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Record W4382542631 · doi:10.1016/j.ces.2023.119050

Chemical looping reforming for syngas production with co-conversion of CH4 and CO2 by using ilmenite ore as both oxygen carrier and catalyst

2023· article· en· W4382542631 on OpenAlexafffund
Zhenkun Sun, Negar Manafi Rasi, Dennis Lu, Robert T. Symonds, Nader Mahinpey, Binchen Wu, Lunbo Duan

Bibliographic record

VenueChemical Engineering Science · 2023
Typearticle
Languageen
FieldEngineering
TopicChemical Looping and Thermochemical Processes
Canadian institutionsUniversity of CalgaryNatural Resources Canada
FundersNatural Resources CanadaWestern UniversityNational Natural Science Foundation of ChinaGovernment of Canada
KeywordsSyngasChemical looping combustionSyngas to gasoline plusIlmenitePartial oxidationMethaneCarbon dioxide reformingChemistryChemical engineeringOxygenNatural gasBiogasCatalysisWaste managementSteam reformingHydrogen productionMineralogyEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Chemical looping CH 4 reforming has been accepted as a highly promising solution for sustainable syngas production from gaseous feedstocks, e.g., natural gas, shale gas, biogas, etc. , with reduced safety concerns and without the need for purified O 2 . However, the presence of CO 2 in fuel streams severely suppresses syngas generation when using iron-based oxygen carriers. This study has identified the most possible pathway of the reactions among CO 2 , CH 4, and FeTiO 3 through density functional theory calculation and isotope analysis. Based on the revealed mechanism, a novel strategy for chemical looping CH 4 reforming coupled with CO 2 utilization has been demonstrated by using naturally occurring and low-cost ilmenite ore as both an oxygen carrier and a catalyst. With a well-designed solid configuration, not only can the suppressing effect of CO 2 on CH 4 partial oxidation be mitigated to some extent, but CH 4 and CO 2 can be simultaneously converted into syngas within the same reactor.

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 score0.054
Threshold uncertainty score0.746

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.219
Teacher spread0.212 · 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

Citations12
Published2023
Admission routes2
Has abstractyes

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