{"title":"Bioprocess Optimization Based On Relevance Vector Regression Models and Evolutionary Programming Technique","authors":"R. Simutis, V. Galvanauskas, D. Levisauskas, J. Repsyte","volume":89,"journal":"International Journal of Agricultural and Biosystems Engineering","pagesStart":441,"pagesEnd":445,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/9998131","abstract":"
This paper proposes a bioprocess optimization procedure based on Relevance Vector Regression models and evolutionary programming technique. Relevance Vector Regression scheme allows developing a compact and stable data-based process model avoiding time-consuming modeling expenses. The model building and process optimization procedure could be done in a half-automated way and repeated after every new cultivation run. The proposed technique was tested in a simulated mammalian cell cultivation process. The obtained results are promising and could be attractive for optimization of industrial bioprocesses.<\/p>\r\n","references":"[1]\tJ. H. Nielsen, J. Villadsen, G. Lid\u00e9n, Bioreaction Engineering Principles, Kluwer Academic\/Plenum Publishers, New York, 2003.\r\n[2]\tB. Xu, M. Jahic, S.-O. Enfors, \"Modeling of Overflow Metabolism in Batch and Fed-Batch Cultures of Escherichia coli,\u201d Biotechnology Progress 15, 1999, pp.81-90.\r\n[3]\tB. Sonnleitner, \"Measurement, monitoring, modelling and control,\u201d inBasic Biotechnology, 3rd ed., C. Ratledge, B. Kristiansen (eds). Cambridge University Press, 2006, pp. 251-270.\r\n[4]\tM. L. Thompson, M. A. Kramer, \"Modeling chemical processes using prior knowledge and neural networks,\u201d AIChE Journal 40 (8), 1994, pp. 1328-1340.\r\n[5]\tJ. Schubert, R. Simutis, M. Dors, I. Havlik, A. L\u00fcbbert, \"Bioprocess optimization and control: Application of hybrid modelling,\u201d Journal of Biotechnology 35, 1994, pp. 51-68.\r\n[6]\tS. Gnoth, R. Simutis, A. L\u00fcbbert, \"Selective expression of the soluble product fraction in Escherichia coli cultures employed in recombinant protein production processes,\u201d Applied Microbiology and Biotechnology 87 (6), 2010, pp. 2047-2058.\r\n[7]\tJ. Peres, R. Oliveira, S. F. de Azevedo, \"Bioprocess hybrid parametric\/nonparametric modelling based on the concept of mixture of experts,\u201d Biochemical Engineering Journal 39 (1), 2008, pp. 190-206.\r\n[8]\tP. Kadlec, B. Gabrys, S. Strandt,\"Data-driven soft sensors in the process industry,\u201d Comput. Chem. Eng., vol. 33, 2009, pp. 795\u2013814.\r\n[9]\tC. M. Bishop, \"Pattern recognition and machine learning,\u201d Springer, 2006.\r\n[10]\tM. E. Tipping, \"Sparse Bayesian Learning and the Relevance Vector Machine,\u201d Journal of Machine Learning Research 1, 2001, pp. 211\u2013244.\r\n[11]\tSparseBayes Version 2.0 software package for Matlab, 2013, http:\/\/www.relevancevector.com.\r\n[12]\tD. J. Fogel, \"Evolutionary Computation: toward a new philosophy of machine intelligence,\u201d IEEE Press, New York, 1995.\r\n[13]\tM. Aehle, K. Bork, S. Schaepe, A. Kuprijanov, R. Horstkorte, R.Simutis, A. L\u00fcbbert, \"Increasing batch-to-batch reproducibility of CHO-cell cultures using a model predictive control approach,\u201d Cytotechnology. Dordrecht : Springer. ISSN 0920-9069, 2012, Vol. 64, iss. 6, pp. 623-634.\r\n","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 89, 2014"}