Economic Analysis of Biomass Supply Chains: A Case Study of Four Competing Bioenergy Power Plants in Northwestern Ontario
Bibliographic record
Abstract
Supply chain optimization for biomass-based power plants is an important research area due to greater emphasis on green energy sources. This paper develops and applies two optimization models to analyze the impacts of biomass competition on cost structures and gross margins for four competing biomass-based power plants in northwestern Ontario. Model scenarios are run to study the impacts of changes in parameters relevant to biomass type and processing technology, and prices of inputs and outputs on procurement costs. Cost minimization model shows that per unit procurement costs are directly proportional to the size of the power plants in all scenarios. Profit maximization model, on the other hand, shows that FMUs that are closer to the power plants make higher gross margins. However, the margins significantly increase for FMUs that are close to the power plants potentially offering higher prices. The variations in costs and gross margin structures under various model scenarios are explained by location of depletion cells relative to power plants, availability of each type of biomass in depletion cells, biomass demands, and differential processing costs for two types of biomass. These results can aid decision makers to make improved decisions related to biomass supply chains for bioenergy production.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| 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".