465830 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
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