GIS method to design and assess the transportation performance of a decentralized biorefinery supply system and comparison with a centralized system: case study in southern Quebec, Canada
Bibliographic record
Abstract
Abstract A decentralized supply chain that integrates biomass depots as an intermediate pre‐processing hub may be an efficient way to obtain stable and dense non‐food carbohydrate commodities that are economically transportable over long distances. This paper presents an integrated geographic information systems (GIS) method based on transport optimization to design and compare the performance of a decentralized versus a centralized biorefinery supply system. The method determines the suitable locations, allocations, sizing, and number of depots according to different demand location scenarios. The method is exemplified by a real case study in southern Quebec. In the design, the biomass depot generates raw sugar, shipped to the biorefinery, and co‐products that are used on site without transportation as animal feed and bioenergy. This diversification strategy provided by a joint production permits savings amounting to two‐thirds of the tonnage on the second transportation arc. The results present the average travel time performance in minutes in different scenarios. In the centralized configuration, the optimized stand‐alone biorefinery location scenario is 45% (59 min) and 58% (100 min) more efficient in terms of transportation than the two biorefineries located in existing industrial parks. However, the latter have a decentralized configuration that is more efficient than their centralized equivalents. In the decentralized configuration, depending on farmer participation, the biorefineries located in the existing industrial park have a transportation performance 16–42% (27–55 min) that is more efficient than their respective centralized configuration. Smaller depots and the use of numerous depots tend to reduce the average impedance as biomass availability increases. This is due to the economies of transportation related to a denser and more meshed network. © 2019 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".