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Record W4412634435 · doi:10.1021/acs.iecr.5c01876

Design of Biocarbon Byproduct Utilization Processes for Ironmaking and Steelmaking

2025· article· en· W4412634435 on OpenAlexafffundabout
Jamie Rose, Giancarlo Dalle Ave, Thomas A. Adams

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2025
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsMcMaster University
FundersNatural Resources CanadaMcMaster University
KeywordsSteelmakingWaste managementEnvironmental scienceProcess engineeringChemistryPulp and paper industryEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide One method of reducing life cycle carbon emissions in steelmaking is using biocarbon to replace coal as a carbon source. However, biocarbon production has several challenges; two of these are increased cost compared to coal and the production of corrosive condensable byproducts. This work includes novel process designs and eco-technoeconomic analyses for three methods of byproduct utilization to close the price gap between biocarbon and coal, while also treating the byproducts before they condense. These methods include combustion for heat, electricity generation, and syngas production through autothermal steam reforming. All utilization methods were found to be financially viable. Based on utility costs and emissions in southern Ontario, Canada, the net added value of byproducts is up to 750 CAD 2024, and net carbon emissions savings are up to 4 tons CO 2 e for each ton of biocarbon produced, compared to a scenario where value is not gained from the byproducts.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.137
GPT teacher head0.334
Teacher spread0.197 · 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

Citations1
Published2025
Admission routes3
Has abstractyes

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