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Record W2170745751 · doi:10.5539/enrr.v4n4p59

Use of Environmental and Thermodynamic Indicators to Assess the Performance of an Integrated Process for Ethanol Production

2014· article· en· W2170745751 on OpenAlexvenueno aff
Alex Rodrigues Nogueira, A. C. G. Donke, Marília I. S. Folegatti Matsuura, Patrícia Helena Lara dos Santos Matai, Luiz Kulay

Bibliographic record

VenueEnvironment and Natural Resources Research · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Impact and Sustainability
Canadian institutionsnot available
FundersFundação para a Ciência e a TecnologiaCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsExergyBagasseLife-cycle assessmentEnvironmental scienceEthanol fuelBiofuelProduction (economics)Primary energyEnergy consumptionPulp and paper industryProcess engineeringWaste managementMathematicsEnergy (signal processing)EngineeringEconomicsStatistics

Abstract

fetched live from OpenAlex

Corn is one of the possibilities for diversification of Brazilian ethanol production. Four scenarios of analysis were established. The environmental dimension was evaluated by the Life Cycle Assessment (LCA) approach, whereas the Thermodynamic performance was verified by applying the techniques of Cumulative Energy Demand (CED) and Cumulative Exergy Demand (CExD). The production of ethanol from corn using wood chips for energy supply of the plant resulted in a homogeneous environmental performance. Factors such direct seeding -and the LHV of the wood for energy support this result. For both Thermodynamic analysis the production of sugarcane ethanol had better indexes because the use of bagasse replaced other sources of primary energy. This result remained for a combined analysis between the two dimensions, which related environmental effects in terms of Climate Change with the aggregation of primary energy consumption for ideal 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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.489
Threshold uncertainty score0.522

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.029
GPT teacher head0.311
Teacher spread0.282 · 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 designObservational
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

Citations10
Published2014
Admission routes1
Has abstractyes

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