Dynamic Real-Time Optimization of Transitions in Industrial Polymerization Processes using Solution Models

J. V. Kadam, W. Marquardt Lehrstuhl für Prozesstechnik, RWTH Aachen University, Turmstr. 46, 52064 Aachen, Germany B. Srinivasan, D. Bonvin Laboratoire d’Automatique, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland Optimization of Transitions in Industrial Polymerization Processes using Solution Models Increasing demand on delivering on-spec product, higher operating costs and diminishing profit margins in the polymerization industry require cost- optimal process operation. This has led to the development of new tools and techniques for the operation of polymerization processes. Besides the added complexity due to market-driven operation, polymerization processes have intrinsic characteristics that cause specific problems in applying advanced optimization and control techniques. Planned or unplanned polymer load and grade changes are typical transitions routinely performed in a polymerization process. In this contribution, transition optimization problems (e.g. minimization of the transition time or the amount of off-spec polymer product) under quality control are considered. We address the challenges faced by any polymerization control strategy, i.e. process uncertainty, which requires on-line updates of any off-line computed solution. Furthermore, it is assumed that measurements of key process variables are available both on- line and at the end of the transition. Two major measurement-based approaches are classified as: 1. Process model approach: Here, measurements are used on-line to correct the current state of the process and re-estimate key model parameters. The inputs are updated subsequently by a repetitive on-line solution of an optimization problem that utilizes a dynamic process model. 2. Solution model approach: Here, measurements are used to directly update the inputs using a parameterized solution model that has been obtained from off-line optimization using a nominal dynamic process model (this is explained later). In the solution model approach considered in this paper (see [1] for more details), the necessary conditions of optimality (NCO) are enforced using a solution model and measurements. Thus, the approach is also labeled NCO tracking [1]. The solution model is obtained by appropriately parameterizing a robust optimal solution of the optimization problem with uncertainty [2]. The solution model is defined in terms of the sequence of input arcs corresponding to constraint-seeking arcs (active path constraints) and sensitivity-seeking arcs. The constrained variables are kept active using simple PID-type controllers. Sensitivity-seeking input arcs minimize the objective function sensitivity. Terminal constraints are handled by updating the corresponding switching times in a run-to-run fashion using the constraint measurements available at the end of the transition. Furthermore, terminal constraints can also be enforced within a single run by proposing a model for predicting the terminal constrained vari