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Estimation of Kinetic Parameters by a Hybrid Algorithm of Nonlinear Model Based Differential Evolution for Lactic Acid Production

  • J. Satya Eswari EMAIL logo


The kinetic model parameters are estimated for lactic acid production using mixed microbial consortium in batch fermentation by using different optimization methods. For every time interval concentrations are measured and formulated the parameter appraisal delinquent. Cellular progression kinetic model of exponential or logistic was verified for the effect of various substrates and lactic acid production of mixed culture. This paper proposed hybrid algorithm such as nonlinear models in conjunction with differential evolution and kinetic model. The nonlinear regression with graphical method and Nelder-Mead simplex linked kinetic model was compared with the differential evolution for parameter estimation. The optimized kinetic parameters are found to be within the range of experimental conditions for which the model is developed offers a significant enhancement of lactic acid production. From the computational results, the proposed kinetic model linked differential evolution strategy is thus found effective in exploring the input search space and optimizing the kinetic parameters.


Monetary backing from DST over the donation SR/wos-a/ET-20/2009 is appreciatively approved.


1. Akerberg C, Hofvendahl K, Zacchi G, Hahn HB. Modelling the influence of pH, temperature, glucose and lactic acid concentrations on the kinetics of lactic acid production by Lactococcus lactis ssp. Lactis ATCC 19435 in whole-wheat flour. Appl Microbiol Biotechnol 1998;49:682–90.10.1007/s002530051232Search in Google Scholar

2. Law BA, Sezgin E, Sharpe ME. Amino acid nutrition of some commercial cheese starters in relation to their growth in supplemented whey media. J Dairy Res 1976;43:291.10.1017/S0022029900015855Search in Google Scholar

3. Reiter R, Oram JD. Nutritional studies on cheese starters I. Vitamin and amino acid requirements of single strain starters. J Dairy Res 1962;29:63.Search in Google Scholar

4. Bautista E, Dahiya RS, Speck ML. Identification of compounds causing symbiotic growth of Streptococcus thermophilus and Lactobacillus bulgaricus in milk. J Dairy Res 1966;33:299–307.10.1017/S0022029900011985Search in Google Scholar

5. Galesloot TE, Hassing F, Verenga HA. Symbiosis in yogurt.Stimulation of Lactobacillus bulgaricus by a factor produced by Streptococcus thermophilus. Neth Milk Dairy J 1968;22:50–63.Search in Google Scholar

6. Moon NJ, Reinbold GW. Commensalism and competition in mixed cultures of Lactobacillus bulgaricus and Streptococcus thermophilus. J Milk Food Technol 1950;39:337–341.Search in Google Scholar

7. Pette JW, Lolkema H. Groeifactoren voor Streptococcus thermophilus. Neth. Milk Dairy J 1950;4:209–24.Search in Google Scholar

8. Kavitha S. Production of Endoglucanase in Mixed Culture of Trichderma viride and Aspergillus niger - Kinetics and Modeling. Int J Chem Tech Res. 2011;3:1845–50.Search in Google Scholar

9. Storn R, Price KV. Minimizing the Real Function of the ICEC’96 Contest by Differential Evolution. IEEE Conf. Evolution Comput 1996;842.10.1109/ICEC.1996.542711Search in Google Scholar

10. Storn R, Price KV. Differential Evolution: A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. J. Global Opt 1997;11:341.10.1023/A:1008202821328Search in Google Scholar

11. Satya Eswari J, Venkateswarlu Ch. Evaluation of Kinetic Parameters of an Anaerobic Biofilm Reactor Treating Pharmaceutical Industry Wastewater by Ant Colony Optimization Environmental Engineering Science. Environmental engineering Science 2013;30:527-35.10.1089/ees.2012.0158Search in Google Scholar PubMed PubMed Central

12. Satya Eswari J, Venkateshwarlu Ch. Optimization of culture conditions for Chinese hamster ovary (CHO) cells production using differential evolution. Int J Pharm Pharm Sci 2012;4:465–70.Search in Google Scholar

