Comparison of optimization algorithms for modeling of Haldane-type growth kinetics during phenol and benzene degradation

Su Youn Kang, Sang Gil Lee, Dong Ju Kim, Jaemin Shin, Junseok Kim, Soon-Jae Lee, Jae Woo Choi

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

In this study, three optimization algorithms (discretized domain, Monte Carlo, steepest descent) were compared to determine the best algorithm for estimation of Haldane-type microbial growth kinetic parameters. Application of these algorithms to growth data measured during phenol and benzene degradation showed different results in the estimated parameters obtained under various boundary conditions and growth phases. Regardless of the specific algorithm used, the factor with the greatest influence on parameter estimation was the boundary condition for the half-saturation constant (KS), although the parameters were also sensitive to the growth phase for phenol. Among the three algorithms, Monte Carlo was found to be the best and most consistent. The estimated parameters of phenol and benzene using an appropriate boundary value of KS were comparable with the outputs reported in previous studies, but those derived with inappropriate boundary values were not consistent with previously reported data.

Original languageEnglish
Pages (from-to)118-124
Number of pages7
JournalBiochemical Engineering Journal
Volume106
DOIs
Publication statusPublished - 2016 Feb 15

Fingerprint

Growth kinetics
Phenol
Benzene
Phenols
Degradation
Growth
Boundary conditions
Kinetic parameters
Parameter estimation

Keywords

  • Benzene
  • Growth kinetics
  • Kinetic parameters
  • Microbial growth
  • Modeling
  • Optimization
  • Phenol

ASJC Scopus subject areas

  • Biotechnology
  • Bioengineering
  • Biomedical Engineering
  • Environmental Engineering

Cite this

Comparison of optimization algorithms for modeling of Haldane-type growth kinetics during phenol and benzene degradation. / Kang, Su Youn; Lee, Sang Gil; Kim, Dong Ju; Shin, Jaemin; Kim, Junseok; Lee, Soon-Jae; Choi, Jae Woo.

In: Biochemical Engineering Journal, Vol. 106, 15.02.2016, p. 118-124.

Research output: Contribution to journalArticle

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