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

Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

305
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
305
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

107
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
107
Heat Engines01:10

Heat Engines

3.1K
A heat engine is a device used to extract heat from a source and then convert it into mechanical work used for various applications. For example, a steam engine on an old-style train can produce the work needed for driving the train.
Whenever we consider heat engines (and associated devices such as refrigerators and heat pumps), we do not use the standard sign convention for heat and work. For convenience, we assume that the symbols Qh, Qc, and W represent only the amounts of heat transferred...
3.1K
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
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

7.5K
The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
7.5K
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

1.8K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
1.8K

You might also read

Related Articles

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

Sort by
Same author

Modelling the transformation of energy-intensive industries based on site-specific investment decisions.

Scientific reports·2024
Same author

Neonatal Early-Onset Infection With SARS-CoV-2 in a Newborn Presenting With Encephalitic Symptoms.

The Pediatric infectious disease journal·2020
See all related articles

Related Experiment Video

Updated: Sep 21, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.1K

A parallelized hybrid genetic algorithm with differential evolution for heat exchanger network retrofit.

Jan A Stampfli1,2, Donald G Olsen1, Beat Wellig1

  • 1Lucerne University of Applied Sciences and Arts, Competence Center Thermal Energy Systems and Process Engineering, Technikumstrasse 21, Horw 6048, Switzerland.

Methodsx
|May 27, 2022
PubMed
Summary

This study introduces a novel evolutionary algorithm for heat exchanger network retrofit. The customized approach effectively finds feasible solutions for complex, multi-period problems, improving optimization strategies.

Keywords:
Differential evolutionGenetic algorithmHeat exchanger network (HEN)Meta-heuristicsMulti-periodParallel processingRetrofit

More Related Videos

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
09:04

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

3.2K
Designing Automated, High-throughput, Continuous Cell Growth Experiments Using eVOLVER
07:26

Designing Automated, High-throughput, Continuous Cell Growth Experiments Using eVOLVER

Published on: May 19, 2019

12.2K

Related Experiment Videos

Last Updated: Sep 21, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.1K
A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
09:04

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

3.2K
Designing Automated, High-throughput, Continuous Cell Growth Experiments Using eVOLVER
07:26

Designing Automated, High-throughput, Continuous Cell Growth Experiments Using eVOLVER

Published on: May 19, 2019

12.2K

Area of Science:

  • Chemical Engineering
  • Process Systems Engineering
  • Optimization Algorithms

Background:

  • Heat exchanger network (HEN) retrofit is crucial for process efficiency but challenging due to problem complexity and multi-period operations.
  • Deterministic algorithms often struggle to find feasible solutions in these complex scenarios.
  • Stochastic algorithms offer a promising alternative for navigating complex solution spaces.

Purpose of the Study:

  • To develop a customized evolutionary optimization algorithm for HEN retrofit problems.
  • To address the difficulties in finding feasible solutions for complex, multi-period HEN retrofit.
  • To enhance the efficiency and success rate of HEN retrofit optimization.

Main Methods:

  • A two-level evolutionary algorithm combining a genetic algorithm (GA) for topology optimization (discrete variables) and differential evolution (DE) for heat load optimization (continuous variables).
  • Implementation of penalizing and preserving strategies for effective constraint handling.
  • Parallelization of the GA evaluation, running DE on each chromosome concurrently across multiple cores.

Main Results:

  • The hybrid GA-DE approach successfully optimizes both the topology and heat loads of HENs.
  • The customized algorithm demonstrates enhanced capability in finding feasible solutions for complex retrofit problems.
  • Parallelization significantly speeds up the evaluation process for the genetic algorithm.

Conclusions:

  • The presented customized evolutionary algorithm is effective for heat exchanger network retrofit, particularly for multi-period operations.
  • The combination of GA and DE, along with parallelization, provides a robust and efficient optimization framework.
  • This approach offers a significant improvement over traditional deterministic methods for complex HEN retrofit challenges.