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Record W2009344001 · doi:10.1002/bbb.286

Impacts of co‐location, co‐production, and process energy source on life cycle energy use and greenhouse gas emissions of lignocellulosic ethanol

2011· article· en· W2009344001 on OpenAlexafffundabout
Jon McKechnie, Yimin Zhang, Akifumi Ogino, Brad Saville, Sylvia Sleep, Mark Turner, Robert Pontius, Heather L. MacLean

Bibliographic record

VenueBiofuels Bioproducts and Biorefining · 2011
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsMascoma (Canada)University of Toronto
FundersNational Renewable Energy LaboratoryAUTO21 Network of Centres of Excellence
KeywordsGreenhouse gasGasolineEthanol fuelLife-cycle assessmentBiofuelRaw materialEnvironmental scienceLignocellulosic biomassFossil fuelWaste managementPulp and paper industryProduction (economics)EngineeringChemistryEconomics

Abstract

fetched live from OpenAlex

Abstract The performance of lignocellulosic ethanol in reducing greenhouse gas (GHG) emissions and fossil energy use when substituting for gasoline depends on production technologies and system decisions, many of which have not been considered in life cycle studies. We investigate ethanol production from short rotation forestry feedstock via an uncatalyzed steam explosion pre‐treatment and enzymatic hydrolysis process developed by Mascoma Canada, Inc., and examine a set of production system decisions (co‐location, co‐production, and process energy options) in terms of their influence on life cycle emissions and energy consumption. All production options are found to reduce emissions and petroleum use relative to gasoline on a well‐to‐wheel (WTW) basis; GHG reductions vary by production scenario. Land‐use‐change effects are not included due to a lack of applicable data on short rotation forestry feedstock. Ethanol production with wood pellet co‐product, displacing coal in electricity generation, performs best amongst co‐products in terms of GHG mitigation (−109% relative to gasoline, WTW basis). Maximizing pellet output, although requiring import of predominately fossil‐based process energy, improves overall GHG‐mitigation performance (−130% relative to gasoline, WTW). Similarly, lower ethanol yields result in greater GHG reductions because of increased co‐product output. Co‐locating ethanol production with facilities exporting excess steam and biomass‐based electricity (e.g. pulp mills) achieves the greatest GHG mitigation (−174% relative to gasoline, WTW) by maximizing pellet output and utilizing low‐GHG process energy. By exploiting co‐location opportunities and strategically selecting co‐products, lignocellulosic ethanol can provide large emission reductions, particularly if based upon sustainably grown, high yield, low input feedstocks. © 2011 Society of Chemical Industry and John Wiley & Sons, Ltd

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.221
Teacher spread0.195 · 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

Citations32
Published2011
Admission routes3
Has abstractyes

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