World's largest biofuel and pellet plants – geographic distribution, capacity share, and feedstock supply
Bibliographic record
Abstract
Abstract Biomass can be used for energy purposes by either combustion to heat and power or refining into solid and liquid biofuels. The majority of biomass is used for residential purposes in developing countries. Modern biomass use in industrialized countries is increasing, and more and more biomass is also traded to be used for energy purposes. The purpose of this paper is to locate the 15 largest ethanol, biodiesel, and wood pellet plants. Facilities generating heat, steam and electricity were left out. Secondly it is not generally known what share of biomass users are large plants. Also an effort is made to find out how much these large‐scale biomass refining plants use imported feedstock. For the most part, very large industrial processing facilities are found in a small number of countries. The largest ethanol mills are found almost exclusively in the United States, with one very large plant in the Netherlands. The distribution of biodiesel and wood pellet plants is more dispersed. The countries with the most large biodiesel plants include the USA, Brazil, Spain, and the Netherlands. The countries with the most very large wood pellet plants include the USA, Canada, Russia, and Germany. Torrefaction and pyrolysis technologies are still rarely used on industrial scale. Ethanol and wood pellet plants tend to be sourced from local feedstocks, while biodiesel plants are much more likely to use imported feedstocks or a mix of imports and local biomass. All of these fuels are increasingly traded through the international market. © 2014 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".