Optimisation of Operating Conditions in Fed-Batch Baker’s Yeast Fermentation

• Authors: Ilknur Atasoy , Mehmet Yuceer , Ridvan Berber
• Languages of publication: EN

Abstracts

EN

Saccharamyces cerevisia known as baker’s yeast is a product used in various food industries. Worldwide economic competition makes it a necessity that industrial processes be operated in optimum conditions, thus maximisation of biomass in production of saccharamyces cerevisia in fedbatch reactors has gained importance. The facts that the dynamic fermentation model must be considered as a constraint in the optimisation problem, and dynamics involved are complicated, make optimisation of fed-batch processes more difficult. In this work, the amount of biomass in the production of baker’s yeast in fed-batch fermenters was intended to be maximised while minimising unwanted alcohol formation, by regulating substrate and air feed rates. This multiobjective problem has been tackled earlier only from the point of view of finding optimum substrate rate, but no account of air feed rate profiles has been provided. Control vector parameterisation approach was applied the original dynamic optimisation problem which was converted into a NLP problem. Then SQP was used for solving the dynamic optimisation problem. The results demonstrate that optimum substrate and air feeding profiles can be obtained by the proposed optimisation algorithm to achieve the two conflicting goals of maximising biomass and minimising alcohol formation.

Keywords

EN

fed-batch fermenters; baker’s yeast; dynamic optimisation; SQP; nonlinear programming

• Publisher: De Gruyter Open
• Journal: Chemical and Process Engineering
• Year: 2013
• Volume: 34
• Issue: 1
• Pages: 175-186

Dates

published

1 - 03 - 2013

online

02 - 04 - 2013

Contributors

author

Ilknur Atasoy

• Ankara University, Department of Chemical Engineering, Faculty of Engineering, Tandogan, 06100 Ankara, Turkey

author

Mehmet Yuceer

• Inonu University, Department of Chemical Engineering, Faculty of Engineering, 44280 Malatya, Turkey

author

Ridvan Berber

• Ankara University, Department of Chemical Engineering, Faculty of Engineering, Tandogan, 06100 Ankara, Turkey

