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This work addresses the application of the Robust Infinite Horizon Model Predictive Control (RIHMPC) to a heat integrated propylene distillation system at a Petrobras refinery. The approach proposed here is tested on the rigorous dynamic simulation software (Dynsim®) that reproduces the system as a virtual plant and is able to communicate with the MPC algorithms developed in Matlab, through an Open Platform Communication (OPC) interface. The controller is based on a minimal order state‐space model that is equivalent to the system step response and considers the zone control of the outputs and optimizing targets for the inputs. The optimizing targets are obtained through the steady‐state economic optimization using the real‐time optimization package (ROMeo®1). The proposed integration approach provides convergence and stability to the closed‐loop system. The propylene distillation system is simulated with the proposed control and optimization strategies and the results show that, from the economic performance and robustness viewpoint, for this particular system, the proposed robust MPC is significantly better than the nominal IHMPC based on a single linear model obtained at the most probable operating point.

-In the process industry, advanced controllers usually aim at an economic objective, which usually requires closed-loop stability and constraints satisfaction. In this paper, the application of a MPC in the optimization structure of an industrial Propylene/Propane (PP) splitter is tested with a controller based on a state space model, which is suitable for heavily disturbed environments. The simulation platform is based on the integration of the commercial dynamic simulator Dynsim ® and the rigorous steady-state optimizer ROMeo ® with the real-time facilities of Matlab. The predictive controller is the Infinite Horizon Model Predictive Control (IHMPC), based on a state-space model that that does not require the use of a state observer because the non-minimum state is built with the past inputs and outputs. The controller considers the existence of zone control of the outputs and optimizing targets for the inputs. We verify that the controller is efficient to control the propylene distillation system in a disturbed scenario when compared with a conventional controller based on a state observer. The simulation results show a good performance in terms of stability of the controller and rejection of large disturbances in the composition of the feed of the propylene distillation column.

Here, the implementation of the gradient-based Economic MPC (Model Predictive Control) in an industrial distillation system is studied. The approach is an alternative to overcome the conflict between MPC and RTO (Real Time Optimization) layers in the conventional control structure. The study is based on the rigorous dynamic simulation software (SimSci Dynsim®) that reproduces the real system very closely and is able to communicate with Matlab where the control/optimization algorithm is implemented. The gradient of the economic function, which is required to the on-line execution of the extended control strategy, is obtained through the sensitivity tool of the real-time optimization package (SimSci ROMeo®). It is shown that the proposed integration approach leads to convergence and stability to the closed-loop system. In order to study the pros and cons of the new strategy, a propylene distillation system is simulated with both, the proposed approach (one-layer MPC+RTO) and the conventional two-layers hierarchical structure of control and optimization. The results show that, from the performance, stability and disturbance rejection viewpoint, the proposed gradientbased extended control method for this particular system is equivalent or better than the conventional approach.