Bio Fuel Feedstock and Finish Products – Linings Case Study
Bibliographic record
Abstract
Abstract The production of Renewable Fuels has been embraced by the Global Oil and Gas Industry to adopt more environmentally sustainable practices. This remarkable technology switch has been made possible by concerted research and design changes to traditional sourcing, handling, refining, and storing of natural oils feedstocks and bio-based finish products. Each end of the production chain of biofuels presents corrosion challenges to the infrastructure being used in the process and they must be separately and thoroughly understood by the coatings industry. This presentation examines lessons learned by a leading coatings manufacturer when answering the call for recommending adequate linings for feedstock and finish product storage tanks with an emphasis on an actual project done in the province of Newfoundland and Labrador in Canada where a mothballed refinery is being refurbished to produce biofuels. The R & D has been scaled up at different part of the world to mitigate corrosion in Biofuels markets. The raw feed stock supply options keep growing from standard vegetable seed oils to remaining agricultural waste, from animal fat to animal wastes and municipal wastes, are increasing the unknown variables in the process causing corrosion and solutions to mitigate. These waste to fuel category is attracting diversified feed stocks in offering from the new market. The information exchanges’ & improved tests could help in the lining selection process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".