(706h) A Model-Based Framework for Advanced Optimal Operation of Polymerization Processes

A
model-based framework for advanced optimal operation of polymerization
processes

N. Ghadipasha, A. Geraili, J.A. Romagnoli

Department of Chemical Engineering

Louisiana State University

Baton Rouge, LA, 70809

Abstract:

As
demand for new and more sophisticated polymers increases, the issue of
controlling polymerization reactors becomes more important. The principal
problems in achieving good control of polymerization processes are inadequate
on-line measurement, lack of understanding of the dynamics of the process,
nonlinearities arising during the reaction due to phenomena such as gelation
and high exothermicity and lack of well-developed techniques for control of
nonlinear processes. Hence, although there has been extensive work on
controlling polymerization reactors, they have been limited to special case
studies. Furthermore, due to lack of on-line measurement, most of the control
works have been based on open loop methods.

This contribution discusses
the formulation and implementation of a generic and flexible model-based
framework for integrated simulation, estimation, optimization and feedback
control of polymerization systems. The emphasis is on developing a
comprehensive scheme which can be applied for the optimal operation of various
polymeric systems. This goal was achieved by combining the powerful
capabilities of the automatic continuous online monitoring of polymerization
system, ACOMP, with a modern simulation, estimation and optimization software
environment. ACOMP is a widely applicable platform for monitoring
polymerization reactions. It combines simultaneous
data from multiple detectors so continuous monitoring of salient reaction
characteristics can be performed, such as kinetics, conversion of comonomers,
evolution of molecular mass, intrinsic viscosity and detection of unusual
phenomena, such as microgelation and runaway reactions. The
proposed structure in this work will forge initial links between ACOMP and
advanced modelling and control principles and demonstrate unprecedented
feedback control of polymerization reactions.

The conceptual
representation of the aforementioned framework is illustrated in Figure 1. The
modelling work is carried out using gPROMS modelling language, providing a
complete environment for modelling/analysis of complex systems. The parameter
estimation entity makes use of the data gathered from the experimental runs. It
has the ability to estimate various number of parameters, using data from
multiple dynamic experiments and ability to specify different variance models
among the variables as well as among different experiments. The optimization
entity allows for the typical dynamic optimization problems arising from batch
and/or semibatch operation to be formulated and implemented. Different off-line
optimal strategies are developed and fully tested using experimental
facilities. As an example, Figure 2 shows the validation results
in terms of the model predictions and the experimental data when the obtained
inputs trajectories are applied into the experimental systems. Finally,
the issue of feedback control of polymerization reactors is considered.
Development of a nonlinear input-output linearizing geometric approach to
control the reactor concentration is explained and the performance is validated
experimentally. Significant improvement compared with conventional controllers
is observed which is due to the exploitation of nonlinear structure of the
model in solving the control design problem.

Figure 1: Schematic representation of the integrated simulation,
estimation, optimization and feedback control of polymerization systems

Figure 2: Validation of optimal runs for the monomer concentration
and weight average molecular weight