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

Thermal expansion and Thermal stress: Problem Solving01:27

Thermal expansion and Thermal stress: Problem Solving

1.4K
San Francisco's Golden Gate Bridge is exposed to temperatures ranging from -15 °C to 40 °C. At its coldest, the main span of the bridge is 1275 m long. Assuming that the bridge is made entirely of steel, what is the change in its length between these temperatures?
To solve the problem, first, identify the known and unknown quantities. The initial length (L) of the bridge is 1275 m, the coefficient of linear expansion (α) for steel is 12 x 10-6/°C, and the change in...
1.4K

You might also read

Related Articles

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

Sort by
Same author

Analysis of Different Organic Rankine and Kalina Cycles for Waste Heat Recovery in the Iron and Steel Industry.

ACS omega·2022
Same author

Artificial Neural Network Modeling and Numerical Simulation of Syngas Fuel and Injection Timing Effects on the Performance and Emissions of a Heavy-Duty Compression Ignition Engine.

ACS omega·2021
Same author

Entropy Generation Analysis of a Thermal Cracking Reactor.

ACS omega·2021
Same author

Thermodynamic, exergo-economic and exergo-environmental analysis of hybrid geothermal-solar power plant based on ORC cycle using emergy concept.

Heliyon·2020

Related Experiment Video

Updated: Sep 25, 2025

Optimize Flue Gas Settings to Promote Microalgae Growth in Photobioreactors via Computer Simulations
14:33

Optimize Flue Gas Settings to Promote Microalgae Growth in Photobioreactors via Computer Simulations

Published on: October 1, 2013

14.5K

Optimization of a Thermal Cracking Reactor Using Genetic Algorithm and Water Cycle Algorithm.

Peyman Roudgar Saffari1, Hesamoddin Salarian2, Ali Lohrasbi3

  • 1Department of Mechanical Engineering, Adiban Institute of Higher Education, Garmsar 35819-69855, Iran.

ACS Omega
|April 27, 2022
PubMed
Summary
This summary is machine-generated.

This study optimizes thermal cracking reactors for ethylene production, enhancing product yield and reducing energy consumption. Advanced algorithms like Genetic Programming and Water Cycle Algorithm improve efficiency and sustainability in the chemical industry.

More Related Videos

Coupling Carbon Capture from a Power Plant with Semi-automated Open Raceway Ponds for Microalgae Cultivation
08:17

Coupling Carbon Capture from a Power Plant with Semi-automated Open Raceway Ponds for Microalgae Cultivation

Published on: August 14, 2020

5.3K
Laboratory Production of Biofuels and Biochemicals from a Rapeseed Oil through Catalytic Cracking Conversion
11:33

Laboratory Production of Biofuels and Biochemicals from a Rapeseed Oil through Catalytic Cracking Conversion

Published on: September 2, 2016

14.0K

Related Experiment Videos

Last Updated: Sep 25, 2025

Optimize Flue Gas Settings to Promote Microalgae Growth in Photobioreactors via Computer Simulations
14:33

Optimize Flue Gas Settings to Promote Microalgae Growth in Photobioreactors via Computer Simulations

Published on: October 1, 2013

14.5K
Coupling Carbon Capture from a Power Plant with Semi-automated Open Raceway Ponds for Microalgae Cultivation
08:17

Coupling Carbon Capture from a Power Plant with Semi-automated Open Raceway Ponds for Microalgae Cultivation

Published on: August 14, 2020

5.3K
Laboratory Production of Biofuels and Biochemicals from a Rapeseed Oil through Catalytic Cracking Conversion
11:33

Laboratory Production of Biofuels and Biochemicals from a Rapeseed Oil through Catalytic Cracking Conversion

Published on: September 2, 2016

14.0K

Area of Science:

  • Chemical Engineering
  • Petrochemical Processes
  • Reaction Engineering

Background:

  • Ethylene is a crucial chemical building block, with global production reaching 150 million tons in 2016.
  • Current ethylene production relies on energy-intensive steam cracking of fossil fuels, accounting for 8% of petrochemical energy use.
  • Thermal cracking reactors are vital but present challenges in reaction, flow, momentum, and energy management.

Purpose of the Study:

  • To investigate the molecular mechanism and optimize a tubular thermal cracking reactor fed by propane.
  • To develop a systematic approach for optimizing thermal cracking reactors using a combination of machine learning and optimization algorithms.
  • To enhance product yield and reduce entropy generation in the thermal cracking process.

Main Methods:

  • Development of a reaction model for a tubular thermal cracking reactor.
  • Application of heat to the outer tube wall to resolve process issues.
  • Utilized Genetic Programming (GP) for objective function generation, combined with Water Cycle Algorithm (WCA) and Genetic Algorithm (GA) for multi-objective optimization.

Main Results:

  • Multi-objective GA optimization increased weighted product percentage to 61.13% and decreased entropy production rate by 16.51% compared to the base case.
  • Multi-objective WCA achieved a higher weighted product percentage of 61.81% and reduced entropy production rate by 18.77%.
  • Key decision variables influencing optimization included inlet gas temperature, pressure, air mass flow rate, and wall temperature.

Conclusions:

  • The proposed systematic optimization approach, integrating GP, WCA, and GA, significantly improves thermal cracking reactor performance.
  • Optimization successfully balances maximizing product yield and minimizing entropy generation, leading to more efficient and sustainable ethylene production.
  • The study demonstrates a substantial improvement in process efficiency and a reduction in energy intensity for a critical petrochemical process.