Real-time optimization with persistent parameter adaptation applied to experimental rig. Issue 3 (2021)

• Record Type: Journal Article
• Authors: Matias, José; Castro Oliveira, Julio Paez de; Le Roux, Galo A.C.; Jäschke, Johannes
• Is Part Of: IFAC-PapersOnLine. Volume 54:Issue 3(2021)
• Page Start: 475
• Page End: 480
• Publication Date: 2021
• Subjects: Real-time operating systems -- Optimization; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2021.08.287
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Real-time optimization (RTO) is a steady-state model-based method used for optimizing process operation in chemical plants. The most common implementation, two-step RTO (TS-RTO), updates the steady-state model parameters in the first step, and optimizes this model in the second step. It has a major drawback, which is the need to wait for steady-state. If data from transient periods is directly used for updating the steady-state model parameters, the production optimization results will most likely be sub-optimal, decreasing the benefits. This becomes even more acute if the system is constantly affected by disturbances and has long settling times. Matias and Le Roux [2018] proposed a TS-RTO variant that uses a dynamic estimator to update the steady-state model parameters, which was named real-time optimization with persistent parameter adaptation (ROPA). By using dynamic estimation, it ensures that the model is always updated to the plant and the steady-state optimization can be scheduled at any desired rate without needing to wait for steady-state. This hybrid approach has been successfully tested in simulations. In this paper, we show its first implementation in a lab-scale rig, which emulates a subsea oil well network. The results show that the hybrid approach enables an increase in the optimization frequency and a decrease in the optimization results variability, improving the overall economic performance when compared to the TS-RTO implementation.