TY - JOUR TI - Integration of process scheduling and control DO - https://doi.org/doi:10.7282/T3862J92 PY - 2015 AB - The objective of this dissertation is to develop integrated models and optimization methods to solve for chemical process scheduling and control problems. A traditional approach to handle process operations at scheduling and control levels is to consider them as separate optimization problems. However, scheduling and dynamic optimization at control level are naturally connected. An integrated decision making helps to achieve an overall optimality and thus improves the profitability of process operations. Integration of scheduling and control results in Mixed Integer Dynamic Optimization (MIDO) which is computationally expensive. To reduce the complexity brought by integration, research efforts of this dissertation target two goals focusing on first reducing the model complexity, and second reducing the solution computational time especially in the case of online implementations (i.e. closed loop implementations). In this dissertation, we first proposed an approach of implementing closed loop scheduling and control when the processes are subject to disturbance. Then we proposed a decomposition approach for the large size Mixed Integer Nonlinear Programming (MINLP) resulted from the integration of scheduling and control through sensitivity analysis. To facilitate online applications, we adopt multi-parametric Model Predictive Control (mp-MPC) at the control level and built a new integrated model using the explicit control solution generated by mp-MPC. We also developed an integrated model using a Piecewise Affine (PWA) model and used fast MPC at the control level to overcome the exponential dimension increasing in mp-MPC. Finally we discuss the uncertainty in process operations and present solution procedures of robust MPC for nonlinear problem at the control level. Throughout this dissertation, detailed integrated models and the solution algorithms are developed and case studies are used to demonstrate the effectiveness of the proposed approaches. KW - Chemical and Biochemical Engineering KW - Production scheduling KW - Mathematical optimization LA - eng ER -