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

Step-Growth Polymerization: Overview01:03

Step-Growth Polymerization: Overview

3.9K
Step-growth or condensation polymerization is a stepwise reaction of bi or multifunctional monomers to form long-chain polymers. As all the monomers are reactive, most of the monomers are consumed at the early stages of the reaction to form small chains of reactive oligomers, which then combine to form long polymer chains in the late stages. Hence, the reaction has to proceed for a long time to achieve high molecular weight polymers.
Many natural and synthetic polymers are produced by...
3.9K
Ziegler–Natta Chain-Growth Polymerization: Overview01:17

Ziegler–Natta Chain-Growth Polymerization: Overview

3.5K
Ziegler–Natta polymerization is another form of addition or chain‐growth polymerization used for synthesizing linear polymers over branched polymers. The catalyst used for polymerization is the Ziegler–Natta catalyst, named after Karl Ziegler and Giulio Natta, who developed it in 1953. This catalyst is an organometallic complex of titanium tetrachloride and triethyl aluminum, with the active form of the catalyst being an alkyl titanium compound. Using the Ziegler–Natta...
3.5K
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

164
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,...
164
Radical Chain-Growth Polymerization: Overview01:10

Radical Chain-Growth Polymerization: Overview

2.8K
Chain-growth or addition polymerization is successive addition reactions of monomers with a polymer chain. In radical chain-growth polymerization, the reaction proceeds via a free-radical intermediate. The free radical is formed from radical initiators, which spontaneously generate free radicals by homolytic fission. Organic peroxides (such as dibenzoyl peroxide, as shown in Figure 1) or azo compounds are popular radical initiators. A low concentration ratio of radical initiator to monomer is...
2.8K
Anionic Chain-Growth Polymerization: Overview01:20

Anionic Chain-Growth Polymerization: Overview

2.2K
The polymerization process that involves carbanion as an intermediate is called anionic polymerization. It is also a type of addition or chain-growth polymerization. Anionic polymerization gets initiated by a strong nucleophile such as an organolithium or a Grignard reagent. The most commonly used initiator for anionic polymerization is butyl lithium. Monomers involved in anionic polymerization must possess a vinyl group bonded to one or two electron-withdrawing groups. For instance,...
2.2K
PD Controller: Design01:26

PD Controller: Design

400
In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
400

You might also read

Related Articles

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

Sort by
Same author

Online Sensor Fault Detection Using Machine Learning Algorithms on a Laboratory-Scale Batch Reactor: LSTM Approach.

ACS omega·2026
Same author

Technoeconomic Analysis of a Novel Microwave Process to Produce Ethylene from Methane.

ACS omega·2026
Same author

Microwave-Driven Nonoxidative and Selective Conversion of Methane to Ethylene over Mn-Based Catalysts.

Industrial & engineering chemistry research·2025
Same author

Bauhinia monandra derived mesoporous activated carbon for the efficient adsorptive removal of phenol from wastewater.

Scientific reports·2025
Same author

Reinforcement Learning-Based Nonlinear Model Predictive Controller for a Jacketed Reactor: A Machine Learning Concept Validation Using Jetson Orin.

ACS omega·2025
Same author

Q‑Learning-Based Multivariate Nonlinear Model Predictive Controller: Experimental Validation on Batch Reactor for Temperature Trajectory Tracking.

ACS omega·2025

Related Experiment Video

Updated: Oct 20, 2025

Operation of the Collaborative Composite Manufacturing CCM System
10:09

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

6.8K

Development and Validation of Advanced Nonlinear Predictive Control Algorithms for Trajectory Tracking in Batch

Prajwal Shettigar J1, Kshetrimayum Lochan1, Gautham Jeppu1

  • 1Department of Mechatronics Engineering, Department of Chemical Engineering, Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India.

ACS Omega
|September 13, 2021
PubMed
Summary
This summary is machine-generated.

A new nonlinear model-based control (NMBC) strategy offers superior real-time trajectory tracking for acrylamide polymerization reactors compared to nonlinear model predictive control (NMPC), especially when combined with an unscented Kalman filter (UKF) state estimator.

More Related Videos

A Protocol for Real-time 3D Single Particle Tracking
10:16

A Protocol for Real-time 3D Single Particle Tracking

Published on: January 3, 2018

15.1K
Confocal Imaging of Confined Quiescent and Flowing Colloid-polymer Mixtures
10:56

Confocal Imaging of Confined Quiescent and Flowing Colloid-polymer Mixtures

Published on: May 20, 2014

12.3K

Related Experiment Videos

Last Updated: Oct 20, 2025

Operation of the Collaborative Composite Manufacturing CCM System
10:09

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

6.8K
A Protocol for Real-time 3D Single Particle Tracking
10:16

A Protocol for Real-time 3D Single Particle Tracking

Published on: January 3, 2018

15.1K
Confocal Imaging of Confined Quiescent and Flowing Colloid-polymer Mixtures
10:56

Confocal Imaging of Confined Quiescent and Flowing Colloid-polymer Mixtures

Published on: May 20, 2014

12.3K

Area of Science:

  • Chemical Engineering
  • Polymer Science
  • Control Systems

Background:

  • Acrylamide polymerization reactors require precise control for optimal product yield and safety.
  • Existing control strategies like nonlinear model predictive control (NMPC) face challenges in real-time implementation for complex reaction dynamics.
  • Accurate state estimation is crucial for effective advanced process control.

Purpose of the Study:

  • To develop and evaluate a computationally efficient nonlinear model-based control (NMBC) strategy for trajectory tracking in acrylamide polymerization.
  • To compare the performance of NMBC against NMPC in a real-time experimental setting.
  • To assess the impact of a nonlinear state estimator, the unscented Kalman filter (UKF), on control performance.

Main Methods:

  • Development of a nonlinear model-based control (NMBC) algorithm.
  • Implementation of nonlinear model predictive control (NMPC) for comparison.
  • Utilizing an unscented Kalman filter (UKF) for state estimation of the polymerization reaction.
  • Experimental validation on a lab-scale acrylamide polymerization reactor to track a time-varying temperature profile.

Main Results:

  • The NMBC strategy demonstrated significantly improved performance over NMPC.
  • Real-time trajectory tracking of the temperature profile was successfully achieved.
  • The integration of the UKF state estimator enhanced the effectiveness of both control algorithms, particularly NMBC.

Conclusions:

  • NMBC provides a computationally efficient and superior control solution for acrylamide polymerization compared to NMPC.
  • The combination of NMBC with UKF state estimation offers robust and accurate real-time control for complex polymerization processes.
  • This study highlights the potential of advanced model-based control strategies in optimizing chemical reactor operations.