This paper presents a new methodology to integrate process design and control design for chemical processes. The proposed method estimates infinite-time horizon bounds for worst-case scenarios and enforces process feasibility constraints using Structured Singular Value analysis (mu). By using these bounds expensive dynamic optimizations are avoided thus permitting the formulation of a computationally efficient algorithm that is suitable for large-scale systems. The approach was applied to the simultaneous design and control of the Tennessee Eastman process. The effect of different parameters on the resulting process design and control design for this plant were analyzed. The results indicate that an increase of the reactor's capacity with respect to the currently used value may result in significant cost reduction. (C) 2009 Elsevier Ltd. All rights reserved.