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Record W4214635181 · doi:10.3390/en15051770

CO2 Emissions and Economy of Co-Firing Carbonized Wood Pellets at Coal-Fired Power Plants: The Case of Overseas Production of Pellets and Use in Japan

2022· article· en· W4214635181 on OpenAlexaboutno aff
Masami Ashizawa, Maromu Otaka, Hiromi Yamamoto, Atsushi Akisawa

Bibliographic record

VenueEnergies · 2022
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsnot available
Fundersnot available
KeywordsPelletsCarbonizationCoalWaste managementEnvironmental scienceBiomass (ecology)PyrolysisHeat of combustionPulp and paper industryMaterials scienceEngineeringChemistryCombustionComposite materialGeology

Abstract

fetched live from OpenAlex

CO2 emissions reduction from coal-fired power plants is an urgent issue in Japan, as well as around the world. The purpose of this study is to estimate the CO2 emissions and economy of using carbonized wood pellets produced overseas and co-fired at coal-fired power plants in Japan. We examined carbonized wood pellets produced in Canada and Vietnam, since those countries are major exporters of wood pellets for Japan. The results obtained are as follows: (1) The CO2 emissions and calculated cost per calorific value of carbonized wood pellets (CP25), which have a fixed carbon content of 25 wt.%, are lower than those of wood pellets at the port of import in Japan. When the fixed carbon of carbonized biomass is controlled at 25 wt.% or more via a carbonizer, sufficient pyrolysis gas (the heat source used for drying and carbonization without auxiliary fuel) can be obtained. (2) Carbonized wood pellets manufactured in Vietnam are more economical than those manufactured in Canada, since the resource of wood is less expensive and the transportation distance is shorter from Vietnam compared to Canada. (3) When carbonized wood pellets at CP25 are co-fired in coal-fired power plants, they do not affect the cost of the electricity generated, even if the carbonized pellets are blended at a high ratio.

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 score0.006
Threshold uncertainty score0.332

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 head0.009
GPT teacher head0.200
Teacher spread0.191 · 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

Citations21
Published2022
Admission routes1
Has abstractyes

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