Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Surface Tension of Fluid01:22

Surface Tension of Fluid

342
Surface tension is a fundamental property of fluids, occurring at the boundary between a liquid and a gas or between two immiscible liquids. This phenomenon arises from the cohesive forces between molecules at the fluid's surface, creating an effect similar to a stretched elastic membrane. Inside each fluid, molecules are equally attracted in all directions by neighboring molecules, but surface molecules experience a net inward force, resulting in surface tension.
Surface tension varies...
342
Theories of Dissolution: The Danckwerts' Model and Interfacial Barrier Model01:09

Theories of Dissolution: The Danckwerts' Model and Interfacial Barrier Model

345
Various dissolution theories provide insight into the factors that influence the dissolution rate. Danckwerts' Model suggests that turbulence, rather than a stagnant layer, characterizes the dissolution medium at the solid-liquid interface. In this model, the agitated solvent contains macroscopic packets that move to the interface via eddy currents, facilitating the absorption and delivery of the drug to the bulk solution. The regular replenishment of solvent packets maintains the...
345
Surface Tension and Surface Energy01:16

Surface Tension and Surface Energy

1.5K
When a paint brush is immersed in water, the bristles wave freely inside the water. When it is taken out, the bristles stick together. The reason behind this effect is surface tension.
Consider a beaker filled with liquid. The bulk molecules in the liquid experience equal attractive forces on all sides with the surrounding molecules. However, the surface molecules experience a net attractive force downward due to the bulk molecules. The surface of the liquid behaves like a stretched membrane,...
1.5K
Turbulent Flow: Problem Solving01:09

Turbulent Flow: Problem Solving

160
Carbonation is a process used to dissolve carbon dioxide gas in a liquid, commonly used in the production of carbonated beverages. Achieving efficient carbonation requires careful control of temperature, pressure, and flow conditions. By adjusting these parameters, carbonation efficiency can be maximized, producing a higher concentration of CO2 in the liquid.
Temperature is a key factor in CO2 solubility. In this case, the CO2 gas and the liquid are cooled to 20°C. Lower temperatures...
160
Surface Tension, Capillary Action, and Viscosity02:57

Surface Tension, Capillary Action, and Viscosity

28.1K
Surface Tension
The various IMFs between identical molecules of a substance are examples of cohesive forces. The molecules within a liquid are surrounded by other molecules and are attracted equally in all directions by the cohesive forces within the liquid. However, the molecules on the surface of a liquid are attracted only by about one-half as many molecules. Because of the unbalanced molecular attractions on the surface molecules, liquids contract to form a shape that minimizes the number...
28.1K
Newtonian Fluid: Problem Solving01:18

Newtonian Fluid: Problem Solving

267
Newtonian fluids exhibit a constant viscosity, meaning their shear stress and shear strain rate are directly proportional. This property ensures a predictable and stable response to applied forces, maintaining a linear relationship between force and flow. Examples include water, air, and light oils, consistently demonstrating this proportional behavior regardless of external conditions.
A velocity gradient forms within the fluid when a Newtonian fluid is placed between two parallel plates, with...
267

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Interpretable white-box modeling for nitrogen storage in metal-organic frameworks.

Scientific reports·2026
Same author

Synergistic effects of alkalines, salts, and silica nanoparticles on in-situ surfactant generation from different crude oils in relation to enhanced oil recovery.

Scientific reports·2026
Same author

White-box modeling of asphaltene precipitation during natural depletion of oil reservoirs.

Scientific reports·2026
Same author

Experimental investigation of coal tailing-derived nanoparticles for CO<sub>2</sub> foam stabilization.

Environmental research·2026
Same author

Predicting methane adsorption in coal and shale with white-box and black-box machine learning models.

Scientific reports·2026
Same author

Comparative analysis of natural and synthetic surfactant adsorption by quartz minerals: an experimental study.

Scientific reports·2026

Related Experiment Video

Updated: Jul 24, 2025

Microtensiometer for Confocal Microscopy Visualization of Dynamic Interfaces
08:05

Microtensiometer for Confocal Microscopy Visualization of Dynamic Interfaces

Published on: September 9, 2022

2.4K

Modeling interfacial tension of surfactant-hydrocarbon systems using robust tree-based machine learning algorithms.

