MétaCan
Menu
Back to cohort
Record W4417123437 · doi:10.3390/en18246393

Pressurized Chemical Looping Flue Gas Polishing via Novel Integrated Heat Exchanger Reactor

2025· article· en· W4417123437 on OpenAlexafffund
Hongtian Ge, Matthew A. Perry, Jan B. Haelssig, Arturo Macchi

Bibliographic record

VenueEnergies · 2025
Typearticle
Languageen
FieldEngineering
TopicChemical Looping and Thermochemical Processes
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFlue gasPolishingHeat exchangerPacked bedCombustionHeat recovery ventilationNatural gasAir preheaterFlue

Abstract

fetched live from OpenAlex

Pressurized chemical looping combustion (PCLC) provides the benefit of simplifying the carbon capture process by generating a flue gas stream with high CO2 concentration. However, flue gas polishing is required to remove the residual impurities for pipeline transport. The intensified heat exchanger reactor (IHXR) is a promising method for flue gas polishing while maximizing useful heat recovery that incorporates alternating catalytic packed beds with interstage cooling via printed circuit heat exchangers (PCHE). This work offers a design process for an IHXR capable of polishing a flue gas stream from a 100 MWth natural gas-fired PCLC unit while recovering 1.6 MW of useful heat in the form of saturated steam at 180 °C. Simulation work performed in Aspen HYSYS was used to determine the polished flue gas outlet species concentrations as well as the required number and size of the packed bed sections. The PCHEs for interstage cooling were sized via a thermal circuit approach. The final IHXR consists of six packed beds at 0.06 m in length and five PCHEs at 0.265 m in length, combining to a total IHXR length of 1.685 m. The height and width of the IHXR is shared between the packed beds and PCHEs at 0.91 m and 0.45 m, respectively. The resulting IHXR is capable of recovering heat at a rate of approximately 2.3 MW/m3.

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 score0.021
Threshold uncertainty score0.944

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 head0.008
GPT teacher head0.212
Teacher spread0.204 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueEnergiesSame topicChemical Looping and Thermochemical ProcessesFrench-language works237,207