Biomass logistics as a determinant of second‐generation biofuel facility scale, location and technology selection
Bibliographic record
Abstract
Abstract Feedstock logistics, as dictated by biomass physical properties, location, and distribution, as well as transportation infrastructure, were shown to be a primary determinant of the scale, location, and technology selection of any future biorefineries. The maximum capacity of both biochemical‐ and thermochemical‐based second‐generation biofuel facilities was established based on feedstock logistics including delivery mode (road, rail, or ship), maximum number of deliveries by mode, feedstock type (whole logs, chips, pellets, or bio‐oil), and biofuel yield from those feedstocks. The world's largest ethanol plant, pulp mill, coal‐based power plant, and oil refinery were used to approximate maximum plant size for different technologies, and to set an upper limit on the number of deliveries logistically possible for each transport mode. It was apparent that thermochemical conversion to transportation biofuels was favored for large, multimodal coastal facilities that are able to receive imported biomass in the form of feedstock intermediates, such as pellets and bio‐oil (maximum capacities of 2844 and 6001 million liters gasoline equivalent (MLGE) respectively). Biochemically‐based conversion processes, primarily due to smaller economies of scale and the typical use of higher moisture content and undensified feedstocks, such as whole logs and chips, are better suited for smaller facilities (maximum 1405 and 1542 MLGE, respectively) that rely on local feedstocks delivered by truck and/or rail. It was also apparent that optimization of the feedstock‐intermediate‐product chain, including biomass densification for transportation and high conversion yield, is essential if the scale of the any second‐generation biofuels facility is to be maximized. Copyright © 2010 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".