Affordable Access

Publisher Website

Application of artificial neural networks for crossflow microfiltration modelling: “black-box” and semi-physical approaches

Authors
Journal
Computers & Chemical Engineering
0098-1354
Publisher
Elsevier
Publication Date
Volume
21
Issue
9
Identifiers
DOI: 10.1016/s0098-1354(96)00332-8
Keywords
  • Neural Network
  • Hybrid Model
  • Crossflow Microfiltration
Disciplines
  • Computer Science

Abstract

Abstract The neural network technique was applied to the study of the crossflow microfiltration process. Two application procedures are presented: (i) the “black-box” approach does not require an accurate description of the process, relying merely on the ability of neural networks to approximate the dynamics of any system; (ii) the semi-physical approach is an attempt to take into account a priori knowledge. Neural networks are then used simply to assess unknown parameters. Experiments were performed on suspensions of baker's yeast. In order to obtain the data set necessary required to train the different networks, two concentrations were tested in several operating conditions (filtration pressures and tangential flow velocities).

There are no comments yet on this publication. Be the first to share your thoughts.