An approach based on neural networks for estimation and generalization of crossflow filtration processes

Authors:
Ivan Nunes da Silva;Rogerio Andrade Flauzino
Affiliations:
University of São Paulo, USP, Department of Electrical Engineering, SEL, CP 359, CEP 13566-590, São Carlos, SP, Brazil;University of São Paulo, USP, Department of Electrical Engineering, SEL, CP 359, CEP 13566-590, São Carlos, SP, Brazil
Venue:
Applied Soft Computing
Year:
2008

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Abstract

The crossflow filtration process differs of the conventional filtration by presenting the circulation flow tangentially to the filtration surface. The conventional mathematical models used to represent the process have some limitations in relation to the identification and generalization of the system behaviour. In this paper, a system based on artificial neural networks is developed to overcome the problems usually found in the conventional mathematical models. More specifically, the developed system uses an artificial neural network that simulates the behaviour of the crossflow filtration process in a robust way. Imprecisions and uncertainties associated with the measurements made on the system are automatically incorporated in the neural approach. Simulation results are presented to justify the validity of the proposed approach.