Systematic assessment of triticale‐based biorefinery strategies: sustainability assessment using multi‐criteria decision‐making (MCDM)
Bibliographic record
Abstract
Abstract Triticale – a hybrid of rye and wheat – is a man‐made crop that has the potential to be a preferred feedstock for the biorefinery in Canada because of its ability to grow on marginal land, its high yields, and its non‐competition with food‐based crops. However, it is challenging to identify sustainable investment options among the many possible triticale‐based biorefinery pathways. Several product‐process combinations for the production of polylactic acid (PLA) were defined in this study, each involving different degrees of technology and market risk. The different biorefinery configurations had conflicting rankings considering different criteria, making a trade‐off analysis essential to assess the most sustainable biorefinery strategies. Economic, competitive, and environmental dimensions of the biorefinery alternatives were thus evaluated in a multi‐criteria decision‐making (MCDM) panel, so that the triticale‐based biorefinery strategies could be ranked using a sustainability perspective. In this study, a set of ten criteria determined as the most important through previously‐conducted MCDMs were presented to a decision‐making panel. They determined that for PLA production, maximizing electricity production through a straw‐dedicated CHP unit was the least sustainable investment option, due to poor economic and competitiveness performance associated with its capital‐intensiveness and its failure to include a value‐added product portfolio. Therefore, this investment option was screened out from the list of strategies to be further analyzed. On the other hand, options featuring higher technology risk including energy‐efficient separation processes (ultra‐filtration) and integrated fermentation processes (SSCF) attained significantly better sustainability scores due mainly to their low energy and raw materials consumption values. © 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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".