A Novel Solid–Liquid Equilibrium Model for Describing the Adsorption of Associating Asphaltene Molecules onto Solid Surfaces Based on the “Chemical Theory”
Bibliographic record
Abstract
Asphaltenes exhibit an amphiphlic behavior and tend to form colloidal i -mers, because of their chemical structure. The formation of colloidal aggregates can generate formation damage for the precipitation and/or deposition of asphaltenes, because of the degree of self-association, altering the wettability of rock surface and significantly affect crude oil viscosity and specific gravity. This study aims at introducing a novel model for describing, at the macroscopic level, the adsorption equilibria of self-associating molecules such as asphaltenes in solution onto solid surfaces based on the “chemical theory”. The model describes the adsorption isotherms temperature-dependent using three parameters, namely, maximum amount adsorbed, constant of i -mer reactions, and Henry’s law constant. Furthermore, a temperature-independent model of five parameters, based on the modifications of the constants of reaction and Henry’s law using an Arrhenius-type equation was proposed for estimating the thermodynamics parameters, such as Δ G ads °, Δ H ads °, and Δ S ads ° of the adsorption process. This model improves the understanding of interactions asphaltene–asphaltene and asphaltene–solid surface on the adsorption–equilibrium process. The theoretical predictions of isotherms were validated successfully by determining the root mean-square errors (RSM%) between data obtained from published literature and values predicted for asphaltenes and surfaces with differing chemical natures. More than 40 experimental data taken from literature have been used for validating the solid–liquid equilibrium (SLE) model for describing the adsorption isotherm of asphaltenes from different origins on surfaces with different chemical nature, which shows the model robustness due to the complexity of the liquid phase adsorption for those complex molecules.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".