Systematic assessment of triticale‐based biorefinery strategies: techno‐economic analysis to identify investment opportunities
Bibliographic record
Abstract
Abstract In recent years, interest has increased in biorefineries that use a variety of biomass residues and non‐food crops. Triticale (X Triticosecale Wittmack) is a human‐developed crop that has the potential to become a preferred industrial energy crop for the biorefinery because of its capacity to grow on marginal land, its higher yields compared to existing cereal crops such as wheat, and its non‐competition with food‐based crops. However, implementation of the triticale‐based biorefinery will require the identification of sustainable strategies and as its sustainability pillar, the identification of economically promising strategies. In this study, the economic performance of several triticale‐based product‐process scenarios, including the production of ethanol, polylactic acid (PLA), and thermoplastic starch polymer, has been assessed through the evaluation of six economic criteria. These conflicting criteria have been evaluated in a multi‐criteria decision‐making (MCDM) panel, demonstrated for PLA platform, which has made it possible (i) to rank the alternatives by their economic performance, and (ii) to identify a set of the most important criteria to be used in a sustainability study. The MCDM results show that the alternatives with the best performance on profitability‐oriented criteria do not necessarily achieve the highest overall economic score. This suggests the need to consider both business‐strategy‐oriented criteria and profitability‐oriented criteria in strategic decision‐making. The MCDM results show that the internal rate of return, the downside internal rate of return, and the resistance to supply market uncertainty with relative weights of 24.8%, 23.6%, and 18.1% respectively, are the most important of the criteria assessed. © 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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".