443
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Multi-objective parameter estimation on cultivation of yeast Kluyveromyces marxianus var. lactis MC5

ORCID Icon
Article: 2300449 | Received 03 May 2023, Accepted 22 Dec 2023, Published online: 08 Jan 2024

References

  • Viesturs U, Simeonov I, Pencheva T, et al. Contemporary approaches to modelling, optimisation and control of biotechnological processes. Sofia, Bulgaria: Bulgarian Academy of Sciences; 2010.
  • Pencheva Т, Petrov M, Ilkova T, et al. Bioprocess engineering. Tzonkov S, Viesturs U, editors. Sofia, Bulgaria: Avangard Prima; 2006.
  • Doran Pauline M. Bioprocess engineering principles. 2nd ed. Oxford, UK: Academic Press; 2013.
  • Roeva O, Fidanova S. Application of genetic algorithms and ant colony optimization for modelling of E. coli cultivation process. In: Roeva O, editor, Real-World applications of genetic algorithms. Croatia: IntechOpen; 2012. doi: 10.5772/2674.
  • Wang F-S, Sheu J-W. Multiobjective parameter estimation problems of fermentation processes using a high ethanol tolerance yeast. Chem Eng Sci. 2000;55(18):1–9. doi: 10.1016/S0009-2509(00)00038-5.
  • Versyck K, Claes J, Van Impe F. Practical identification of unstructured growth kinetics by application of optimal experimental design. Biotechnol Prog. 1997;13(5):524–531. doi: 10.1021/bp970080j.
  • Strigul N, Dette H, Melas V. A practical guide for optimal designs of experiments in the monod model. Environ Modell Software. 2009;24(9):1019–1026. doi: 10.1016/j.envsoft.2009.02.006.
  • Gera N, et al. Growth kinetics and production of glucose oxidase using aspergillusniger NRRL326. Chem Biochem Eng Q. 2008;22(3):315–320.
  • Oliveira S, Oliveira R, Tacin M, et al. Kinetic modeling and optimization of a batch ethanol fermentation process. J Bioprocess Biotech. 2016;6(1):1–7.
  • Sendín OH, Vera J, Torres NV, et al. Model based optimization of biochemical systems using multiple objectives: a comparison of several solution strategies. Math Comp Model Dyn. 2006;12(5):469–487. doi: 10.1080/13873950600723442.
  • Vera J, De Atauri P, Cascante M, et al. Multicriteria optimization of biochemical systems by linear programming: application to production of ethanol by Saccharomyces cerevisiae. Biotechnol Bioeng. 2003;83(3):335–343. doi: 10.1002/bit.10676.
  • Zhou Y, Titchener-Hooker N. The application of a pareto optimisation method in the design of an integrated bioprocess. Bioprocess Biosyst Eng. 2003;25(6):349–355. doi: 10.1007/s00449-003-0318-0.
  • Storn R, Price K. Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J Global Optimization. 1997;11(4):341–359. doi: 10.1023/A:1008202821328.
  • Su T-L, Horng-Jhy J. Hybrid differential evolution for problems of kinetic parameter estimation and dynamic optimization of an ethanol fermentation process. Ind Eng Chem Res. 2001;40(13):2876–2885.
  • Levitin G. Genetic algorithms in reliability engineering. Reliab Eng Syst Saf. 2006;91(9):975–976. doi: 10.1016/j.ress.2005.11.007.
  • Adeyemo J, Enitan A. Optimization of fermentation processes using evolutionary algorithms – A review. Sci Res Essays. 2011;6(7):1464–1472.
  • Deb K. Multi-objective optimisation using evolutionary algorithms: an introduction. In: Wang L, Ng A, Deb K, editors. Multi-objective evolutionary optimisation for product design and manufacturing. London: Springer; 2011.
  • Pedrozo H, Dallagnol A, Schvezov C. Genetic algorithm applied to simultaneous parameter estimation in bacterial growth. J Bioinform Comput Biol. 2021;19(1):2050045. doi: 10.1142/S0219720020500456.
  • Roeva O. A modified genetic algorithm for a parameter identification of fermentation processes. Biotechnol Biotechnol Equip. 2006;20(1):202–209. doi: 10.1080/13102818.2006.10817333.
  • Sergienko I, Parasyuk N, Kaspshitskaya M. A fuzzy problem of multiparametric choice of optimal solutions. Cybern Syst Anal. 2003;39(2):163–173. doi: 10.1023/A:1024731004624.
