Economic modeling of woody biomass utilization for bioenergy and its application in central Appalachia, USA
Bibliographic record
Abstract
A mixed integer programming model was developed to estimate the delivered cost of woody biomass, including the costs associated with stumpage, bundling (if any), extraction, storage, loading and hauling, and chipping–grinding under different woody biomass handling systems. The model was designed to optimize a woody biomass based biofuel facility’s location with the objective of minimizing the total annual delivered cost of woody biomass under resource and operational constraints. The model was applied in the central Appalachian region within the state of West Virginia. Results showed that the optimal plant location would be at Addison or Grantsville in West Virginia, depending on the system used when daily demand is 900 tonnes of dry woody biomass. For that base-case scenario, the average delivered cost ranged from $2.30·GJ–1to $3.02·GJ–1across the systems. Extensive sensitivity analysis was performed under different scenarios, including biomass availability and purchase–stumpage price, demand, extraction distance, and fuel pricing. The delivered cost was mostly affected by woody biomass demand. Skidding distance had the least impact on the delivered cost. The results would be useful in facilitating the research and economic development of woody biomass utilization for bioenergy in the region.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 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".