Ali Rashidi-Khaniabadi1, Elham Rashidi-Khaniabadi2, Behnam Amiri-Ramsheh3

  • 1Department of Petroleum Engineering, EOR Research Center, Omidiyeh Branch, Islamic Azad University, Omidiyeh, Iran.

Scientific Reports
|July 5, 2023
PubMed
Summary

Predicting interfacial tension (IFT) is crucial for enhanced oil recovery (EOR). Tree-based machine learning models accurately estimated IFT, with Gradient Boosted Regression Trees (GBRT) showing the best performance.

More Related Videos

Studying Surfactant Effects on Hydrate Crystallization at Oil-Water Interfaces Using a Low-Cost Integrated Modular Peltier Device
06:31

Studying Surfactant Effects on Hydrate Crystallization at Oil-Water Interfaces Using a Low-Cost Integrated Modular Peltier Device

Published on: March 18, 2020

6.4K
Accurate Determination of the Equilibrium Surface Tension Values with Area Perturbation Tests
00:07

Accurate Determination of the Equilibrium Surface Tension Values with Area Perturbation Tests

Published on: August 30, 2019

7.5K

Related Experiment Videos

Last Updated: Jul 24, 2025

Microtensiometer for Confocal Microscopy Visualization of Dynamic Interfaces
08:05

Microtensiometer for Confocal Microscopy Visualization of Dynamic Interfaces

Published on: September 9, 2022

2.4K
Studying Surfactant Effects on Hydrate Crystallization at Oil-Water Interfaces Using a Low-Cost Integrated Modular Peltier Device
06:31

Studying Surfactant Effects on Hydrate Crystallization at Oil-Water Interfaces Using a Low-Cost Integrated Modular Peltier Device

Published on: March 18, 2020

6.4K
Accurate Determination of the Equilibrium Surface Tension Values with Area Perturbation Tests
00:07

Accurate Determination of the Equilibrium Surface Tension Values with Area Perturbation Tests

Published on: August 30, 2019

7.5K

Area of Science:

  • Petroleum Engineering
  • Chemical Engineering
  • Machine Learning

Background:

  • Interfacial tension (IFT) is a critical parameter for enhanced oil recovery (EOR) operations in petroleum engineering.
  • Laboratory measurement of IFT is labor-intensive and expensive, necessitating alternative estimation methods.
  • Advanced intelligent techniques offer a viable alternative for accurate IFT prediction.

Purpose of the Study:

  • To predict interfacial tension (IFT) between surfactants and hydrocarbons using tree-based machine learning algorithms.
  • To evaluate the performance of Decision Tree (DT), Extra Trees (ET), and Gradient Boosted Regression Trees (GBRT) models.
  • To identify key input parameters influencing IFT through sensitivity analysis.

Main Methods:

  • Utilized 390 experimental data points from previous studies for model training and validation.
  • Employed DT, ET, and GBRT algorithms with inputs including temperature, molecular weight, surfactant concentration, HLB, and PIT.
  • Evaluated model performance using statistical metrics (e.g., R-squared, average absolute relative error) and graphical methods.

Main Results:

  • GBRT achieved the highest prediction accuracy with an average absolute relative error of 2.71% and an R-squared value of 0.9939.
  • DT and ET models also demonstrated strong performance with errors of 4.12% and 3.52%, respectively.
  • Sensitivity analysis indicated that Phase Inversion Temperature (PIT), surfactant concentration, and Hydrophilic-Lipophilic Balance (HLB) significantly impact IFT.

Conclusions:

  • Tree-based machine learning models, particularly GBRT, provide accurate and efficient alternatives for estimating IFT in EOR applications.
  • The developed models can aid in optimizing EOR strategies by predicting IFT with high precision.
  • Understanding the influence of parameters like PIT, surfactant concentration, and HLB is essential for controlling IFT and enhancing oil recovery.