Dual Ni-Based Catalytic System for the Adjustment of the H2/CO Ratio in Syngas for Green Fuel Technologies

2020· article· en· W3207355979 on OpenAlexaff
Suntorn Sangsong, Tanakorn Ratana, Sabaithip Tungkamani, Thana Sornchamni, Monrudee Phongaksorn, Eric Croiset

Bibliographic record

VenueCatalysts · 2020
Typearticle
Languageen
FieldChemical Engineering
TopicCatalysts for Methane Reforming
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSyngasExothermic reactionEndothermic processMethaneSyngas to gasoline plusRaw materialWater-gas shift reactionChemical engineeringCatalysisMaterials scienceFischer–Tropsch processChemistryProcess engineeringSteam reformingHydrogen productionAdsorptionOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

A novel dual Ni-based catalytic process (DCP) to control the H2/CO ratio of 2 in the syngas product within one step at temperature <700 °C was created and constructed. With the sequence of the catalysts located in the single reactor, the endothermic combined steam and CO2 reforming of methane (CSCRM) reaction and the exothermic ultra-high-temperature water–gas shift (UHT-WGS) reaction work continuously. During the process, the H2/CO ratio is raised suddenly at UHT-WGS after the syngas is produced from CSCRM, and CSCRM utilizes the heat released from UHT-WGS. Due to these features, DCP is more compact, enhances energy efficiency, and thus decreases the capital cost compared to reformers connecting with shift reactors. To prove this propose, the DCP tests were done in a fixed-bed reactor under various conditions (temperature = 500, 550, and 600 °C; the feed mixture (CH4, CO2, H2O, and N2) with H2O/(CH4 + CO2) ratio = 0.33, 0.53, and 0.67). According to the highest CH4 conversion (around 65%) with carbon tolerance, the recommended conditions for producing syngas with the H2/CO ratio of 2 as a feedstock of Fischer–Tropsch synthesis include the temperature of 600 °C and the H2O/(CH4 + CO2) ratio of 0.53.

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)0.001
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 score0.015
Threshold uncertainty score0.345

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.019
GPT teacher head0.238
Teacher spread0.219 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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