Nature inspired methods for optimization
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Nature inspired methods for optimization

A Julia primer for process engineering

About the Book

This book develops and uses three nature inspired methods for optimization. All the code for these methods, written in the Julia language, is presented. Julia is a language which, to quote the developers, "combines expertise from the diverse fields of computer science and computational science to create a new approach to numerical computing." The methods presented are used to solve three case studies from process systems engineering and, again, all the code is included and explained in the text. The book demonstrates the potential of these methods and illustrates the key features of the Julia language.

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  • Categories

    • Computers and Programming
    • Systems Engineering
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About the Author

Eric Fraga
Eric Fraga

Professor of Process Systems Engineering, University College London (UCL).

Table of Contents

  • 1. Introduction
    • 1.1. Optimization
    • 1.2. Process systems engineering
    • 1.3. Nature inspired optimization
    • 1.4. Literate programming
    • 1.5. The Julia language
    • 1.6. Representation
  • 2. Nature inspired optimization methods
    • 2.1. Introduction
    • 2.2. Shared functionality
    • 2.3. Genetic Algorithms
    • 2.4. Particle Swarm Optimization
    • 2.5. Plant Propagation Algorithms
    • 2.6. Random search
    • 2.7. Illustrative example
  • 3. Chlorobenzene purification process
    • 3.1. The process
    • 3.2. The Jacaranda system
    • 3.3. The Jacaranda Chlorobenzene process model
    • 3.4. The Chlorobenzene process objective function
    • 3.5. Example evaluation
    • 3.6. Results
    • 3.7. Summary
  • 4. Plug flow reactor
    • 4.1. Introduction
    • 4.2. The formulation for optimization
    • 4.3. The representation
    • 4.4. Implementation
    • 4.5. Alternative representation
    • 4.6. Implementation
  • 5. Heat exchanger networks
    • 5.1. Heat exchanger networks
    • 5.2. Representation of a network superstructure
    • 5.3. Network model
    • 5.4. Problem definition
    • 5.5. Network evaluation
    • 5.6. Optimization
    • 5.7. Summary
  • 6. Conclusions
  • 7. Bibliography
  • 8. Appendix. The Nature Inspired Optimization module
  • 9. Appendix. Supplementary HEN functions
    • 9.1. Textual output
    • 9.2. Adjacency matrix presentation of network
    • 9.3. Graphical network representation

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