Development and Evaluation of Non-Ionic Polymeric Surfactants as Asphaltene Inhibitors
Bibliographic record
Abstract
Abstract Asphaltenes are of particular interest to the petroleum industry because of their depositional effect which creates problems for production, storage, transportation and refinery processes. A class of non-ionic polymeric surfactants has been developed to prevent the aggregation of asphaltene colloids in crude oils. A surfactant dosage rate as low as 25 ppm can be used to keep the asphaltenes dispersed at nearly 100 %. These polymeric surfactants are made from sustainable and biodegradable raw materials and free of BTEX, other aromatic solvents and phenol formaldehyde resin. The polymeric surfactants were synthesized with a range of monomers at various ratios and under different conditions. The products were then tested in three crude oils from the USA and Canada (API: 45-11 °) to evaluate their performance in a range of systems. The inhibition effect was analyzed with an optical scanning device according to ASTM D7061-06. It was found that the chemical bonding and physical absorption between an asphaltene molecule and the polymeric surfactant played an important role in stabilizing the asphaltene colloids in crude oil. The hydrophobic chain of the polymeric surfactant provided steric hindrance between the asphaltene colloids while the polar groups gave multiple interaction points for bonding and absorption to the asphaltene. Achieving a balance between these aspects of the molecular design has created a new class of polymeric surfactants based on sustainable and biodegradable raw materials which efficiently inhibit the precipitation of asphaltenes from a range of crude oils at low dose rates.
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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 | 0.000 | 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) | 0.001 | 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 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".