  • Tonon F, Bernardini А. Multiobjective optimization of uncertain structures through fuzzy sets and random set theory. Computer Aided Civil Eng. 1999;14(2):119–140. doi: 10.1111/0885-9507.00135.
  • Chen Y, Wang F-S. Crisp and fuzzy optimization of a fed-batch fermentation for ethanol production. Ind Eng Chem Res. 2003;42(26):6843–6850. doi: 10.1021/ie0210107.
  • Wang F-S, Jing C-H, Tsao G. Fuzzy-decision-making problems of fuel ethanol production using a genetically engineered yeast. Ind Eng Chem Res. 1998;37(8):3434–3443. doi: 10.1021/ie970736d.
  • Petrov M, Ilkova T. Fuzzy-decision-making problem of L-lysine production. Chem Biochem Eng Q. 2012;26(3):257–265.
  • Petrov M, Ilkova T. Application of the intercriteria analysis for selection of growth rate models for cultivation of strain Kluyveromyces marxianus var. lactis MC 5. Notes Intuit Fuzzy Sets. 2015;21(5):49–60.
  • Petrov M, Ilkova T. Intercriteria decision analysis for choice of growth rate models of batch cultivation by strain Kluyveromyces marxianus var. lactis MC5. J Int Sci Publ Mater Methods Technol. 2016;10:468–486.
  • Petrov М. Modelling and multi-criteria decision analysis for selecting growth rate for batch cultivation of yeast Kluyveromyces marxianus var. lactis MC 5. Part I: modelling with different types of growth rate models. Bulg Chem Commun. 2021;53(4):418–423.
  • Petrov М. Modelling and multi-criteria decision analysis for selecting growth rate for batch cultivation of yeast Kluyveromyces marxianus var. lactis MC 5. Part II: multi-criteria decision analysis for selecting growth rate model. Bulg Chem Commun. 2021;53(4):436–441.
  • Atanassov K, Mavrov D, Atanassova V. Intercriteria decision making: a new approach for multicriteria decision making, based on index matrices and intuitionistic fuzzy sets. Issues Intuit Fuzzy Sets Gen Nets. 2014;11:1–8.
  • Brans J, Mareschal B, Vincke P. PROMETHEE: a new family of outranking methods in multicriteria analysis. Oper Res. 1984;3:477–490.
  • Brans J, Mareschal B, Vincke P. How to select and how to rank projects: the PROMETHEE method. Eur J Oper Res. 1986;24(2):228–238. doi: 10.1016/0377-2217(86)90044-5.
  • Petrov M. Modelling and use of inter-criteria decision analysis for selecting growth rate models for batch cultivation of yeast Kluyveromyces marxianus var. lactis MC 5. Fermentation. 2021;7(3):163. doi: 10.3390/fermentation7030163.
  • Petrov M, Ilkova T, Vanags J. Modelling of a batch whey cultivation of Kluyveromyces marxianus var. lactis MC 5 with investigation of mass transfer processes in the bioreactor. Int. J. Bioautomation. 2015;19(1):S81–S92.
  • http://www.promethee-gaia.net/software.html.
  • IMSL. Math/library user’s manual. Houston: IMSL, Inc.; 1991.
  • COMPAQ. Visual FORTRAN programmer’s guide, v. 6.6. Houston, Texas: Compaq Computer Corporation; 2001.
  • Petrov M, Ilkova T, Tzonkov S. Modeling and fuzzy optimization of whey fermentation by Kluyveromyces marxianus var. lactis MC5. Chem Biochem Eng Q. 2005;19(1):49–55.
  • Ilkova T, Petrov M. Dynamic and neuro-dynamic optimization of a fed-batch fermentation process, lecture notes in computer science: lecture notes in artificial intelligence. Vol. 5253. Berlin, Heidelberg: Springer; 2007. pp. 365–369.
  • Petrov М. Multiple objective optimization and optimal control of fermentation processes. Int. J. Bioautomation. 2008;10:21–30.
  • Petrov M, Ilkova T. A combined algorithm for multi-objective fuzzy optimization of whey fermentation. Chem Biochem Eng Q. 2009;23(2):153–160.
  • Ilkova T, Petrov M, Roeva O. Optimization of a whey bioprocess using neuro-dynamic programming strategy. Biotechnol Biotechnol Equip. 2012;26(5):3249–3253. doi: 10.5504/BBEQ.2012.0063.
  • Ilkova T, Petrov M. Neuro-fuzzy based model of batch fermentation of Kluyveromyces marxianus var. lactis MC5. Biotechnol Biotechnol Equip. 2014;28(5):975–979. doi: 10.1080/13102818.2014.944364.