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Record W2963835026 · doi:10.3934/energy.2019.4.430

Life Cycle Assessment of renewable filler material (biochar) produced from perennial grass (Miscanthus)

2019· article· en· W2963835026 on OpenAlexaffabout
Debela T. Tadele, Poritosh Roy, Fantahun M. Defersha, Manjusri Misra, Amar K. Mohanty

Bibliographic record

VenueAIMS energy · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBioenergy crop production and management
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMiscanthusBiocharLife-cycle assessmentEnvironmental sciencePyrolysisBiomass (ecology)EcotoxicityRaw materialBioenergyCarbon sequestrationWaste managementPulp and paper industryBiofuelChemistryAgronomyCarbon dioxideEngineeringProduction (economics)Biology

Abstract

fetched live from OpenAlex

Biochar, a hydrophobic biomaterial produced from lignocellulosic biomass is a promising alternative to conventional filler materials. Although a variety of feedstocks have been analyzed for producing biomaterials to a limited extend, a complete LCA study of Miscanthus biochar is scarce. This study evaluates the life cycle of biochar produced from Miscanthus that is grown on the marginal land in Ontario, Canada. Life cycle environmental impacts are determined by using the SimaPro LCA software adopting the TRACI method. The global warming potential (GWP) of the life cycle of biochar is found to be 117.6 kg CO<sub>2</sub>eq/t. Miscanthus cultivation (93.0 kg CO<sub>2</sub>eq/t) is the main contributor in the life cycle of Miscanthus biochar followed by pyrolysis (23.3 kg CO<sub>2</sub>eq/t) and transportation (4.8 kg CO<sub>2</sub>eq/t). Miscanthus cultivation is also the main contributor to acidification potential and non-carcinogenic potential; however, transportation and pyrolysis are the hotspots in the case of eutrophication, smog and ecotoxicity, and carcinogenic potential, ozone depletion potential and fossil fuel depletion, respectively. The sensitivity analysis reveals that the environmental impacts decrease with an increase of Miscanthus yield. The study provides information on the life cycle environmental impacts of biomaterial which would facilitate in selecting environmentally favorable filler material to replace conventional filler materials to mitigate environmental impacts.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.553
Threshold uncertainty score0.994

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)0.0070.000

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 head0.010
GPT teacher head0.206
Teacher spread0.196 · 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.

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

Citations19
Published2019
Admission routes2
Has abstractyes

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