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Bioprocess and Biosystems Engineering

, Volume 41, Issue 11, pp 1679–1696 | Cite as

Dynamic optimization of fed-batch bioprocesses using flower pollination algorithm

  • Sarma Mutturi
Research Paper

Abstract

There exist several optimization strategies such as sequential quadratic programming (SQP), iterative dynamic programing (IDP), stochastic-based methods such as differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSA), and ant colony optimization (ACO) for finding optimal feeding profile(s) during fed-batch fermentations. Here in the present study, flower pollination algorithm (FPA) which is inspired by the pollination process in terrestrial flowering plants has been used for the first time to find the optimal feeding profile(s) during fed-batch fermentations. Single control variable, two control variables and state variable bounded problems were chosen to test the robustness of the FPA for optimal control problems. It was observed that FPA is computationally less intensive in comparison with other stochastic strategies. Thus, obtained results were compared to other studies and it has been found that the FPA converged either to newer optima or closer to the established global optimum for the cases studied.

Graphical abstract

Keywords

Optimal control Dynamic optimization Flower pollination algorithm Fed-batch bioreactor 

Abbreviations

ACO

Ant colony optimization

ANN

Artificial neural networks

CPU

Central processing unit

CVP

Control vector parameterization

DAE

Differential algebraic equation

DE

Differential evolution

DI

Deviation index

FPA

Flower pollination algorithm

GA

Genetic algorithm

IDP

Iterative dynamic programming

KKT

Karush–Kuhn–Tucker (conditions)

MODE

Multi-objective optimization differential evolution

MOFPA

Multi-objective flower pollination algorithm

NDF

Numerical differentiation formula

NSGA-II

Non-dominated sorting genetic algorithm -II

OCP

Optimal control problem

ODE

Ordinary differential equation

OFE

Objective function evaluations

PI

Performance index

PSA

Particle swarm algorithm

PSO

Particle swarm optimization

SQP

Sequential quadratic programming

VEGA

Vector evaluated genetic algorithm

Notes

Acknowledgements

The Director, CSIR—Central Food Technological Research Institute (CFTRI), Mysore, India, is also acknowledged for supporting this work.

Supplementary material

449_2018_1992_MOESM1_ESM.doc (130 kb)
Supplementary material 1 (DOC 130 KB)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Microbiology and Fermentation Technology DepartmentCSIR-Central Food Technological Research InstituteMysoreIndia
  2. 2.Academy of Scientific and Innovative ResearchGhaziabadIndia

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