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Record W2404699613 · doi:10.2172/1093743

Improving Heat Recovery In Biomass-Fired Boilers

2013· report· en· W2404699613 on OpenAlexafffund
James R. Keiser, William B. Sharp, Douglas Singbeil, Preet M. Singh, Laurie A Frederick, Joseph Meyer

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEngineering
TopicThermodynamic and Exergetic Analyses of Power and Cooling Systems
Canadian institutionsFPInnovations
FundersOak Ridge National LaboratoryBattelleFPInnovationsOffice of Energy EfficiencyUT-BattelleGeorgia Institute of TechnologyOffice of Energy Efficiency and Renewable EnergyU.S. Department of Energy
KeywordsFlue gasDew pointBoiler (water heating)Natural gasWater vaporWaste managementEnvironmental scienceAbsorption heat pumpHeat recovery ventilationWaste heat recovery unitWaste heatProcess engineeringPetroleum engineeringEnvironmental engineeringNuclear engineeringThermodynamicsChemistryEngineeringHeat exchangerMechanical engineering

Abstract

fetched live from OpenAlex

Combustion of biomass has been used by industry to produce steam and power for many years, but new technologies are being introduced to better recover the energy from biomass as well as to produce a synthetic gas (syngas) that can be used as a starting point in the production of automotive and diesel fuels as well as higher value chemicals. It is of significance that operating temperatures in combustion and gasification systems are often restricted by materials limitations resulting from the degradation of materials in the highest temperature areas. For systems recovering heat and/or generating steam, operating limits are often imposed by degradation of the superheater tubes that recover heat from the combustion gases at the highest temperatures.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.012
GPT teacher head0.222
Teacher spread0.210 · 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

Citations5
Published2013
Admission routes2
Has abstractyes

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