Integration of Real Time Optimization with Model Predictive Control applied to a Gas-lift System: A comparative study.

• Autor(a) principal: Shamaki, Patience Bello
• Data de Publicação: 2020
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Biblioteca Digital de Teses e Dissertações da USP
• Texto Completo: https://www.teses.usp.br/teses/disponiveis/3/3137/tde-05032021-093254/
• topic: Controle por zonas; Controle preditivo; Estimação em tempo real.; Extended Kalman filter (EKF); Filtros de Kalman; Model predictive control (MPC); Online estimation; Real-time Optimization (RTO); Tempo real (Otimização); Zone control

There is a rising need for more practical, efficient and sustainable techniques for improving industrial system operation in the face of a highly competitive market. The integration of real time optimization (RTO) and model predictive control (MPC) is a classical approach applied in the industry for improving processes. In this work, we analyze the application of the two most common RTO and MPC integration strategies - one-layer and two-layer - to a gas-lifted system. We analyzed the performance of the economic cost function and efficiency in handling disturbance for each strategy, as well as consider practical industrial application. in the two-layer strategy, an upper economic optimization layer uses a rigorous nonlinear steady state model to compute the optimal process decision variables and send to the controller as an optimizing target which then computes the optimal control actions to achieve these targets. For this strategy, the hybrid RTO (HRTO) technique is implemented in the upper layer and an established controller - infinite horizon MPC with zone control- in the lower dynamic control layer. The hybrid RTO stems from the modification of the traditional static RTO found in the industry, to deal with the steady state wait time challenge. This is achieved by updating the optimizer with dynamic information rather than the static used in the traditional RTO. The zone control strategy allows the controller to focus on reaching a desired input target supplied by the optimization layer if the outputs are kept within their specified zones and constraints are respected. In the one-layer strategy, the gradient of the economic cost function is included in the controller cost function to be considered when computing the manipulated variable used to achieve optimal process operation. It was proposed with the main aim of practical industrial application. The two strategies were applied to a gas-lifted system and their results are compared and discussed considering economic objective. The results show that the IHMPC can reach the desired input targets despite abrupt disturbances of the uncertain parameter while keeping the outputs within the desired zone. Therefore, the HRTO can efficiently work with the IHMPC implemented in achieving optimal operation under uncertainties interfering as disturbance. It also shows that the one layer strategy gives similar results to the two-layer strategy, implying that it can also achieve similar economic objective. However, the two-layer strategy using HRTO technique is more efficient in handling the disturbances.