Soumitri, M S and Majumdar, Saptarshi and Mitra, Kishalay
(2015)
Optimization using ANN Surrogates with Optimal Topology and Sample Size.
IFAC papers online, 48 (8).
pp. 1168-1173.
ISSN 1474-6670
Abstract
Industrial scale process modelling and optimiza
tion of long chain branched polymer reaction
network is currently an area of extensive research owing to the advantages and growing popularity of
branched polymers. The highly complex nature of these reaction networks
requires
a large set of stiff
ordinary
differential equations
to model them mathematically with adequate precision and accuracy. In
such a scenario, where execution time of model is expensive, the idea of making the online optimization
and control of these processes seems to be a near impossib
le task. Catering to these problems in the
ongoing research, the authors presented a novel work where the kinetic model of long chain branched
poly vinyl acetate has been utilized to find the optimum processing con
ditions of operation using Sobol
sequence
based
ANN
as meta models in a fast and highly efficient manner. The article presents a novel
generic algorithm, which not only disables the heuristic approach of designing the
ANN
architecture but
also allows the computationally expensive first principle m
odel to determine the configuration of the
ANN
which can emulate it with maximum accuracy along with the size of training samples required. The
use of
such a fast and efficient Sobol
based ANN as surrogate model obtained by the proposed algorithm
m
akes the
optimization process 10
times
faster
as compared to a case where optimization is carried out
with
the expensive
first principle model.
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