•The current methodologies available to perform the integration of process design and control involve the numerical solution of computationally intensive dynamic optimization problems which are time consuming, even for simple processes. In the present research, a new practical methodology is being developed to optimally design chemical processes under uncertainty and disturbances. The key idea is this approach is to represent the closed-loop nonlinear dynamic process model as a nominal state space model complemented with uncertainty in the model parameters. Then, robust control tools can be applied to estimate an infinite norm-based bound on the process internal stability and process output variability. Therefore, the methodology proposed in this research avoids the solution of computationally intensive nonlinear stochastic dynamic optimization problem since the integration of design and control problem has been reduced to a nonlinear constrained optimization problem.
