A methodology for the simultaneous design and control of large-scale systems under process parameter uncertainty

This work presents a simultaneous design and control methodology for large-scale systems. The approach is based on the identification of an uncertain model from a first-principle process model. Using the identified uncertain model, a Structured Singular Value (SSV) analysis is used to estimate the realizations in the disturbance set that generates the worst-case variability and constraint violations. Then, simulations of the first-principle process model are performed with the critical disturbance profile as input to estimate the actual worst-case output variability and the worst-case variations in the process constraints. Since the proposed methodology is formulated as a nonlinear constrained optimization problem, it avoids the computationally expensive task of solving dynamic optimization problems, making it suitable for application to large-scale systems. The proposed methodology was tested on the Tennessee Eastman process to show that a redesign of the major process units in the process could significantly reduce the costs of this plant.