Systematic assessment of triticale‐based biorefinery strategies: a biomass procurement strategy for economic success
Bibliographic record
Abstract
Abstract An economical supply of biomass feedstock is an essential part of any biorefinery project. With procurement costs accounting for nearly 50% of operating costs, current biomass supply chain and procurement operations must be continuously improved to reduce procurement costs. Strategic negotiations between the farmer (the producer) and the end user (the biorefinery), in which both parties benefit, should also take place. This study examines procurement supply chains for triticale, for a biorefinery, and proposes a financial model that will satisfy both producer and end user. A biomass cost model was developed to determine the procurement costs of triticale biomass. Several biomass procurement supply chain alternatives were evaluated. Results from the study determined that a biorefinery would pay $225 per tonne of biomass for the delivery of 250 002 tonnes of triticale grain and 265 791 tonnes of triticale straw per year. In addition, the study shows that increased yields of triticale and its similarities in growing and harvesting methods with currently produced agricultural crops will rapidly enable it to become a viable feedstock source for biorefineries. The biomass procurement strategy described appears to be an attractive alternative for producers and provides a good basis for furnishing a long‐term cost‐competitive supply of feedstock to the triticale biorefinery. This financial model is based on the premise that the risk and cost of developing increasingly engineered triticale crops will be borne by the biorefinery owner. © 2015 Society of Chemical Industry and John Wiley & Sons, Ltd
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".