An optimization framework that addresses the simultaneous process and control design of chemical systems including the selection of the control structure is presented. Different control structures composed of centralized and fully decentralized predictive controllers are considered in the analysis. The system's dynamic performance is quantified using a variability cost function that assigns a cost to the worst-case closed-loop variability, which is calculated using analytical bounds derived from tests used for robust control design. The selection of the controller structure is based on a communication cost term that penalizes pairings between the manipulated and the controlled variables based on the tuning parameters of the MPC controller and the process gains. Both NLP and MINLP formulations are proposed. The NLP formulation is shown to be faster and converges to a similar solution to that obtained with the MINLP formulation. The proposed methods were applied to a wastewater treatment industrial plant. (C) 2013 Elsevier Ltd. All rights reserved.