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

Response Surface Methodology01:16

Response Surface Methodology

284
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
284
Sulfur Assimilation01:20

Sulfur Assimilation

79
Sulfur is an essential element in biological systems, contributing to synthesizing key biomolecules, including amino acids such as cysteine and methionine, and cofactors such as coenzyme A and biotin. Microorganisms primarily assimilate sulfur as sulfate (SO₄²⁻) from the environment, which must undergo a series of biochemical transformations before it can be incorporated into cellular components. As sulfate is highly oxidized, it must undergo assimilatory sulfate reduction to...
79
Atomic Absorption Spectroscopy: Atomization Methods01:25

Atomic Absorption Spectroscopy: Atomization Methods

674
Atomic Absorption Spectroscopy (AAS) atomizes samples through flame atomization or electrothermal atomization. Flame atomization typically involves a nebulizer and spray chamber assembly to combine the sample with a fuel–oxidant mixture, creating a fine aerosol mist that enters a burner. Typically, the fuel and oxidant are combined in an approximately stoichiometric ratio. However, for atoms that are easily oxidized, a fuel-rich mixture may be more advantageous. Only about 5% of the...
674

You might also read

Related Articles

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

Sort by
Same author

Multivariate experimental investigation and RSM modeling of kinematic viscosity behavior in different classes of base oils.

Scientific reports·2026
Same author

Exploring the specific heat capacity of aqueous blends of K<sub>2</sub>CO<sub>3</sub>- PZ-MEA in CO<sub>2</sub> capture using ANN and RSM models.

Scientific reports·2026
Same author

Amine-functionalized cellulose-chitosan composites as an efficient adsorbent for CO<sub>2</sub> capture.

Environmental science and pollution research international·2026
Same author

Machine learning and response surface analysis of mean drop size and dispersed phase holdup in an L-shaped pulsed sieve plate column.

Scientific reports·2026
Same author

Exploring the potential of SnO<sub>2</sub> nanoparticles for CO<sub>2</sub> capture using RSM and ANN.

Scientific reports·2026
Same author

Study of CO<sub>2</sub> capture by synthesized composite and modelling with machine learning and response surface methodology.

Scientific reports·2025

Related Experiment Video

Updated: Sep 17, 2025

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
10:36

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

Published on: November 3, 2023

1.7K

Exploring the adsorption desulfurization efficiency using RSM and ANN methodologies.

Mahyar Mansouri1, Mohsen Shayanmehr1, Ahad Ghaemi2

  • 1School of Chemical, Petroleum and Gas Engineering, Iran University of Science and Technology, Narmak, Tehran, 16846, Iran.

Scientific Reports
|July 2, 2025
PubMed
Summary

This study optimized zeolite performance for adsorptive desulfurization using Response Surface Methodology (RSM) and Artificial Neural Networks (ANN). ANN models, particularly RBF, achieved superior accuracy in predicting sulfur adsorption, identifying micropore volume as key.

Keywords:
ANNDesulfurization adsorptionRSMZeolite

More Related Videos

A Dual-Functional Electroactive Filter Towards Simultaneously SbIII Oxidation and Sequestration
08:34

A Dual-Functional Electroactive Filter Towards Simultaneously SbIII Oxidation and Sequestration

Published on: December 5, 2019

5.6K
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

2.6K

Related Experiment Videos

Last Updated: Sep 17, 2025

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
10:36

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

Published on: November 3, 2023

1.7K
A Dual-Functional Electroactive Filter Towards Simultaneously SbIII Oxidation and Sequestration
08:34

A Dual-Functional Electroactive Filter Towards Simultaneously SbIII Oxidation and Sequestration

Published on: December 5, 2019

5.6K
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

2.6K

Area of Science:

  • Materials Science
  • Chemical Engineering
  • Environmental Science

Background:

  • Zeolites are highly effective adsorbents for removing sulfur compounds due to their large surface area and tunable properties.
  • Adsorptive desulfurization is crucial for producing ultra-low sulfur fuels and mitigating environmental pollution.

Purpose of the Study:

  • To model and optimize the sulfur adsorption performance of modified zeolites using advanced computational techniques.
  • To compare the efficacy of Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) in predicting zeolite desulfurization efficiency.

Main Methods:

  • Response Surface Methodology (RSM) with a central composite design (CCD) was employed for initial modeling.
  • Artificial Neural Networks (ANN), including Radial Basis Function (RBF) and Multilayer Perceptron (MLP), were developed for enhanced prediction.
  • Global Sensitivity Analysis (GSA) and Monte Carlo simulations were used for parameter influence and uncertainty assessment.

Main Results:

  • RSM achieved a high adjusted R-squared of 0.9502, but ANN models demonstrated superior accuracy.
  • The RBF network yielded an R-squared of 0.9951 and a low MSE of 0.0015, outperforming MLP.
  • Micropore volume was identified as the most significant factor influencing sulfur adsorption.

Conclusions:

  • Artificial Neural Networks (ANN) provide a robust and highly accurate alternative to traditional modeling for adsorptive desulfurization.
  • The optimized ANN models offer a pathway to efficient, scalable production of ultra-low sulfur fuels with reduced experimental effort.
  • This research enhances process understanding and facilitates the development of advanced desulfurization technologies.