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Modelling of induced aeration in turbine aerators by use of radial basis function neural networks
Author(s) -
Aldrich C.,
Van Deventer J. S. J.
Publication year - 1995
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.5450730604
Subject(s) - radial basis function , artificial neural network , aeration , impeller , turbine , basis (linear algebra) , sensitivity (control systems) , function (biology) , viscosity , mathematics , control theory (sociology) , computer science , engineering , mechanics , physics , artificial intelligence , biology , thermodynamics , mechanical engineering , evolutionary biology , waste management , control (management) , geometry , electronic engineering
Gas induction in agitated vessels with turbine impellers can be modelled accurately by means of radial basis function neural nets. The results obtained with the radial basis neural net were significantly better than those obtained by multivariate regression models or standard back propagation neural nets. Moreover, by using the radial basis function neural net model, it was possible to conduct a sensitivity analysis of the variables affecting aeration. Increased medium density showed a strong adverse effect, while variation of the viscosity can cause an increase or a decrease in the rate of aeration, depending on the prevailing process conditions.