Batch reactors are
large vessels in which chemical reactions take
place. They are mostly found to be used in process control industries
for processes such as reactant mixing, waste treatment of leather
byproducts, and liquid extraction. Modeling and controlling of these
systems are complex due to their highly nonlinear nature. The Wiener
neural network (WNN) is employed in this work to predict and track
the temperature profile of a batch reactor successfully. WNN is different
from artificial neural networks in various aspects, mainly its structure.
The brief methodology that was deployed to complete this work consisted
of two parts. The first part is modeling the WNN-based batch reactor
using the provided input–output data set. The input is feed
given to the reactor, and the reactor temperature needs to be maintained
in line with the optimal profile. The objective in this part is to
train the neural network to efficiently track the nonlinear temperature
profile that is provided from the data set. The second part is designing
a generalized predictive controller (GPC) using the data obtained
from modeling the reactor to successfully track any arbitrary temperature
profile. Therefore, this work presents the experimental modeling of
a batch reactor and validation of a WNN-based GPC for temperature
profile tracking.