TY - JFULL AU - R. Simutis and V. Galvanauskas and D. Levisauskas and J. Repsyte PY - 2014/6/ TI - Bioprocess Optimization Based On Relevance Vector Regression Models and Evolutionary Programming Technique T2 - International Journal of Agricultural and Biosystems Engineering SP - 440 EP - 444 VL - 8 SN - 1307-6892 UR - https://publications.waset.org/pdf/9998131 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 89, 2014 N2 - 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. ER -