Nonlinear Model Predictive Control and Dynamic Real Time Optimization for Large-scale Processes

This dissertation addresses some of the theoretical and practical issues in optimized operationsin the process industry. The current state-of-art is to decompose the optimizationinto the so-called two-layered structure, including real time optimization (RTO) and advancedcontrol. 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 modelsof processes has become an emerging technology. However, the integration of the firstprincipledynamic models is likely to introduce large scale optimization problems, whichneed to be solved online. The associated computational delay may be cumbersome for theonline applications.We first derive a first-principle dynamic model for an industrial air separation unit (ASU).The recently developed advanced step method is used to solve both set-point tracking andeconomically-oriented NMPC online. It shows that set-point tracking NMPC based on thefirst-principle model has superior performance against that with linear data-driven model.In addition, the economically-oriented NMPC generates around 6% cost reduction comparedto set-point tracking NMPC. Moreover the advanced step method reduces the onlinecomputational delay by two orders of magnitude.Then we deal with a realistic set-point tracking control scenario that requires achievingoffset-free behavior in the presence of plant-model mismatch. Moreover, a state estimatoris used to reconstruct the plant states from outputs. We propose two formulations usingNMPC and moving horizon estimation (MHE) and we show both approaches are offsetfreeat steady state. Moreover, the analysis can be extended to NMPC coupled with othernonlinear observers. This strategy is implemented on the ASU process.After that, we study the robust stability of output-feedback NMPC in the presence of plantmodelmismatch. The Extended Kalman Filter (EKF), which is a widely-used technologyin industry is chosen as the state estimator. First we analyze the stability of the estimationerror and a separation-principle-like result indicates that the stability result is the same asthe closed-loop case. We further study the impact of this estimation error on the robuststability of the NMPC.Finally, nominal stability is analyzed for the D-RTO, i.e. economically-oriented NMPC,for cyclic processes. Moreover, two economically-oriented NMPC formulations with guaranteednominal stability are proposed. They ensure the system converges to the optimalcyclic steady state.