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-This work studies the optimization and control of a styrene polymerization reactor. The proposed strategy deals with the case where, because of market conditions and equipment deterioration, the optimal operating point of the continuous reactor is modified significantly along the operation time and the control system has to search for this optimum point, besides keeping the reactor system stable at any possible point. The approach considered here consists of three layers: the Real Time Optimization (RTO), the Model Predictive Control (MPC) and a Target Calculation (TC) that coordinates the communication between the two other layers and guarantees the stability of the whole structure. The proposed algorithm is simulated with the phenomenological model of a styrene polymerization reactor, which has been widely used as a benchmark for process control. The complete optimization structure for the styrene process including disturbances rejection is developed. The simulation results show the robustness of the proposed strategy and the capability to deal with disturbances while the economic objective is optimized.

In this work, we study a simultaneous process design and control methodology using infinite horizon model predictive control (IHMPC). The methodology is based on the change in market conditions caused by the difference in the raw material and product prices. The economic and dynamic objectives are integrated into a single structure in the formulation of the optimization problem. The presented solution divides the problem into three stages and the final problem is solved using two strategies: goal attainment and quadratic cost. In the first two stages, utopian designs are calculated by the isolated solution of economic and dynamic problems. Finally, an integrated problem is solved to achieve the utopian values calculated in the previous stages, in order to produce simultaneously the plant with the best economic and dynamic performance. The results demonstrate the advantages of the methodology for process design, compared with the traditional sequential methodology.

This paper concerns the development of a stable model predictive controller (MPC) to be integrated with real time optimization (RTO) in the control structure of a process system with stable and integrating outputs. The real time process optimizer produces optimal targets for the system inputs and/or outputs that should be dynamically implemented by the MPC controller. This paper is based on a previous work (Comput. Chem. Eng. 2005, 29, 1089 where a nominally stable MPC was proposed for systems with the conventional control approach where only the outputs have set points. This work is also based on the work of Gonzalez et al. (J. Process Control 2009, 19, 110) where the zone control of stable systems is studied. The new controller is obtained by defining an extended control objective that includes input targets and zone control for the outputs. Additional decision variables are also defined to increase the set of feasible solutions to the control problem. The hard constraints resulting from the cancellation of the integrating modes at the end of the control horizon are softened, and the resulting control problem is made feasible to a large class of unknown disturbances and changes of the optimizing targets. The methods are illustrated with the simulated application of the proposed approaches to a distillation column of the oil refining industry.

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