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

Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

386
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
386
Linear Differential Equations01:27

Linear Differential Equations

124
The integrating factor method provides a systematic way to solve first-order linear differential equations, especially those that cannot be handled by separation of variables. This method is particularly useful in modeling time-dependent physical systems influenced by both constant inputs and resistive forces. A common example is the motion of a car subjected to a constant engine force while experiencing air resistance proportional to its velocity.In such scenarios, Newton’s second law...
124
Multimachine Stability01:25

Multimachine Stability

593
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
593
Relation between Mathematical Equations and Block Diagrams01:20

Relation between Mathematical Equations and Block Diagrams

3.6K
In a spring-mass-damper system, the second-order differential equation describes the dynamic behavior of the system. When transformed into the Laplace domain under zero initial conditions, this equation can be effectively analyzed and manipulated. The transformation into the Laplace domain converts differential equations into algebraic equations, simplifying the process of isolating the output.
3.6K
Open and closed-loop control systems01:17

Open and closed-loop control systems

1.8K
Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
An open-loop control system operates without feedback from the output. It consists of two primary elements: the controller and the controlled process. The controller receives an input signal...
1.8K
Control Systems01:10

Control Systems

1.9K
Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
At the heart...
1.9K

You might also read

Related Articles

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

Sort by
Same author

An Agent-Based Modeling Dynamic Hybrid Model for Project Management in Research and Development.

Industrial & engineering chemistry research·2026
Same author

Chitosan-xanthan gum-based hydrogels loaded with essential oil distillation by-products of Aloysia citrodora Paláu for antimicrobial systems.

International journal of biological macromolecules·2026
Same author

Are Reusable Dry Electrodes an Alternative to Gelled Electrodes for Canine Surface Electromyography?

Animals : an open access journal from MDPI·2025
Same author

Microencapsulated α-Tocopherol and Moringa Extract for Improved Skin Protection: Insights From Human Skin Assessment in Cosmetic Formulations.

Journal of cosmetic dermatology·2025
Same author

Machine learning framework to predict product distribution of lignocellulosic biomass pyrolysis.

Bioresource technology·2025
Same author

Lignin from aldehyde-assisted fractionation can provide light-colored Pickering emulsions through colloidal particles formed using alkaline antisolvent.

International journal of biological macromolecules·2025

Related Experiment Video

Updated: Mar 2, 2026

Image-based Lagrangian Particle Tracking in Bed-load Experiments
10:32

Image-based Lagrangian Particle Tracking in Bed-load Experiments

Published on: July 20, 2017

9.5K

Dynamics of a True Moving Bed separation process: Linear model identification and advanced process control.

Idelfonso B R Nogueira1, Ana M Ribeiro2, Márcio A F Martins3

  • 1Laboratory of Separation and Reaction Engineering, Associate Laboratory LSRE/LCM, Department of Chemical Engineering, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal; Department of Automation Science and Engineering, Tampere University of Technology, Korkeakoulunkatu 10, FI-33720 Tampere, Finland.

Journal of Chromatography. A
|May 19, 2017
PubMed
Summary

This study introduces a new method for identifying transfer functions in Simulated Moving Bed (SMB) units, improving model predictive control (MPC) for complex processes. The enhanced control strategy effectively manages SMB units at their optimal operating points.

Keywords:
Enantiomers separationModel predictive controlProcess transfer functionSimulated moving bed

More Related Videos

Visually Based Characterization of the Incipient Particle Motion in Regular Substrates: From Laminar to Turbulent Conditions
11:51

Visually Based Characterization of the Incipient Particle Motion in Regular Substrates: From Laminar to Turbulent Conditions

Published on: February 22, 2018

9.2K
Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

2.2K

Related Experiment Videos

Last Updated: Mar 2, 2026

Image-based Lagrangian Particle Tracking in Bed-load Experiments
10:32

Image-based Lagrangian Particle Tracking in Bed-load Experiments

Published on: July 20, 2017

9.5K
Visually Based Characterization of the Incipient Particle Motion in Regular Substrates: From Laminar to Turbulent Conditions
11:51

Visually Based Characterization of the Incipient Particle Motion in Regular Substrates: From Laminar to Turbulent Conditions

Published on: February 22, 2018

9.2K
Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

2.2K

Area of Science:

  • Chemical Engineering
  • Process Control
  • Separation Technology

Background:

  • Simulated Moving Bed (SMB) units present significant control challenges due to complex dynamics and difficult property measurement.
  • Traditional transfer function identification for SMB units at optimal operating points is problematic.

Purpose of the Study:

  • To develop a novel strategy for identifying transfer functions in TMB/SMB units.
  • To apply this identification strategy to linear model predictive controllers (MPC).
  • To address the limitations of linear models for processes with unique dynamics by proposing a modified MPC approach.

Main Methods:

  • Development of a new strategy for transfer function identification in TMB/SMB systems.
  • Application of identified transfer functions to classical linear model predictive controllers (MPC).
  • Implementation of a switching system within MPC to select the most adequate transfer function based on process dynamics.

Main Results:

  • The methodology facilitates straightforward identification of transfer functions at the process's optimal operating point.
  • The enhanced MPC effectively controls the process for both servo and regulator problems.
  • The identified transfer functions are successfully applied to control a four-column SMB unit under optimal conditions.

Conclusions:

  • The proposed method simplifies transfer function identification for SMB units at optimal conditions.
  • The modified MPC strategy, utilizing a switching system, enhances control performance for complex SMB dynamics.
  • This approach offers a robust solution for controlling SMB units, particularly in demanding operational scenarios.