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Record W2890562933 · doi:10.14288/1.0368789

Life cycle and techno-economic assessment of transportation biofuels from hydrothermal liquefaction of forest residues in British Columbia

2018· article· en· W2890562933 on OpenAlexaboutno aff
Yuhao Nie

Bibliographic record

VenuecIRcle (University of British Columbia) · 2018
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsnot available
Fundersnot available
KeywordsBiofuelLife-cycle assessmentHydrothermal liquefactionLiquefactionNatural resource economicsEnvironmental scienceForestryBusinessWaste managementEngineeringGeographyEconomicsProduction (economics)Geotechnical engineering

Abstract

fetched live from OpenAlex

Biofuels from hydrothermal liquefaction (HTL) of abundantly available forest residues in British Columbia (BC) can potentially make great contributions to reduce the greenhouse gas (GHG) emissions from the transportation sector. Life cycle and techno-economic assessment are conducted to evaluate the environmental and economic performance of a hypothetic 100 million liters per year (MLPY) HTL biofuel system in the Coast Region of BC based on three different supply chain designs. The life cycle GHG emission of HTL biofuels ranges from 17.0-20.5 g CO₂-eq/MJ, corresponding to 78%-82% reduction compared with petroleum fuels. A further reduction of 6.8 g CO₂-eq/MJ can be achieved when by-product biochar is applied for soil amendment. The conversion stage dominates the total GHG emissions, making up more than 50%. The process emitting most GHGs over the life cycle of HTL biofuels is HTL buffer production. Transportation emissions can be lowered by 83% if forest residues are converted to bio-oil before transportation. Process performance parameters (e.g., HTL energy requirement and biofuel yield) and the location specific parameter (e.g., electricity mix) have significant influence on the GHG emissions of HTL biofuels. The economic analysis shows that the minimum selling price (MSP) of HTL biofuels ranges from $0.82-$0.90 per liter of gasoline equivalent, which is about 63%-80% higher than that of petroleum fuels. Converting forest residues to bio-oil and wood pellets before transportation can significantly lower the variable operating cost but not the MSP of HTL biofuels, due to the considerable increase in capital investment. Bio-oil and biofuel yield can significantly influence the MSP of HTL biofuels. Therefore, technology advancement is needed to bring down the production cost of HTL biofuels, otherwise, a high carbon tax can be applied to make HTL biofuels competitive with petroleum fuels.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.174
Threshold uncertainty score0.350

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.004
GPT teacher head0.172
Teacher spread0.167 · 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
Published2018
Admission routes1
Has abstractyes

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