Steady-state real-time optimization using transient measurements in the absence of a dynamic mechanistic model: A framework of HRTO integrated with Adaptive Self-Optimizing IHMPC. (October 2021) - Delou, Pedro de Azevedo; Curvelo, Rodrigo; de Souza, Maurício B.; Secchi, Argimiro Resende
Abstract: In processes with slow dynamics or subjected to frequent disturbances, the detection of a steady-state operation might be rare, which hinders the application of the classic RTO. To overcome this issue, the Hybrid RTO (HRTO) is a proposition where a dynamic observer substitutes the Steady-State Detection (SSD) and the static parameter estimation steps. Recent progress has been made in HRTO frameworks. Still, all of them start from the assumption that a reliable mechanistic dynamic model is available, which is not true in many applications. In fact, the development of a dynamic process model is not considered in most typical RTO design projects. Therefore, the present work proposes an HRTO framework aiming, mainly, to overcome this first assumption. We present a complete control framework where the basic assumption is that a reliable mechanistic dynamic model is not available, but a static process model is at hand. The HRTO is made possible by using an approximate dynamic model that takes advantage of the static process model and identified linear dynamics from the plant in a Hammerstein structure. Three models are proposed based on the Hammerstein structure. These models are used in the dynamic observer, a proposed variation of an Extended Kalman Filter (EKF), and as the controller's internal model. The economic objectives are introduced into the control layer by the self-optimizing variables in an adaptive infinite-horizon MPC formulation. The framework presents full model compatibility between the observer, controller, and optimizer layers. A validation case study is developed in the Williams–Otto reactor with two proposed EKF tunings. The open-loop results provide evidence that the proposed Hammerstein structure is adequate as an approximate dynamic model, preserving the observability characteristics of the static model. The closed-loop results show that our framework outperforms the classic RTO structure in economic return, especially in the transient regions. Moreover, the proposed approach has a very natural way of handling active constraint changes. This effect is shown in the results at the moment that a constraint becomes active. The economic performance of the RTO is impaired compared to our proposed HRTO scheme. Finally, the average computational cost of each iteration of our framework is twice as fast as the classic RTO framework, which suggests the potential applicability of the proposed methodology on an industrial scale. Graphical abstract: Highlights: A hybrid RTO in the absence of a mechanistic dynamic process model is introduced. A Hammerstein structure is used in the dynamic observer and controller. Economic objectives are inserted into the controller by tracking SOC variables. A Hammerstein Adaptive IHMPC that explicitly deals with SOC variables is developed. Full compatibility between observer, controller and optimization models is achieved.