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Abstract:
Model Predictive Control (MPC), a control algorithm which
uses an optimizer to solve for the optimal control moves over a
future time horizon based upon a model of the process, has become a
standard control technique in the process industries over the past
two decades. In most industrial applications, a linear dynamic
model developed using empirical data is used even though the
process itself is often nonlinear. Linear models have been used
because of the difficulty in developing a generic nonlinear model
from empirical data and the computational expense often involved in
using nonlinear models. In this paper, we present a generic neural
network based technique for developing nonlinear dynamic models
from empirical data and show that these models can be efficiently
used in a model predictive control framework. This nonlinear MPC
based approach has been successfully implemented in a number of
industrial applications in the refining, petrochemical, paper, and
food industries. Performance of the controller on a nonlinear
industrial process, a polyethylene reactor, is presented.
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