Potential synergies of drop‐in biofuel production with further co‐processing at oil refineries
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Drop‐in biofuels have been defined as functionally equivalent to petroleum‐based transportation fuels and are fully compatible with the existing petroleum infrastructure. They will be essential in sectors such as aviation if we are to achieve emission reduction and climate mitigation goals. Currently, ‘conventional’ drop‐in biofuels, which are primarily based on upgrading of lipids / oleochemicals, are the only significant source of commercial volumes of drop‐in biofuels. However, the necessary increased, future volumes will likely come from ‘advanced’ drop‐in biofuels based on biomass feedstocks such as forest and agriculture residues. Biocrudes / bio‐oils produced from lignocellulosic feedstocks using thermochemical technologies such as gasification, pyrolysis, and hydrothermal liquefaction need to be further upgraded to drop‐in biofuels. However, advanced drop‐in biofuels have been slow to reach commercial maturity due to significant technical challenges, high capital costs, and the challenge of generally lower oil prices. It is likely that the co‐processing of drop‐in biofuels with conventional petroleum refining could considerably reduce capital costs. Initially, co‐processing is likely to be established through the upgrading of conventional / oleochemical feedstocks (lipids). Lipids are readily available in large volumes (global production in 2017 was ~185 million metric tonnes) and can be more easily integrated into oil‐refinery processes. In contrast, lignocellulose‐derived biocrudes / bio‐oils are not yet available in significant volumes and are more complex to co‐process in a refinery. The likely strategies for co‐processing of oleochemicals (lipids) and bio‐oil and biocrude feedstocks based on different insertion points within the refinery infrastructure are discussed. © 2019 The Authors. Biofuels, Bioproducts and Biorefining published by Society of Chemical Industry and John Wiley & Sons, Ltd.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it