Robust integration of real time optimization with linear model predictive control

Alvarez, Luz Adriana; Odloak, Darci

doi:10.1016/j.compchemeng.2010.06.017

Cited by 40 publications

(23 citation statements)

References 15 publications

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“…The integrating process is different from the stable one, and the steady‐state predictive model is uncertain. Thus, some approaches are proposed to treat uncertainty in the framework of robust optimization . In the literature, a learning‐type MPC algorithm is employed to track the setpoint, which could be time‐varying within‐day.…”

Section: Introductionmentioning

confidence: 99%

Two‐layer model predictive control for chemical process model with integrating controlled variables

Yang

Ding

2019

Can J Chem Eng

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This paper proposes an overall solution to the two‐layer model predictive control (MPC) for the integrating controlled variables in the process model. The scheme includes three modules, that is, the open‐loop prediction module, the steady‐state target calculation (SSTC) module, and the dynamic control module. Based on the real‐time output measurements and past inputs, the open‐loop prediction module predicts the future outputs in the presence of disturbances. The economic optimization of SSTC is comprised of the feasibility stage and the economics stage, considering constraints of multi‐priority ranks. The dynamic control module receives the steady‐state targets from SSTC and calculates the control signals. The optimization problems of SSTC and dynamic control operate with the same frequency. This overall method guarantees the consistency of three modules with respect to the model, the constraints, and the targets. The simulation example illustrates that steady‐state targets are adjusted dynamically after the occurrence of disturbances, and offset‐free control is achieved.

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Section: Introductionmentioning

confidence: 99%

Two‐layer model predictive control for chemical process model with integrating controlled variables

Yang

Ding

2019

Can J Chem Eng

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“…In a similar strategy, polytopic uncertainty can also be considered where the true model is assumed to be the convex combination of a finite set of models that represent the vertices of a polytope . These ideas can also be included in the infinite horizon MPC and result in various strategies for implementing robust controllers that can be integrated with the RTO layer …”

Section: Introductionmentioning

confidence: 99%

Study of the implementation of a robust MPC in a propylene/propane splitter using rigorous dynamic simulation

Hinojosa

Odloak

2014

Can J Chem Eng

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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.

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“…In this problem, the infinite horizon cost This control structure guarantees robust stability (Alvarez and Odloak, 2010). If the RTO targets are reachable, the process variables converge to the desired targets while the cost functions corresponding to the TC and MPC stages converge to zero for each model i (i=1,…L) and consequently for the true model.…”

Section: Robust Algorithm For the Tc And Mpc Stagesmentioning

confidence: 99%

Optimization and control of a continuous polymerization reactor

Alvarez

Odloak

2012

Braz. J. Chem. Eng.

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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.

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Robust integration of real time optimization with linear model predictive control

Cited by 40 publications

References 15 publications

Two‐layer model predictive control for chemical process model with integrating controlled variables

Two‐layer model predictive control for chemical process model with integrating controlled variables

Study of the implementation of a robust MPC in a propylene/propane splitter using rigorous dynamic simulation

Optimization and control of a continuous polymerization reactor

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