Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
497366 | Applied Soft Computing | 2008 | 9 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science Applications
Authors
Ivan Nunes da Silva, Rogerio Andrade Flauzino,