Determining the amount of ‘green’ coke generated when co‐processing lipids commercially by fluid catalytic cracking
Bibliographic record
Abstract
Abstract Co‐processing biogenic feedstocks in oil refineries will reduce the greenhouse gas emissions normally associated with fossil‐derived transportation fuels. The fluid catalytic cracker (FCC) within a refinery is a robust processing unit and will probably be a preferred insertion point if biocrudes, produced by the liquefaction of biomass, are co‐processed within a refinery. Fluid catalytic cracking results in a wide range of intermediate products which can be upgraded to gasoline, diesel, heavy fuel oil and liquified petroleum gas blendstocks. Coke is also produced and provides heating for feedstocks, the endothermic catalytic cracking reactions and the regeneration of the FCC catalyst. However, coke combustion also generates carbon dioxide and is a significant source of refinery greenhouse gas emissions. As detailed here, the continuous nature of the process makes the physical evaluation of any biogenic coke fraction, via methods such as C14 isotope analysis, quite challenging. However, quantifying the stack gases provides one way of assessing the renewable content of the carbon dioxide derived from coke combustion. The hourly data from 1 year of commercial operation was assessed using linear and Bayesian ridge regression to quantify the burning coefficient of the coke when co‐processing lipids at the FCC. When a bootstrap method was used to reduce the uncertainties of the coefficients, this allowed us to quantify the renewable (green) fraction of the coke component, indicating the reduction in carbon dioxide emissions when commercially co‐processing biogenic feedstocks. © 2021 Society of Chemical Industry and John Wiley & Sons, Ltd
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".