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Record W2031804026 · doi:10.1109/iceets.2013.6533350

Biomass based energy systems to meet the growing energy demand with reduced global warming: Role of energy and exergy analyses

2013· article· en· W2031804026 on OpenAlexaff
B. Veerabhadra Reddy, T. Srinivas

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicThermodynamic and Exergetic Analyses of Power and Cooling Systems
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsCogenerationExergyGreenhouse gasEnvironmental economicsBiomass (ecology)Environmental sciencePrimary energyGlobal warmingEnergy engineeringEnergy conservationEnergy accountingEfficient energy useEnergy developmentProcess engineeringRenewable energyElectricity generationEngineeringPower (physics)EconomicsClimate changeEcology

Abstract

fetched live from OpenAlex

The demand for energy is growing worldwide and this has to be met with various options in an environmentally friendly manner. Biomass based energy systems are receiving a great deal of attention to meet part of the growing energy demand with reduced global warming. Cogeneration is also receiving a great deal of attention worldwide to meet part of the energy demand due to high overall energy utilization efficiency and reduced pollutants and greenhouse gas emissions. In the present paper the role of biomass energy systems and future scope for various cogeneration systems are discussed. Given the growing energy demand, biomass based energy systems and cogeneration systems will receive a great deal of attention in the coming years to generate power and process heat from various energy sources to meet part of the global energy demand with high energy conversion efficiencies. Also the exergy analysis is receiving attention to analyse various energy systems to identify the sources of irreversibilities and also aids to improve the performance of the systems. The generalized methodology of exergy evaluation has been reported for energy systems.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.643
Threshold uncertainty score0.984

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 head0.007
GPT teacher head0.209
Teacher spread0.202 · 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 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

Citations11
Published2013
Admission routes1
Has abstractyes

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