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• W1539308724 abstract "The industrial processes are governed generally by general principles of the physics and chemistry. With the aid of data acquisition systems supported in microprocessor it is possible to obtain real data of the industrial process, that it characterizes in detail his dynamics and input-output dependency. Several methods of identification allow, from these data, to obtain linear and nonlinear models of these processes (Rossiter, 2003; Morari, 1994); which are the base to predict the process behaviour within all the family of the model based predictive controllers (MPC). Diverse algorithms MPC have demonstrated its effectiveness in those control loops characterized by strong nonlinearities, difficult dynamic, inverse answers and great delay; that they are generally those of greater influence in the final product quality and the process efficiency (Allgower et al., 2004; Qin & Badgwell 2003). One of the most important steps in the implementation of a MPC is just the obtaining of the model that can predict with reliability the future behaviour of the controlled variable, like answer to a predefined optimized control action (Rawlings 2000). This work applies two kind of MPC: (i) Classical Model-Based Predictive Control and (ii) Neural Network Model Predictive Control (NNMPC). The classical MPC strategy uses a discrete model obtained from general phenomenological model of the feed-batch crystallization process, consisting of mass, energy and population balance. The NNMPC strategy uses to obtain a neural network, the training algorithms proposed in the Neural Network Toolbox of MatLab (version 7.04) (Bemporad et al., 2005). In this particular case it is analyzed a fed-batch sugar crystallization process, in this process there is abundant information, detailed mathematical models and real industrial data. (Chorao, 1995; Feyo de Azevedo & Goncalves 1988; Georgieva et al., 2003). This fact motivated the use of the neural networks to model the process and to propose a neural network MPC (NNMPC) that considers the process like a gray box, of which has inputoutput information and the historical experience of he process behaviour." @default.
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• W1539308724 date "2011-06-24" @default.
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• W1539308724 title "Model Predictive Control Strategies for Batch Sugar Crystallization Process" @default.
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• W1539308724 doi "https://doi.org/10.5772/16853" @default.
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