Synergistic Hydrate Inhibition by Bovine Serum Albumin With Kinetic Hydrate Inhibitors
Bibliographic record
Abstract
Abstract Using kinetic hydrate inhibitors (KHIs) can be technically and economically an ideal solution to achieving deep-sea hydrate risk management, but biodegradability requirements in many offshore locations, such as the North Sea, can restrict their usage. The problem could be addressed by developing so-called "green hydrate inhibitors." Many natural chemicals have been tested to achieve this goal. However, their performance has not been found to be at par with the non-biodegradable high-performing commercial KHIs. It has been shown by some studies that although natural chemicals cannot perform very well alone, they perform quite well when used as synergists with KHIs, possibly due to their large molecular structures with both hydrophilic and hydrophobic functional groups leading to increased steric hindrance to hydrate formation and their higher molecular viscosity possibly causing a reduction in the gas and water mass transfer rate to the hydrate nucleation sites. Thus, in this study, a protein prevalent in the circulatory system of bovine species called bovine serum albumin (BSA), was tested as a synergist with four KHIs used in academia for research and industry (PVP, PVCap, CKHI-1, and CKHI-2). Due to BSA's biodegradability, biocompatibility and nontoxicity, it is widely employed in the medical sector as a drug delivery agent. Standard constant cooling rate hydrate formation experiments at 1°C/h are performed to measure the induction time (delay in hydrate nucleation) and average hydrate growth rate within 1 hour of hydrate nucleation to evaluate the hydrate-inhibiting performance of various blends made of BSA and the four KHIs. The Induction time (IT) and the average hydrate growth rate (AHGR) delivered by each of the 0.5 wt % of the individual four KHIs are compared with the IT, and AHGR obtained from the blend of 0.25 wt % respective KHI mixed with 0.25 wt % BSA. It was found that the blends made from PVP, PVCap and CKHI-2 provided higher ITs (up to 66% for CKHI-2) and lower AHGRs (down to -60% for CKHI-2) compared to the ITs, and AHGRs obtained from the individual commercial KHIs at the same total dosage of 0.5 wt%. The blend CKHI-1 and BSA gave almost the same IT and AHGR as obtained from CKHI-1 alone. Along with high performance, the blended solutions also provide higher biodegradability as compared to commercial KHIs.
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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".