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Robust closed-loop dynamic real-time optimization
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AI
DOI：10.1016/j.jprocont.2023.04.003.png)
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Abstract
En 中文
Real-time optimization (RTO) is a valuable tool for economic optimization of chemical process systems. Incorporating plant dynamics and model predictive control (MPC) behavior into the RTO problem can improve its performance by accounting for plant transitions under the action of its control system, resulting in a closed-loop dynamic RTO (CL-DRTO) formulation. This paper extends the formulation for direct inclusion of uncertainty handling. A robust multi-scenario CL-DRTO scheme which models the dynamic behavior of the plant and its MPC system under uncertainty is introduced. The method is applied and its performance evaluated in two nonlinear case studies, where an input clipping approximation scheme is used to reduce the computation time. The effects of number of scenarios and multiple sources of uncertainty are also investigated.
Keywords:
Dynamic real -time optimization
MPC
Robust
Stochastic optimization
Uncertainty
Multi-scenario