Integrating self-optimizing control and real-time optimization using zone control MPC. (October 2015)

• Record Type: Journal Article
• Authors: Graciano, José Eduardo A.; Jäschke, Johannes; Le Roux, Galo A.C.; Biegler, Lorenz T.
• Is Part Of: Journal of process control. Volume 34(2015:Oct.)
• Page Start: 35
• Page End: 48
• Publication Date: 2015-10
• Subjects: Hierarchical control structure -- Real-time optimization -- Self-optimizing control -- Model predictive control; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2015.07.003
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: Self-optimizing control (SOC) is complementary to real-time optimization. Zone control approach is an alternative to address active set change problem in SOC. The proposed framework shows better performance than the classical approach. Abstract: The combination of real-time optimization (RTO) and model predictive control (MPC) methodologies is widely used in the chemical and petrochemical industry to optimize continuous processes. However, often the setpoint updates computed by the RTO are not frequent enough to capture all disturbances. This leads to suboptimal process operation, because the system is not re-optimized after a disturbance, until it has reached its new (suboptimal) steady state. An efficient way to handle this issue is to include economic considerations in the design of the lower MPC layer. This is the main idea of self-optimizing control (SOC), where controlled variables are selected, such that keeping them constant results in near-optimal operation without requiring an RTO update. Thus, we argue that SOC is complementary to RTO, and we develop a new MPC strategy with zone control and SOC variable targets. In particular, we present an approach toward solving the problem of low-frequency setpoint updates, also when the active set changes. We demonstrate the performance of our approach in steady state and dynamic simulations, and compare it to a classic RTO/MPC combination. The results show that our approach improves the coordination between the RTO and MPC layers. As it gives better performance in between RTO executions, it also leads to a higher overall profit.