Dynamic Real Time Optimization for Large Scale Processes
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The current state-of-art dynamic process optimization is to decompose the optimization into the so-called two-layered structure, including real time optimization (RTO) and advanced control. Due to model discrepancy and inconsistent time scales in different layers, this structure may render suboptimal solutions. Therefore, the dynamic real time optimization (D-RTO) or economically-oriented nonlinear model predictive control (NMPC) that directly optimizes the economic performance based on first-principle dynamic models of processes has become an emerging technology. This book addresses the compu...
The current state-of-art dynamic process optimization is to decompose the optimization into the so-called two-layered structure, including real time optimization (RTO) and advanced control. Due to model discrepancy and inconsistent time scales in different layers, this structure may render suboptimal solutions. Therefore, the dynamic real time optimization (D-RTO) or economically-oriented nonlinear model predictive control (NMPC) that directly optimizes the economic performance based on first-principle dynamic models of processes has become an emerging technology. This book addresses the computational issues associated with solving the large scale optimization problems. Moreover economic benefits of the proposed D-RTO are illustrated. Finally theoretical issues such as stability of the proposed method is analyzed.
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