Patent Analysis of SDA Technology for Heavy Oil Upgrading
Bibliographic record
Abstract
Non-conventional energy is considered as important future energy source, as conventional energy has limitation for its capacity. The demand on value added process in heavy oil/oil sand bitumen is increasing in particular. Solvent Deasphalting (SDA) process for Deasphalted Oil (DAO) is used as heavy oil upgrading process in existing refinery process. SDA process for heavy oil upgrading has been already commercialized by leading countries. SDA R&D projects have been carried out actively by those countries. In this study, patent analysis for SDA technology development was carried out. From 1970's, when SDA patents were applied, the patents in Korea, USA, Japan, Canada and Europe were searched and distributed to extraction, recovery, solvent and etc. 334 patents were selected relating to heavy oil upgrading SDA process. The application status of SDA process patents showed a tendency to increase slightly. The number of patent applied was USA patent 131 (39%), Canada patent 83 (25%), Japan patent 35 (11%) and Korea patent 6 (2%). It will be necessary for efficient use of energy resource to support SDA R&D by government.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.012 | 0.009 |
| 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) | 0.010 | 0.002 |
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".