Robust asphaltene onset pressure prediction using ensemble learning
Most works on asphaltene onset pressure (AOP) prediction rely on a single model without making them robust against noise. This paper adopts a robust approach to training three machine learning models—Multi-Layer Perceptron (MLP), CatBoost, and Random Forest (RF)—to predict AOP as a function of oil c...
Saved in:
| Main Authors: | Jafar Khalighi, Alexey Cheremisin |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
|
| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024017353 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Modeling of asphaltene precipitation - part I: comparative study for asphaltene precipitation envelope prediction methods
by: Ali A. Ali, et al.
Published: (2024-12-01) -
Modeling Asphaltene Precipitation-Part II: Comparative Study for Asphaltene Precipitation Curve Prediction Methods
by: Ali A. Ali, et al.
Published: (2025-01-01) -
Uncovering the Hydrocracking Efficiency of Iron-Based Catalysts: A Novel Approach to Asphaltene Transformation in Iranian Heavy Oil
by: Muzaffer Yaşar, et al.
Published: (2024-06-01) -
Supported and Free-Standing Non-Noble Metal Nanoparticles and Their Catalytic Activity in Hydroconversion of Asphaltenes into Light Hydrocarbons
by: Leonid Kustov, et al.
Published: (2024-11-01) -
Quinoline impact on composition, structure and aggregative stability of asphaltenes from heavy oil of different chemical types
by: Dmitry S. Korneev, et al.
Published: (2024-12-01)