Reinforcing the Phenomenological Consistency in Artificial Neural
Network Modeling
of
Multiphase Reactors
Chem.
Laurentiu A. Tarca, Bernard
P.A. Grandjean and Faїçal Larachi
Department
of Chemical Engineering & CERPIC
Laval
University - Québec, Canada G1K 7P4
Abstract
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.
Keywords Neural network, genetic algorithm, phenomenology consistency,
multiphase reactor
You can get the PD_CCPBed.zip
file that contains an Excel worksheet simulator to compute
the pressure drop in randomly packed beds with counter current
flow.
You may
also download our Excel worksheets simulators for Trickle-bed or Flooded
Bed reactors.
The neural
correlation was developped with the software NNFit