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

Gas Chromatography: Introduction01:13

Gas Chromatography: Introduction

1.5K
Gas chromatography (GC) is a technique for separating and analyzing volatile compounds in a sample. Its primary purpose is to identify and quantify components in complex mixtures, making it essential in fields such as environmental analysis, pharmaceuticals, and petrochemicals. GC is also called vapor-phase chromatography (VPC) or gas-liquid partition chromatography (GLPC).
In GC,  a sample is vaporized and mixed with an inert carrier gas (the mobile phase), which transports it through a...
1.5K
Sample Preparation for Analysis: Advanced Techniques01:08

Sample Preparation for Analysis: Advanced Techniques

302
Accurate analysis of complex samples often requires advanced preparation techniques to achieve reliable and reproducible results. Samples containing inorganic or organic materials can be challenging to dissolve or decompose effectively. Standard sample preparation methods include acid digestion, fusion, dry ashing, and wet digestion.
Acid digestion with strong acids is commonly used to dissolve inorganic materials that are insoluble (do not dissolve) in water. This method can be useful for...
302
Factors Affecting Solubility04:01

Factors Affecting Solubility

33.2K
Compared with pure water, the solubility of an ionic compound is less in aqueous solutions containing a common ion (one also produced by dissolution of the ionic compound). This is an example of a phenomenon known as the common ion effect, which is a consequence of the law of mass action that may be explained using Le Chȃtelier’s principle. Consider the dissolution of silver iodide:
33.2K
Precipitation and Co-precipitation01:17

Precipitation and Co-precipitation

1.7K
Precipitation and coprecipitation methods can be used to separate a mixture of ions in a solution. In qualitative inorganic analysis, ions that form sparingly soluble precipitates with the same reagent are separated based on the differences in solubility products. For example, consider the separation of Cu(II) and Fe(II) ions by precipitation as insoluble sulfides. First, copper(II) sulfide is precipitated by the addition of acidic H2S, where the dissociation of H2S is suppressed. Adding H2S...
1.7K
Precipitation of Ions03:11

Precipitation of Ions

27.7K
Predicting Precipitation
The equation that describes the equilibrium between solid calcium carbonate and its solvated ions is:
27.7K
Analyte Adsorption and Distribution01:09

Analyte Adsorption and Distribution

613
In certain chromatographic separations, solutes transfer between the mobile phase and the stationary phase via sorption, which typically refers to the process of adsorption. For many chromatographic systems, the sorption process often depends on the polarity of the compounds—an expression of the overall dipole moment within the molecule. During the separation process, there is competition between the solute and solvent for adsorption to the stationary phase. Highly polar compounds and...
613

You might also read

Related Articles

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

Sort by
Same author

Crude Oil Yield Estimation: Recent Advances and Technological Progress in the Oil Refining Industry.

Sensors (Basel, Switzerland)·2025
Same author

Rectenna System Development Using Harmonic Balance and S-Parameters for an RF Energy Harvester.

Sensors (Basel, Switzerland)·2024
Same author

A Novel Hybrid Harris Hawk-Arithmetic Optimization Algorithm for Industrial Wireless Mesh Networks.

Sensors (Basel, Switzerland)·2023
Same author

Prediction of Dry-Low Emission Gas Turbine Operating Range from Emission Concentration Using Semi-Supervised Learning.

Sensors (Basel, Switzerland)·2023
Same author

Smart Grid Stability Prediction Model Using Neural Networks to Handle Missing Inputs.

Sensors (Basel, Switzerland)·2022
Same author

A Neural Network-Based Model for Predicting Saybolt Color of Petroleum Products.

Sensors (Basel, Switzerland)·2022

Related Experiment Video

Updated: Jun 10, 2025

Separation of Aldehydes and Reactive Ketones from Mixtures Using a Bisulfite Extraction Protocol
09:08

Separation of Aldehydes and Reactive Ketones from Mixtures Using a Bisulfite Extraction Protocol

Published on: April 2, 2018

34.0K

Prediction of Solvent Composition for Absorption-Based Acid Gas Removal Unit on Gas Sweetening Process.

Mochammad Faqih1, Madiah Binti Omar1, Rafi Jusar Wishnuwardana1

  • 1Department of Chemical Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia.

Molecules (Basel, Switzerland)
|October 16, 2024
PubMed
Summary

This study introduces predictive models using Extreme Gradient Boosting (XGBoost) to optimize solvent compositions for gas sweetening. The models accurately predict acid gas removal and solvent blends, enhancing efficiency in industrial applications.

Keywords:
MDEAPZXGBoostacid gas removal unitsolvent composition

More Related Videos

Author Spotlight: Standardizing the Development of Amine-Based Silica Composites as CO2 Adsorbents for Direct Air Capture
08:00

Author Spotlight: Standardizing the Development of Amine-Based Silica Composites as CO2 Adsorbents for Direct Air Capture

Published on: September 29, 2023

2.3K
Continuously-stirred Anaerobic Digester to Convert Organic Wastes into Biogas: System Setup and Basic Operation
11:31

Continuously-stirred Anaerobic Digester to Convert Organic Wastes into Biogas: System Setup and Basic Operation

Published on: July 13, 2012

33.7K

Related Experiment Videos

Last Updated: Jun 10, 2025

Separation of Aldehydes and Reactive Ketones from Mixtures Using a Bisulfite Extraction Protocol
09:08

Separation of Aldehydes and Reactive Ketones from Mixtures Using a Bisulfite Extraction Protocol

Published on: April 2, 2018

34.0K
Author Spotlight: Standardizing the Development of Amine-Based Silica Composites as CO2 Adsorbents for Direct Air Capture
08:00

Author Spotlight: Standardizing the Development of Amine-Based Silica Composites as CO2 Adsorbents for Direct Air Capture

Published on: September 29, 2023

2.3K
Continuously-stirred Anaerobic Digester to Convert Organic Wastes into Biogas: System Setup and Basic Operation
11:31

Continuously-stirred Anaerobic Digester to Convert Organic Wastes into Biogas: System Setup and Basic Operation

Published on: July 13, 2012

33.7K

Area of Science:

  • Chemical Engineering
  • Process Optimization
  • Computational Chemistry

Background:

  • Gas sweetening is crucial for removing acid gases like H2S and CO2 from natural gas.
  • Chemical absorption using amine solvents, particularly MDEA with PZ additive, is common in acid gas removal units (AGRUs).
  • Optimizing solvent composition is vital for AGRU performance but often relies on inefficient trial-and-error methods.

Purpose of the Study:

  • To develop a predictive technique for optimizing solvent compositions in AGRUs.
  • To create models that predict acid gas concentrations and optimal MDEA/PZ blends.
  • To provide insights for enhancing AGRU efficiency through data-driven solvent optimization.

Main Methods:

  • Utilized the Extreme Gradient Boosting (XGBoost) ensemble algorithm.
  • Developed two predictive models: one for H2S and CO2 concentrations, another for MDEA and PZ compositions.
  • Validated model performance using R-squared, RMSE, and MAE metrics.

Main Results:

  • XGBoost models demonstrated superior performance, achieving R-squared values above 0.99 in most cases.
  • The models exhibited low RMSE and MAE values (less than 1), indicating high prediction accuracy.
  • Analysis revealed the impact of solvent composition on acid gas absorption across various conditions.

Conclusions:

  • The developed predictive models offer an effective approach for optimizing solvent mixtures in gas sweetening processes.
  • This data-driven method can significantly improve the efficiency and reliability of AGRUs in industrial settings.
  • The study provides valuable insights for chemical engineers in designing and operating gas treatment facilities.