(411b) “Hybrid Modeling” Versus “Winner Model” Approaches For Systems Identification

For any modeling problem an ensemble of models is initially developed,
which may comprise phenomenological, semi-empirical and/or "black box" models, each
category containing more than one candidates, developed for instance with
different model parameters. Then, there are two approaches for deciding about
the most proper model. On the one hand, the "winner model" approach assumes
that a single model from this ensemble can capture all the necessary
information about the modeling problem and therefore the other models are redundant.
According to this approach the "winner model" is typically selected based on
its generalization performance during a validation procedure. On the other
hand, the hybrid modeling approaches combine some of the developed candidate models,
assuming that they may contain different portions of the available information
and therefore their combination should perform better compared to a single
"winner model". To the best of our knowledge, the problem of detecting those members
of a given ensemble of models that have indeed captured different parts of the modeling
problem information has not been studied sufficiently in literature. Moreover,
another open issue is the functional form of the subsequent models combination.

This study presents a framework consisting of three steps for dealing
with the problem of models selection and combination. In the first step the
models characteristics are inferred, based on quantitative and qualitative
indicators for model accuracy and variables influence which are analyzed with
statistical techniques (PCA), in the second step the models are grouped, based
on the principal components of the first step and applying clustering
algorithms, and in a third step diverse forms of combinations of the model
groups are tested. Different types of neural networks are used as typical
examples of "black box" models, while the form of the phenomenological or
semi-empirical models is problem dependent. The proposed framework for hybrid
modeling techniques is compared with the "winner model" approach in a variety
of case studies regarding chemical engineering systems identification: the
prediction of the overall weight conversion in an industrial fluid catalytic
cracking unit, the correction of reaction mechanism and kinetic parameters
fitting, the experimental determination of the specific heat capacity of
solvents, and the prediction of the cumulative energy demand for the production
of chemical substances, an indicator widely used for the evaluation of the
life-cycle environmental impacts. The results demonstrate the advantages of the
proposed methodology both in terms of systems identification accuracy and interpretation.

Keywords

Model selection, hybrid modeling, "black box" modeling, neural
networks, principal components analysis, clustering, systems identification, process
identification, reaction kinetics, cumulative energy demand.