Model Update Based on Transient Measurements for Model Predictive Control and Hybrid Real-Time Optimization

Abstract

The process model has the most relevant role in model predictive control (MPC) design since it is responsible for capturing system dynamics and behavior for control action calculation. Besides that, in real-time optimization (RTO), an accurate model allows the estimation of the optimum values that will lead the plant to optimal operation. Related to linear models, the linearization point sometimes is not capable of tracking the process trajectory in different regions, jeopardizing the entire representation. Regarding these issues, it is proposed in this paper to employ an augmented unscented Kalman filter to update the linear process model used in the MPC and the steady-state nonlinear model used in the hybrid RTO, at each sampling time, to capture the true process behavior and the updated economic cost. The cost function, solved in the RTO layer, is handled in the MPC layer as a process output, a new variable combined by the measurements, that must be driven to the provided optimum value, respecting constraints. The extended MPC approach is capable of handle zone tracking and set-point/target tracking. The Williams−Otto reactor scheme was employed to corroborate the proposed approach since it presents structural and parametric discrepancies between the model and the plant. The presented results showed that the approach was able to track the true value of the optimal cost operation, respecting the soft-constraints (or range) for the process outputs without exceeding manipulating efforts.

Hybrid Modeling of Hetero-Agglomeration Processes: A Framework for Model Selection and Arrangement

“…current applications are summarized in von Stosch et al (2014); Sansana et al (2021); Sharma and Liu (2022); Mowbray et al (2021); McBride et al (2020); Zendehboudi et al (2018); Thon et al (2021) and include general process modeling (Bayer et al, 2020;Krippl et al, 2020;Rato et al, 2020), process control (Wu et al, 2020;Ghosh et al, 2021;Santos et al, 2021), process optimization (Pedrozo et al, 2020;Beykal et al, 2018;Zhao and You, 2019), the development of soft sensors (Winkler et al, 2021;Guo and Liu, 2021;Jan Hagendorfer, 2021) and scale-up (Menesklou et al, 2021a,b;Simon et al, 2006). HMs have also been used in combination with PBE to model agglomeration processes : Georgieva et al (2003) employed a series connection of ANN and PBE to model batch crystallization.…”

Handling disturbances with a zone tracking-oriented stabilizing MPC: enlargement of the domain of attraction

“…Then, the challenge in the upper layer is to maintain the model updated and tackle uncertainties. One strategy that recently has been used to deal with this issue is to use transient measurements in RTO updates (Krishnamoorthy et al, 2018;Santos et al, 2021). Furthermore, the MPC layer must comply with the desired operating conditions while maintaining both stabilizing properties and the feasibility of the resulting optimization problem, even in the presence of disturbances.…”

Otimização em tempo real aplicada em um sistema experimental de Gas Lift.

“…Outras abordagens no contexto da ROPA também foram propostas. Valluru and Patwardhan (2019) e Santos et al (2021) propuseram o uso de estimadores dinâmicos para estimar o modelo da camada de controle preditivo (MPC) visando evitar diferença nos modelos durante a integração das camadas de RTO e MPC. Shamaki and Odloak (2020) combinaram a ROPA com um controlador preditivo de horizonte infinito ( infinite horizon zone control MPC).…”