A novel risk analysis methodology to evaluate the economic performance of a biorefinery and to quantify the economic incentives for participating biomass producers
Bibliographic record
Abstract
Abstract In this paper, a novel risk analysis methodology is presented to evaluate the economic performance of a biorefinery given the volatility in the market price of the final product and the variability in the biomass delivered cost. In addition, potential economic incentives for participating biomass producers are quantified for different farm participation rates. The Monte Carlo simulation model, IBSAL‐MC, is used to estimate the biomass delivered cost distribution, and a modified risk heat map is used to visualize the expected return on investment (ROI) for various combinations of the market price of the final product and the biomass delivered cost. The developed methodology is applied to an under‐construction cellulosic sugar facility located in Sarnia, southwestern Ontario. Four farm participation rates of 20% (base‐case scenario), 30%, 40% and 50% are studied. The results show that the expected annual ROI for the base‐case scenario is estimated to be 1.3%. As the farm participation rate increases, the expected annual ROI increases from 1.3% at 20% farm participation rate to 3.4%, 4.6% and 5.1% at 30%, 40% and 50% rates, respectively. At high sugar market prices ($375–$525/tonne), the overall expected annual ROI increases to 9.5%, 11.4%, 12.6% and 13.0% in 20%, 30%, 40% and 50% farm participation rates, respectively. In this case, the economic incentives to share with biomass producers are estimated to be $14.10/dry tonne (dt), $15.77/dt and $16.33/dt by increasing the farm participation rate from 20% to 30%, 40% and 50%, respectively. © 2018 Society of Chemical Industry and John Wiley & Sons, Ltd
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".