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• W2100996838 abstract "This paper presents dynamic neural-network-based model-predictive control (MPC) structure for a baker's yeast drying process. Mathematical model consists of two partial nonlinear differential equations that are obtained from heat and mass balances inside dried granules. The drying curves that are obtained from granule-based model were used as training data for neural network (NN) models. The target is to predict the moisture content and product activity, which are very important parameters in drying process, for different horizon values. Genetic-based search algorithm determines the optimal drying profile by solving optimization problem in MPC. As a result of the performance evaluation of the proposed control structure, which is compared with the model based on nonlinear partial differential equation (PDE) and with feedforward neural network (FFN) models, it is particularly satisfactory for the drying process of a baker's yeast." @default.
• W2100996838 created "2016-06-24" @default.
• W2100996838 creator A5065478572 @default.
• W2100996838 creator A5070639012 @default.
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• W2100996838 date "2008-07-01" @default.
• W2100996838 modified "2023-10-01" @default.
• W2100996838 title "Dynamic Neural-Network-Based Model-Predictive Control of an Industrial Baker's Yeast Drying Process" @default.
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• W2100996838 doi "https://doi.org/10.1109/tnn.2008.2000205" @default.
• W2100996838 hasPublicationYear "2008" @default.
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