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Record W3041381282 · doi:10.1016/j.xcrp.2020.100104

Economic and Environmental Assessment of Integrated Carbon Capture and Utilization

2020· article· en· W3041381282 on OpenAlexafffund
Imtinan Mohsin, Tareq A. Al‐Attas, Kazi Z. Sumon, Joule Bergerson, Sean McCoy, Md Golam Kibria

Bibliographic record

VenueCell Reports Physical Science · 2020
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsUniversity of Calgary
FundersCanada First Research Excellence FundUniversity of Calgary
KeywordsEnvironmental scienceCarbon fibersCarbon dioxideProduction (economics)Formic acidCarbon capture and storage (timeline)Waste managementFossil fuelCarbon sequestrationTonBaseline (sea)Environmental economicsComputer scienceEngineeringChemistryClimate changeEconomics

Abstract

fetched live from OpenAlex

Integrated carbon capture and utilization (CCU) is appealing for in situ production of fuels and chemicals. Here, we propose and subsequently assess an integrated electrochemical CCU process and compare it with a carbon capture and storage (CCS) route from economic and environmental perspectives. This analysis reveals that under a baseline CCU scenario, carbon products reap either economic (67% and 10% gross margin increase for carbon monoxide [CO] and n-propanol, respectively) or environmental benefits (formic acid production would reduce ∼721 thousand ton carbon dioxide equivalent [CO2e]/year) relative to the CCS route. Under an optimistic scenario, while all of the carbon dioxide (CO2)-derived products are economically compelling over the CCS route, only formic acid production would reduce ∼1,465 thousand ton CO2e/year over CCS (∼575 thousand ton CO2e/year). This study may serve as a framework to decide whether a CCS or a CCU pathway would be compelling under a given scenario when fuel-cell-based CO2 capture technology is used to reduce carbon emissions and create economic value from fossil fuel-based power plants.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations71
Published2020
Admission routes2
Has abstractyes

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