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Record W3044185937 · doi:10.1051/e3sconf/202018001017

Some design characteristics of micro steam turbines for agricultural biomass energy conversion

2020· article· en· W3044185937 on OpenAlexaboutno aff
Gabriel Negreanu, Ion Oprea, Viorel Berbece

Bibliographic record

VenueE3S Web of Conferences · 2020
Typearticle
Languageen
FieldEngineering
TopicThermodynamic and Exergetic Analyses of Power and Cooling Systems
Canadian institutionsnot available
FundersColegiul Consultativ pentru Cercetare-Dezvoltare şi InovareUnitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii
KeywordsBoiler (water heating)BriquettePelletsSteam pressureEnvironmental scienceProcess engineeringMechanical engineeringMass flow rateThermal power stationSteam-electric power stationBiomass (ecology)Waste managementPiston (optics)Nuclear engineeringEngineeringMaterials scienceMechanicsCoal

Abstract

fetched live from OpenAlex

The paper continues the study of reconversion of a 400 kW hot water boiler in a steam generator suitable to valorise the energy content of briquettes and pellets of agricultural biomass. After steam parameters selection (pressure, temperature, mass-flow rate), an overview of main steam machines types (axial, radial, screw, piston engine) is done. Further, a parallel design of most wide-spread ones (Laval, Curtis and radial) were performed, at different rotation speeds, in order to find the best configuration in respect with the flow section dimensions, internal efficiency and power (electrical and thermal) output. The results of the paper could be very useful for the investors in agricultural “waste-toenergy” projects in order to select appropriate technology and equipment.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

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)0.0030.001

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 head0.017
GPT teacher head0.204
Teacher spread0.187 · 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 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

Citations4
Published2020
Admission routes1
Has abstractyes

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