Development of a quantitative risk analysis approach to evaluate the economic performance of an industrial-scale biorefinery
Bibliographic record
Abstract
The overall objective of this dissertation was to evaluate the economic performance of a commercial-scale biorefinery given the volatility in the market price of the final product and the variability in the biomass delivered cost. To this end, a risk analysis methodology comprised of five steps was developed: 1) construct the supply area geographical data base, 2) perform Monte Carlo simulation via the Integrated Biomass Supply Analysis and Logistics Multi-Crop model (IBSAL-MC) to produce the biomass delivered cost distribution, 3) conduct economic analysis by combining the biomass delivered cost distribution with the product market price to generate a ROI (return on investment) heat map, 4) repeat Steps 1 to 3 for an alternative scenario and 5) compare heat maps from different scenarios to quantify the effectiveness and incentive available for achieving an alternative scenario. The proposed methodology was applied to a cellulosic sugar plant under construction in Southwestern Ontario, Canada. Three biorefinery scenarios were considered including small-scale (175 dry tonnes (dt)/day), medium-scale (520 dt/day) and large-scale (860 dt/day). Results showed that the magnitude of the required logistical resources and their associated upfront and administrative costs hindered the biorefinery’s economic performance as its scale increased. The risk analysis approach was then applied to the small-scale scenario. Potential economic incentives for participating biomass producers were quantified as the participation rate increased from 20% to 30%, 40% and 50. While increasing farm participation rate was economically beneficial to the biorefinery, there were more economic benefits if the sugar market price was in a favourable range. When a farmers’ co-operative was introduced to the supply system, if the biorefinery could secure a long-term consumer of the produced sugar in the price range of $425-575/tonne, the farmers’ co-operative and other investors of the biomass project were both more likely to achieve an acceptable annual ROI that exceeds 10%.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".