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Dynamic optimization of fed-batch bioprocesses using flower pollination algorithm.

Sarma Mutturi1,2

  • 1Microbiology and Fermentation Technology Department, CSIR-Central Food Technological Research Institute, Mysore, India. sarma.mutturi@gmail.com.

Bioprocess and Biosystems Engineering
|August 1, 2018
PubMed
Summary
This summary is machine-generated.

The flower pollination algorithm (FPA) offers an efficient method for optimizing fed-batch fermentation feeding profiles. This novel approach is computationally less intensive and achieves optimal or near-optimal results compared to existing strategies.

Keywords:
Dynamic optimizationFed-batch bioreactorFlower pollination algorithmOptimal control

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Area of Science:

  • Biochemical Engineering
  • Computational Biology
  • Optimization Algorithms

Background:

  • Fed-batch fermentation requires precise control of feeding profiles for optimal product yield.
  • Existing optimization strategies include sequential quadratic programming (SQP), iterative dynamic programming (IDP), and various stochastic methods like differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO), and ant colony optimization (ACO).

Purpose of the Study:

  • To introduce and evaluate the flower pollination algorithm (FPA) for determining optimal feeding profiles in fed-batch fermentations.
  • To assess the robustness and computational efficiency of FPA for optimal control problems.

Main Methods:

  • The flower pollination algorithm (FPA), inspired by plant pollination, was applied to optimize feeding profiles.
  • The algorithm's performance was tested on problems with single control variables, two control variables, and state variable bounds.

Main Results:

  • FPA demonstrated computational efficiency, being less intensive than other stochastic optimization strategies.
  • For the tested optimal control problems, FPA converged to new optima or results close to the established global optimum.

Conclusions:

  • FPA is a viable and efficient optimization strategy for fed-batch fermentation feeding profiles.
  • The algorithm shows promise for addressing complex optimal control problems in bioprocesses.