Reinforcing the Phenomenological
Consistency in Artificial Neural Network
Modeling of Multiphase Reactors
Chemical
Engineering and Processing, 42,( 8-9), 2003, 653-662.
Laurentiu A. Tarca, Bernard P.A. Grandjean and Faїçal Larachi
Department of Chemical Engineering & CERPIC
Laval University - Québec, Canada G1K 7P4
Artificial neural networks (ANN)
aided with dimensional analysis have been successfully applied in multiphase
reactors modeling when considerable amount of experimental data (or database)
is available. An important problem that stemmed from this approach was the
ambiguity to select the fittest combination of dimensionless numbers to be used
as ANN inputs to predict a variable of interest. A genetic algorithm (GA) based
methodology was proposed to optimize the combination of inputs by taking into account
the phenomenological consistency (PC) of the resulting ANN models along with
their fitting capabilities. PC is a measure of the capability of an ANN model
to simulate outputs with specified gradient conditions with respect to the
process variables. These conditions are imposed based on a priori knowledge of the system’s behavior.
PC used to be evaluated in the vicinity of a particular point in the database
space. The novelty of the approach was the extension of the phenomenological
consistency test around all the points available in the training data set. This
technique may be regarded as a robust method to prevent data overfitting when the function to be learned by ANN is
characterized by a monotonic behavior with respect to
some of the process variables. The new approach was illustrated using as a case
study the correlation of two-phase pressure drop in randomly packed beds with countercurrent flow.
You can get the PD_CCPBed.xls file that contains an Excel
worksheet simulator to compute the pressure drop in randomly packed beds
with counter current flow.