PhD thesis,
Evolutionary Polymorphic Neural Networks in Chemical Engineering Modeling
Department of Chemical Engineering, New Jersey Institute of Technology, (August 2001)
Abstract
Evolutionary Polymorphic Neural Network (EPNN) is a
novel approach to modeling chemical, biochemical and
physical processes. This approach has its basis in
modern artificial intelligence, especially neural
networks and evolutionary computing. EPNN can perform
networked symbolic regressions for input-output data,
while providing information about both the structure
and complexity of a process during its own
evolution.
In this work three different processes are modeled: 1.
A dynamic neutralisation process. 2. An aqueous
two-phase system. 3. Reduction of a biodegradation
model. In all three cases, EPNN shows better or at
least equal performances over published data than
traditional thermodynamics /transport or neural network
models. Furthermore, in those cases where traditional
modeling parameters are difficult to determine, EPNN
can be used as an auxiliary tool to produce equivalent
empirical formulae for the target process. Feedback
links in EPNN network can be formed through training
(evolution) to perform multiple steps ahead predictions
for dynamic nonlinear systems. Unlike existing
applications combining neural networks and genetic
algorithms, symbolic formulae can be extracted from
EPNN modeling results for further theoretical analysis
and process optimisation.
EPNN system can also be used for data prediction
tuning. In which case, only a minimum number of initial
system conditions need to be adjusted. Therefore, the
network structure of EPNN is more flexible and
adaptable than traditional neural networks.
Due to the polymorphic and evolutionary nature of the
EPNN system, the initially randomised values of
constants in EPNN networks will converge to the same or
similar forms of functions in separate runs until the
training process ends. The EPNN system is not sensitive
to differences in initial values of the EPNN
population. However, if there exists significant larger
noise in one or more data sets in the whole data
composition, the EPNN system will probably fail to
converge to a satisfactory level of prediction on these
data sets.
EPNN networks with a relatively small number of neurons
can achieve similar or better performance than both
traditional thermodynamic and neural network
models.
The developed EPNN approach provides alternative
methods for efficiently modeling complex, dynamic or
steady-state chemical processes. EPNN is capable of
producing symbolic empirical formulae for chemical
processes, regardless of whether or not traditional
thermodynamic models are available or can be applied.
The EPNN approach does overcome some of the limitations
of traditional thermodynamic /transport models and
traditional neural network models.
Tags
- (epnn),
- algorithms,
- artificial
- computing
- evolutionary
- genetic
- intelligence,
- network
- neural
- polymorphic
- programming,