13. Satya Eswari J, Anand M, Venkateswarlu Ch. Optimum culture medium composition for Rhamnolipid production by pseudomonas aeruginosa AT10 using a novel multi-objective optimization method. Journal of chemical technology and Biotechnology. 2012;88:271–9.10.1002/jctb.3825Search in Google Scholar

14. Satya Eswari J, Venkateswarlu Ch. Dynamic Modelling and Metabolic Flux Analysis for Optimized Production of Rhamnolipids. Chemical engineering communications, in press, 205. 0098-6445, 1563-5201, 2015.Search in Google Scholar

15. Satya Eswari J, Anand M, Venkateswarlu Ch. Sadhana - Academy Proceedings in Engineering Science, Optimum culture medium composition for Lipopeptide production by Bacillus subtilis using response surface model-based ant colony optimization, in press. 2015. 0973-7677.10.1007/s12046-015-0451-xSearch in Google Scholar

16. Nelder JA, Mead R. A simplex method for function minimization. The Comp J 1965;7:308–13.10.1093/comjnl/7.4.308Search in Google Scholar

17. Powell MJD. Efficient method for finding the minimum of a function of several variables without calculating derivatives. The Comp J 1964;7:155–62.10.1093/comjnl/7.2.155Search in Google Scholar

18. Spendley W, Hext GR, Himsworth FR. Sequential application of simplex design. In optimization and evaluationary operations. Technometrics 1962;4:441–61.10.1080/00401706.1962.10490033Search in Google Scholar

19. Yarbro LA, Deming SN. Selection and preprocessing of factors for simplex optimization. Analytica Chimica Acta 1974;73:391–8.10.1016/S0003-2670(01)85476-3Search in Google Scholar

20. Jacoby SLS, Kowalik JS, Pizzo JT. Iterative methods for nonlinear optimization problems. Prentice-Hall press, 1972.Search in Google Scholar

21. Nadia TA. Dawood, Synthesis and antimicrobial activity of 1-(4-Aryl-2-Thiazolyl)- and 1-(4-Aryl-2-Oxazolyl)-3, 5- diaryl – pyrazoline derivatives. J. Chem. Pharm. Res, 2011;3:11–21.Search in Google Scholar

22. Jayalakshmi T, Krishnamoorthy P, Ramesh kumar G, Sivamani P. Optimization of culture conditions for keratinase production in Streptomyces sp. JRS19 for chick fether wastes degradation. J Chem Pharm Res 2011;3:498–503.Search in Google Scholar

23. Sánchez RF, Bosch Ojeda C, Cano Pavón JM. J Chem Pharm Res 2010;2:166–73.Search in Google Scholar

24. Gerhard K, Hoppe GSH. Ethanol inhibition of continuous anaerobic yeast growth. Biotechnol Lett 1982;4:39–44.10.1007/BF00139280Search in Google Scholar

25. Gough S, Flynn F, Hack CJ, Marchant R. Optimization of fermentation parameters for maximization of actinomycin D production. App Microb Biotechnol 1996;46:187–90.10.1007/s002530050803Search in Google Scholar PubMed

26. Reddy PRM, Mrudula S, Ramesh B, Reddy G, Seenayya G. Production of thermostable pullulanase by clostridium thermosulphurogenes SV2 in solid state fermentation: Optimization of enzymes laching conditions using response surface methodology. Bioproc Biosys Engg 2000;23:107–12.10.1007/PL00009116Search in Google Scholar

27. Kiran K, Satya Eswari J, Venkateswarlu C. A hierarchical artificial neural system for genera classification and species identification in mosquitoes. Appl. Artif Intell 2012;26:903–20.10.1080/08839514.2012.731342Search in Google Scholar

Received: 2015-6-9
Revised: 2015-11-3
Accepted: 2015-11-3
Published Online: 2015-12-4
Published in Print: 2016-6-1

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