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Comparative Study of Intelligent Soft-Sensors for Bioprocess State Estimation

Rimvydas Simutis, Vytautas Galvanauskas, Donatas Levisauskas, Jolanta Repsyte, and Vygandas Vaitkus
Process Control Department, Kaunas University of Technology, Kaunas, 51368, Lithuania
Abstract—In this article, application of soft-sensors for indirect determination of biomass and product concentration in a complex fed-batch biotechnological process is discussed. Three advanced techniques for soft-sensor design were investigated: feed-forward artificial neural networks, support vector regression model, and relevance vector regression model. Glucose /lactose feed rates and oxygen uptake rate along with its integrated quantity were used as direct reference measurements for estimation. Estimation quality of analyzed soft-sensors was tested using data generated by mechanistic process model. All three analyzed estimation techniques provided very similar estimation results from statistical point of view; nevertheless employment of regression models has some advantage because of its simplicity. Based on that, recommendations for application of the elaborated soft-sensors are given.

Index Terms—state estimation, biomass and product concentrations, artificial neural networks, support vector regression, relevance vector regression.

Cite: Rimvydas Simutis, Vytautas Galvanauskas, Donatas Levisauskas, Jolanta Repsyte, and Vygandas Vaitkus, "Comparative Study of Intelligent Soft-Sensors for Bioprocess State Estimation," Journal of Life Sciences and Technologies, Vol. 1, No. 3, pp. 163-167, September 2013. doi: 10.12720/jolst.1.3.163-167
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