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Article; Bioinformatics

Functional state modelling approach validation for yeast and bacteria cultivations

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Pages 968-974 | Received 06 Feb 2014, Accepted 17 Apr 2014, Published online: 21 Oct 2014

References

  • Murray-Smith R, Johansen TA. Multiple model approaches to modelling and control. London: Taylor & Francis; 1997.
  • Zhang X-Ch, Visala A, Halme A, Linko P. Functional state modeling and fuzzy control of fed-batch aerobic baker's yeast process. J Biotech. 1994;37:1–10.
  • Zhang X-Ch, Visala A, Halme A, Linko P. Functional state modelling approach for bioprocesses: local models for aerobic yeast growth processes. J Process Control. 1994;4(3):127–134.
  • Pencheva T, Vassileva S, Ilkova T, Georgieva Y, Hitzmann B, Tzonkov S. Multimodel approach for modelling of biotechnological processes. Biotechnol Biotechnol Equipment. 2004;18(2):206–214.
  • Roeva O, Pencheva T, Georgieva Y, Hitzmann B, Tzonkov S. Implementation of functional state approach for modelling of Escherichia coli fed-batch cultivation. Biotechnol Biotechnol Equipment. 2004;18(3):207–214.
  • Vilums S, Grigs O. Application of functional state modelling approach for yeast Saccharomyces cerevisiae batch fermentation state estimation. In: Mednis M, editor. 5th International Scientific Conference on Applied Information and Communication Technologies. Proceedings; 2012 Apr 26–27; Jelgava, Latvia: Latvia University of Agriculture. p. 300–305.
  • Vilums S, Kozlinskis E, Brusbardis V. Application of functional state modelling approach for yeast Sacharomyces cerevisiae fed-batch fermentation modelling. 6th International Conference on Application of Information and Communication Technologies. Proceedings; 2012 Oct 17–19; Tbilisi, GA. p. 1–5.
  • Renard F, Wouwer A, Valentinotti S, Dumur D. A practical robust control scheme for yeast fed-batch cultures − an experimental validation. J Process Control. 2006;16(8):855–864.
  • Nagy Z. Model based control of a yeast fermentation bioreactor using optimally designed artificial neural networks. Chem Eng J. 2007;127(1–3):95–109.
  • Slavov Ts, Roeva O. Multiple non-linear model adaptive control of cultivation process: hardware-in-the-loop simulation of control system. CR Acad Bulg Sci. 2014;67(4):577–584.
  • Roeva O, Pencheva T, Viesturs U, Tzonkov S. Modelling of fermentation processes based on state decomposition. Int J Bioautomation. 2006;5:1–12.
  • Nielsen J, Villadsen J. Bioreaction engineering principles. New York (NY): Plenum Press; 1994.
  • Keseler IM, Collado-Vides J, Santos-Zavaleta A, Peralta-Gil M, Gama-Castro S, Muniz-Rascado L, Bonavides-Martinez C, Paley S, Krummenacker M, Altman T, Kaipa P, Spaulding A, Pacheco J, Latendresse M, Fulcher K, Sarker M, Shearer AG, Mackie A, Paulsen I, Gunsalus RP, Karp PD. EcoCyc: a comprehensive database of Escherichia coli biology. Nucleic Acids Res. 2011;39:D583–D590.
  • Goldberg DE. Genetic algorithms in search, optimization and machine learning. London: Addison Wesley Longman; 2006.
  • Angelova M, Pencheva T. Tuning genetic algorithm parameters to improve convergence time. Int J Chem Eng. 2011;2011:646917.
  • Angelova M, Pencheva T. Algorithms improving convergence time in parameter identification of fed-batch cultivation. CR Acad Bulg Sci. 2012;65(3):299–306.
  • Fidanova S, Roeva O, Ganzha M. ACO and GA for parameter settings of E. coli fed-batch cultivation model. In: Fidanova S, editor. Recent advances in computational optimization, Studies of computational intelligence. Vol. 470. Heidelberg, Switzerland: Springer; 2013. p. 51–71.
  • Roeva O, editor. Real-world application of genetic algorithms. Rijeka, Croatia: InTech; 2012.
  • Hjersted J, Henson MA. Population modeling for ethanol productivity optimization in fed-batch yeast fermenters. American Control Conference 2005. Proceedings; 2005 June 8-10; Portland. (USA): IEEE; 2005;5:3253–3258.
  • Angelova M, Atanassov K, Pencheva T. Purposeful model parameters genesis in simple genetic algorithms. Comput Math Appl. 2012;64:221–228.
  • Chen LZ, Chen XD, Nguang SK. Optimization of fed-batch fermentation processes using genetic algorithms based on cascade dynamic neural network models. In: Chen LZ, Nguang SK, Chen XD, editors. Modelling and optimization of biotechnological processes, Studies in computational intelligence. Vol. 15. Berlin: Springer; 2006. p. 57–70.
  • Chong Y, Yan A, Yang X, Cai Y, Chen J. An optimum fermentation model established by genetic algorithm for biotransformation from crude polydatin to resveratrol. Appl Biochem Biotechnol. 2012;166(2):446–457.
  • Pencheva T, Angelova M, Atanassov K. Genetic algorithms quality assessment implementing intuitionistic fuzzy logic. In: Vasant P, editor. Handbook of research on novel soft computing intelligent algorithms: theory and practical applications. Hershey (PA): IGI Global; 2014. p. 327–354.
  • Roeva O. Chapter 21. A comparison of simulated annealing and genetic algorithm approaches for cultivation model identification. In: Sabelfeld KK, Dimov I, editors. Monte Carlo methods and applications. Berlin: Walter de Gruyter; 2013. p. 193–201.
  • Chipperfield AJ, Fleming PJ, Pohlheim H, Fonseca CM. Genetic algorithm toolbox for use with MATLAB. User's guide. Version 1.2. UK: Dept. of Automatic Control and System Engineering. Sheffield, UK: University of Sheffield; 1994.