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

Abstract

Real-time Optimization (RTO) is a production optimization technique that aims at improving plant economic performance in real-time. Many proposals of this technique have been made, being the two step RTO known as model parameter adaptation (MPA) the most traditional. Although being widely used in industrial application, the MPA uses stationary process information to perform the optimization and is not appropriated for dynamic systems. Recently a real time optimization with persistent adaptation (ROPA) has been proposed as a new RTO approach to handle and optimize dynamic systems. With a hybrid approach that uses stationary and transient information this method has shown promising results, but more studies are still necessary to support those results. The absence of a real system application, only implementation in simulated environments, and the lack of studies in different approaches regarding this method are some of the issues that still needs to be discussed. To contribute to the development of ROPA, this work presents an implementation of ROPA on an experimental rig that emulates a subsea oil extraction by Gas Lift. This experiment will evaluate ROPA performance in an real environment and compare the results with MPA. In order to apply ROPA two different online estimators are selected, the extended Kalman filter (EKF) and the moving horizon estimation (MHE). This work begins by modelling the experiment, since MPA and ROPA are model based, then MPA and ROPA-EKF are implemented and compared. Finally, ROPA-MHE is implemented and compared with ROPA-EKF. In order to implement ROPA-MHE a study on the arrival cost, a penalty term of MHE, was performed and it was verified the importance of this term on obtaining accurate estimations. The results obtained in this work have shown that ROPA-EKF had a better performance than MPA, specially in transient states, and ROPA-EKF and ROPA-MHE had similar performance.