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Record W6992460305

A life-cycle assessment of greenhouse gas emissions associated with on-farm biogas production

2017· dissertation· en· W6992460305 on OpenAlexfundaboutno aff

Bibliographic record

VenueThe Atrium (University of Guelph) · 2017
Typedissertation
Languageen
FieldEngineering
TopicAnaerobic Digestion and Biogas Production
Canadian institutionsnot available
FundersCiência sem FronteirasConselho Nacional de Desenvolvimento Científico e TecnológicoAgriculture and Agri-Food CanadaOntario Ministry of Agriculture, Food and Rural AffairsDairy Farmers of Canada
KeywordsDigestateGreenhouse gasBiogasAnaerobic digestionManureLife-cycle assessmentRenewable energyBiofuel
DOInot available

Abstract

fetched live from OpenAlex

Anaerobic digestion (AD) of liquid dairy manure can generate renewable energy and mitigate greenhouse gas (GHG) emissions. A life cycle assessment (LCA) approach needs to be used to fully assess the AD benefits. However, most LCAs conducted to date have used data from pilot studies. Here, an LCA was conducted using year-round on-farm measurements of 1) emissions from untreated manure, 2) AD first year, and 3) AD fully operational. The data were collected at the same on-farm biogas facility throughout 5 years, starting before the AD became fully operational with addition of industrial food waste (IFW). Plus, soil emissions after land application of untreated manure and digestate were included. The total annual methane emissions from the digestate storage were 1.0 kg CH4 m−3 y−1, in the first year, and 5.6 kg m−3 y−1 in the operational year, while untreated manure emitted 6.6 kg m−3 y−1. The main factor driving digestate CH4 emissions was temperature at 2 m depth (r = 0.98, p < 0.001). Volatile solids (VS) concentration in storage was similar in the first and operational years, but the emissions per VS were much higher in the latter, suggesting that VS is not a good predictor for emissions. In fact, digestate emissions were not correlated to VS concentration (r = 0.37; p = 0.29). The LCA assessed the potential GHG environmental impact of the AD system as a substitute for conventional practices using the robust dataset derived from long-term field studies in Canada. The on-site emissions were higher (> 2x) for AD, but consideration of the avoided off-site emissions resulted in overall GHG emissions 20% lower than the conventional manure treatment. The main emission sources were: storage, fugitive emissions from the anaerobic digesters and the co-generator unit. The environmental benefit came from the avoidance of IFW disposal, grid electricity provision, solid manure stockpiling and land application. The AD could potentially reduce 2.0 t CO2eq cow−1 y−1 from the impact of the conventional manure management practice (9.1 t CO2eq cow−1 y−1). Therefore, the co-digestion of manure and IFW should be encouraged and financially supported by the governmental agencies to mitigate GHG emissions and climate change effect.

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 score0.011
Threshold uncertainty score0.022

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 science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.227
Teacher spread0.213 · 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

Citations1
Published2017
Admission routes2
Has abstractyes

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