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Enhancing the Performance of Evolutionary Algorithm by Differential Evolution for Optimizing Distillation Sequence.

Zehua Hu1, Peilong Li2, Yefei Liu1

  • 1College of Chemical Engineering, Nanjing Tech University, Nanjing 211816, China.

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Summary
This summary is machine-generated.

This study optimized chemical distillation sequences using evolutionary algorithms, finding NSGA-II-DE effective for reducing costs and CO2 emissions in triple-column processes.

Keywords:
Aspen Plusdistillation process synthesisdistillation sequenceevolutionary algorithm

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Area of Science:

  • Chemical Engineering
  • Process Optimization
  • Environmental Sustainability

Background:

  • Distillation sequence synthesis is critical for chemical process efficiency and reducing greenhouse gas emissions.
  • Optimizing both process structure and operating parameters is essential for complex chemical engineering problems.

Purpose of the Study:

  • To evaluate the effectiveness of six evolutionary algorithms for distillation sequence synthesis.
  • To identify the optimal operating parameters for a triple-column distillation process to minimize cost and CO2 emissions.

Main Methods:

  • Encoding distillation sequences using a binary tree concept.
  • Evaluating six evolutionary algorithms on a 14-component distillation sequence synthesis problem.
  • Applying the best-performing algorithm (NSGA-II-DE) to optimize triple-column distillation parameters.

Main Results:

  • NSGA-II-DE outperformed the other five evolutionary algorithms in solving the distillation sequence synthesis problem.
  • Optimization using NSGA-II-DE led to significant reductions in total annual costs.
  • A notable decrease in CO2 emissions was achieved through the optimized operating parameters.

Conclusions:

  • The binary tree encoding and NSGA-II-DE provide an effective approach for optimizing distillation sequences.
  • The developed method successfully reduces both economic costs and environmental impact in chemical processes.
  • This research contributes to more sustainable and cost-effective chemical manufacturing.