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Record W3193658759 · doi:10.5383/ijtee.07.02.002

Solar Assisted Gasification of Solid Feedstock: Optical Arrangement and Numerical Simulation

2014· article· en· W3193658759 on OpenAlexvenueno aff
Syed Shabbar Raza, Isam Janajreh

Bibliographic record

VenueInternational Journal of Thermal and Environmental Engineering · 2014
Typearticle
Languageen
FieldEngineering
TopicChemical Looping and Thermochemical Processes
Canadian institutionsnot available
Fundersnot available
KeywordsCombustionSolar furnaceSyngasMaterials scienceRaw materialCombustibilityProcess engineeringSolar energyWood gas generatorNuclear engineeringChemical engineeringThermodynamicsEnvironmental scienceWaste managementChemistryPhysicsHydrogenEngineeringPhysical chemistry

Abstract

fetched live from OpenAlex

Gasification is a high temperature thermo-chemical pathway that is used to convert a solid fuel/feedstock into combustible syngas (CO and H2) and chemicals. The gasified species derive their formation energy by combusting 20- 30% of the feedstock. The combustion resulted in emission of soot, particulate matter, NOx and SOx. Therefore, an attempt is made in this work to offset the energy required for gasification through the use of concentrated solar power, i.e. solar to enthalpy. The system consists of a parabolic and hyperbolic reflector using Cassegrain configuration to concentrate 5 kW of solar flux for the gasifier. Furthermore, a high fidelity computational fluid dynamics (CFD) simulation is established based on the results of thermodynamic and optical models. The CFD simulation couples the effect of heat transfer through solar radiation and reaction kinetics in the participating media. The results demonstrate a cold gasification efficiency of 90% with the product mole fraction of CO and H2 of 0.22 and 0.32, respectively, thereby suggesting the viability and large scale implementation of the system.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.515
Threshold uncertainty score0.296

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 head0.006
GPT teacher head0.199
Teacher spread0.193 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Thermal and Environmental EngineeringSame topicChemical Looping and Thermochemical ProcessesFrench-language works237,207