References

• Agun U., 2002. Optimisation of Feeding Rate Profile in Yeast Fermentation. MSci. Thesis, Ankara University, Turkey, 2002.
• Balku S., Yuceer M., Berber R., 2009. Control vector parameterization approach in optimisation of alternating aerobic-anoxic systems. Optim. Contr. Appl.Met., 30, 573-584. DOI: 10.1002/oca.883573-584.[Crossref]
• Berber R., Pertev C., Turker M., 1999. Optimisation of feeding profile for baker’s yeast production by dynamic programming. Bioproces Eng., 20, 263-269. DOI: 10.1007/PL00009047.[Crossref]
• Besli N., Turker M., Gul E., 1995. Design and simulation of a fuzzy controller for fed-batch fermentation. Bioproces Eng., 13, 141-148. DOI: 10.1007/BF00369697.[Crossref]
• Fletcher R., 1987. Practical Methods of Optimisation. John Wiley & Sons, New York.
• Furlonge H.I., Pantelides C.C., Sorensen E., 1999. Optimal operation of multivessel batch distillation columns. AIChE J., 45, 781-800. DOI: 10.1002/aic.690450413.[Crossref]
• Gill P.E., Murray W., Wright M.H., 1981. Practical Optimisation. Academic Press, London.
• Hocalar A., 2002. Prediction model of industrial baker’s yeast fermentation. MSci. Thesis, Kocaeli University, Turkey.
• Ishikawa T., Natori Y., Liberis L., Pantelides C.C., 1997. Modelling and optimisation of an industrial batch process for the production of dioctyl phthalate. Comput. Chem. Eng., 21, 1239-/1244. DOI: 10.1016/S0098-1354(97)87672-7.[Crossref]
• Johnson A., 1987. The control of fed-batch fermentation processes-a survey. Automatica, 23, 691-705. DOI: 10.1016/0005-1098(87)90026-4.[Crossref]
• Karakuzu C., 2003. Modelling and control of industrial baker’s yeast fermentation with neural network and fuzzylogic. PhD Thesis, Kocaeli University, Turkey.
• Karakuzu C., Türker M. Öztürk S., 2006. Modelling, on-line state estimation and fuzzy control of production scale fed-batch baker’s yeast fermentation. Control Eng. Pract., 14, 959-974. DOI: 10.1016/j.conengprac.2005.05.007.[Crossref]
• Krothapally M., Palanki, S., 1999. A neural network strategy for end-point optimisation of batch processes. ISA Trans., 38, 383-396. DOI: 10.1016/S0019-0578(99)00031-2.[Crossref]
• Nocedal J., Wright S.J., 1999. Numerical Optimisation. Springer-Verlag, New York, NY.
• Parulekar S.J., 1992. Analytical optimisation of some single-cycle and repeated fed-batch fermentations. Chem. Eng. Sci., 47, 4077-4097. DOI: 10.1016/0009-2509(92)85159-9.[Crossref]
• Pertev C., Turker M., Berber R., 1997. Dynamic modeling, sensitivity analysis and parameter estimation of industrial yeast fermenters. Comput. Chem Engng., 21, 739-744. DOI: 10.1016/S0098-1354(97)87591-6.[Crossref]
• Peters N., Guay M., DeHaan D., 2006. Real-time dynamic optimisation of non-linear batch systems. ADCHEM2006, Gramado, Brazil, 2-5 April 2006, 227-232.
• Riascos C.A.M., Pinto J.M., 2004. Optimal control of bioreactors: a simultaneous approach for complex systems. Chem. Eng. J., 99, 23-34. DOI: 10.1016/j.cej.2003.09.002.[Crossref]
• Ronen M., Shabtai Y., Guterman H., 2002. Optimisation of feeding profile for a fed-batch bioreactor by an evolutionary algorithm. J. Biotechnol., 97, 253-263. DOI: 10.1016/S0168-1656(02)00106-2.[Crossref]
• Roubos J.A., van Straten G., van Boxtel A.J.B., 1999. An evolutionary strategy for fed-batch bioreactor optimisation; concepts and performance. J. Biotechnol., 67, 173-187. DOI: 10.1016/S0168-1656(98)00174-6.[Crossref]
• Roy S., Gudi R.D., Venkatesh K.V., Shah S.S., 2001. Optimal control strategies or simultaneous saccharification and fermentation of starch. Bioprocess Chem., 36, 713-722. DOI: 10.1016/S0032-9592(00)00270-3.[Crossref]
• Schubert J., Simutis R., Dors M., Havlik I., Lubbert, A., 1994. Bioprocess optimisation and control: Application of hybrid modeling. J. Biotechnol., 35, 51-68. DOI: 10.1016/0168-1656(94)90189-9.[Crossref]
• Sonnleitner B., Kappeli O., 1986. Growth of Saccharomyces cerevisiae is controlled by its limited respiration capacity; formulation and verification of a hypothesis. Biotechnol. Bioeng., 28, 927-937. DOI: 10.1002/bit.260280620.[Crossref]
• Sorensen E., Macchietto S., Stuart G., Skogestad S., 1996. Optimal control and on-line operation of reactive batch distillation. Comput. Chem. Eng., 20, 1491-/1498. DOI: 10.1016/0098-1354(95)00234-0.[Crossref]
• Valentino S., Srinivasan B., Holmberg U., Bonvin D., Cannizarro C., Rhiel M., von Stockar U., 2003. Optimal operation of fed-batch fermentations via adaptive control of overflow metabolite. Control Eng. Pract., 11, 665-674. DOI: 10.1016/S0967-0661(02)00172-7.[Crossref]
• Vassiliadis V., Sargent R.W.H., Pantelides C.C., 1994. Solution of a class of multistage optimisation problems. Part I, problems without path constraints. Ind. Eng. Chem. Res., 33, 2111-2122. DOI: 10.1021/ie00033a015.[Crossref]
• Yuceer M., Atasoy I., Berber R., 2008. A software for parameter estimation in dynamic models. Braz. J. Chem. Eng., 25, 813-821. DOI: 10.1590/S0104-66322008000400018.[Crossref]
• Yuzgec U., Turker M., Hocalar A., 2009. On-line evolutionary optimisation of an industrial fed-batch yeast fermentation process ISA Transactions, 48, 79-92. DOI: 10.1016/j.isatra.2008.09.001.[Crossref]

Identifiers

DOI

10.2478/cpe-2013-0015