Wood fuel from British Columbia : multi-scale assessment of the economic, energetic and environmental efficiencies of the supply chains of conventional and torrefied wood pellets
Bibliographic record
Abstract
This thesis investigates several key aspects of the supply systems of torrefied and conventional wood pellet (TWP/CWP) from British Columbia (BC): what are the economic, environmental, and energetic (“3E”) performances of TWPs and CWPs supplied from BC into different markets? What is the best pathway for making TWPs? Can the TWPs production process be operated auto-thermally? If so, under what operating conditions? A simulation platform is developed, including models for rotary and fluidized bed dryers, directly and indirectly heated rotary and fluidized bed torrefiers, and integrating heat and mass transfer, kinetics, particle hydrodynamics, thermodynamics and element evolutions. The auto-thermal operation boundaries are identified for the torrefaction system. The boundaries are influenced by drying technology, N₂ flowrate, biomass properties and torrefaction conditions. A heat and mass integration scheme is proposed to avoid the use of N2 for torrefaction by recycling flue gases and to expand the auto-thermal operation boundaries. CWP and TWP production processes are analyzed, revealing that torrefying the biomass before grinding can reduce the “3E” impacts significantly. Due to auto-thermal operation, electricity is the main energy consumption and contributor to greenhouse gas (GHG) emissions. Capital costs contribute about 10% of the total production costs, with the remaining 90% being the operating cost, within which raw material, electricity, and labor are the major components. The minimum selling price at which BC TWPs is estimated as ~$6.7/GJ, equivalent to 140$/t. The “3E” performances of BC CWP/TWPs supply chains to the UK, Japan, Ontario and Alberta are quantified with uncertainties considered. TWPs can reduce “3E” impacts by about 25% in comparison with CWPs. Transportation is the main energy consumer and GHG emission contributor, while transportation and production are the major cost stages. There is significant potential to replace coal with BC TWPs domestically and overseas, particularly in the UK, EU and Pacific Asia, due to the comparative advantages of BC’s clean electricity system and rich biomass resources.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".