280329 How to Deal with Fast Process Dynamics of the Ethylene Solution Polymerization Process During Optimal Grade Transitions?

Wednesday, October 31, 2012: 1:35 PM
324 (Convention Center )
Karen Pontes1, Marcelo Embiruçu1, Inga Wolf2 and Wolfgang Marquardt2, (1)Industrial Engineering Postgraduate Program (PEI), Federal University of Bahia (UFBA), Salvador, Brazil, (2)Process Systems Engineering, RWTH Aachen University, Aachen, Germany

How to Deal with Fast Process Dynamics of the Ethylene Solution Polymerization Process During Optimal Grade Transitions?

Karen V. Pontes1, Marcelo Embiruçu1, Inga J. Wolf2 and Wolfgang Marquardt2

1PEI – Industrial Engineering Postgraduate Program, Bahia Federal University

2AVT – Process Systems Engineering, RWTH Aachen

For highly non-linear processes with frequent product changes such as polymerization processes, operators' and engineers' experience may not suffice to operate the plant in an economically optimal way while maintaining product quality. In this context, real-time optimization strategies coupled with automatic process control offer opportunities for optimal process operation during the production of target polymer grades.

Recent advances in the solution of dynamic optimization (DO) problems have enabled their online solution, so that several approaches considering real-time optimization strategies for polymerization processes have been reported in the last years. The computational delay is usually lower than the grade transition duration, which may take minutes or even hours to complete. However, when approaching large-scale processes with fast dynamics, computational delay may hinder online optimization. This is the case for the ethylene polymerization in solution with catalyst in a series of stirred and tubular reactors, introduced by Braskem S.A. (Camaçari Petrochemical Pole - Brazil). A typical grade transition may take around 2-5 minutes due to high loads and small residence time. These fast dynamics are confirmed by a phenomenological mathematical model validated with industrial data. Bearing this in mind, this work seeks to investigate how real-time optimization strategies can be formulated to optimize the grade transition and production of this ethylene polymerization process.

Firstly, a two-layer dynamic real-time optimization (D-RTO) architecture, as formulated by Würth et al. (2011), is investigated which is then compared to a modification of the established two-layer real-time optimization (RTO) architecture (Marlin and Hrymak, 1997). The economic optimization at the upper-layer is trigged periodically to compensate the impact of slow disturbances (such as trends resulting from heat exchanger fouling or catalyst decay) on economically optimal operation and to compute trajectories for scheduled grade transitions. A linear time-varying MPC (Model-Predictive Controller) on the lower-layer tracks the reference trajectories at a higher sampling time to reject fast disturbances.

The D-RTO suggested here relies on a novel formulation of a two-stage DO problem for polymer grade transitions, considering solely economic goals in the objective function while the polymer properties are specified through constraints. Results show that a typical optimal grade transition problem, represented by a large-scale model made up of 140 differential and 2275 algebraic state variables can be efficiently solved in around 270 seconds. Furthermore, the two-layer architecture avoids the solution of the rigorous DO problem at every controller sampling time. Instead, it has to be solved less frequently. Other approaches, such as the one suggested by Zavala et al. (2006) combine the optimizer and the controller into a single-layer so that a DO problem has to be solved at every sampling time during the grade transition of low-density polyethylene tubular reactors. The DAE model used in the case study of Zavala et al. (2006), containing 294 differential and 64 algebraic state variables, is solved in around 351 seconds. Since grade transition in this case study is slow, a controller sampling time of 6 minutes can be used.

However, since the polymerization process investigated here presents very fast dynamics, a typical grade transition is completed already in around 2-5 minutes hindering the implementation of D-RTO in this industrial scenario. Attempting to ensure profitable process operation during the whole production cycle while targeting polymer properties, a modification of the established RTO approach is investigated. The steady-state optimization followed by the simulation of the optimal step response replaces the solution of the DO problem in the upper-layer of the D-RTO architecture when computing the reference trajectory.

Results show that the two-layer D-RTO allows higher profits and less off-spec production than the modified RTO, as expected. Important to mention, though, is that the RTO framework proposed here can already increase process profitability up to 30% compared to standard process operation, thus presenting itself as a promising alternative for implementation in industrial scenarios with less computational load. The closed-loop responses of both approaches are satisfactory since the underlying MPC can efficiently track the reference trajectories for the controlled and manipulated variables in the presence of fast disturbances.

Würth L, Hannemann R, Marquardt W. A two-layer architecture for economically optimal process control and operation. J. Proc. Cont. 21, 311-321 (2011);

Marlin TE, Hrymak AN. Real-time operations optimization of continuous processes. In: AIChE Symp. Ser. 93, 156-164 (1997);

Zavala VM, Laird CD, Biegler LT. Fast Implementations and Rigorous Models: Can both be accommodated in NMPC? In: Int. J. Robust Nonlinear Control 1–20 (2006).


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