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Record W2107309110 · doi:10.5539/mas.v9n2p223

The Mathematical Model of the Coal Gasification Process in a Flow

2015· article· en· W2107309110 on OpenAlexvenueno aff
Nikolay N. Efimov, Alexander Alekseevich Belov, Dmitry Anatolyevich Shaforost, Natalia Vasilyevna Fedorova, Vera Sergeevna Pryatkina

Bibliographic record

VenueModern Applied Science · 2015
Typearticle
Languageen
FieldEngineering
TopicMining and Gasification Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsCoal gasificationCoalProcess (computing)Work (physics)Flow (mathematics)Mathematical modelProcess engineeringOrdinary differential equationComputer scienceWaste managementDifferential equationThermodynamicsMechanicsMathematicsEngineeringPhysics

Abstract

fetched live from OpenAlex

The work purpose - perfection of gasification technologies of processing low reactive high zole coals. For purpose achievement the method of an intensification of process of gasification of coal in an ascending stream of the oxidizer activated by nanocatalyst, and also a mathematical model of the specified process is offered. A mathematical model of a process of coal gasification in a one-dimensional steady flow in the form of a system of ordinary differential equations of energy and changes in the concentrations of the reactants is represented in this paper. Model is based on the kinetics of chemical reactions that determine the process. The offered mathematical model will allow to make calculations of parameters of macrokinetics and heat exchange at coal gasification in an ascending stream.

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)0.001
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: none
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.042
GPT teacher head0.248
Teacher spread0.206 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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