Eni Slurry Technology: A new process for heavy oil upgrading
Bibliographic record
Abstract
EST (Eni Slurry Technology) represents a significant technological innovation in residue conversion and unconventional oil upgrading and will mark a step change in the treatment of the heavy end of the barrel. This new technology, internally developed by Eni, allows the total conversion of the heaviest fraction of the barrel into useful products, mainly transportation fuels, with a great major impact on the economic and environmental valorisation of hydrocarbon resources. EST employs nano-sized hydrogenation catalysts and an original process scheme which allow complete feedstock conversion to valuable distillates or its upgrading to synthetic crude oil with a substantial API gravity gain, avoiding the production of residual by-products, such as pet-coke or heavy fuel oil. Since the 1990's, the technology has been successfully tested on both laboratory and pilot scales. Following the positive results obtained at this scale, Eni decided to build a 1200 bpd Commercial Demonstration Plant (CDP) within its Taranto refinery. The plant was completed and successfully started up in the third quarter of 2005. Since then, the CDP unit operation has allowed the successful test of EST performance on heavy feedstocks from around the world (Russia, Venezuela, Mexico, Middle East and Canada), confirming the great flexibility of the process. The peculiar characteristics of EST in terms of yield, products quality, absence of undesired by-products and feedstock flexibility constitute its superior economic and environmental attractiveness. EST can offer additional margins in the range of 3-5 $/bbl of feedstock over current conversion technologies, which can be crucial for the exploitation of unconventional oil reserves.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".