This work presents a systematic framework for solvent design and selection based on optimum economic and controllability separation process performance. Solvents are initially designed in a solvent-process screening stage where conceptual process models are optimized to identify solvent options and process features of optimum economic performance. The integration of solvent and process design is facilitated computationally by a data mining approach which allows a reduced number of solvents to be evaluated in process design optimization. Highly performing solvents identified in the first stage are introduced into rigorous separation process design, supported, again by the proposed data mining approach. At this stage detailed process models enable identification of operating process characteristics which can be used as targeted set-points in a subsequent control design problem. Selected solvents and design configurations which are able to match the targeted set-points are then investigated for their performance in compensating the effects of multiple and large in magnitude process disturbances. A non-linear sensitivity analysis approach is employed that calculates the optimum steady-state effort for the solvent process design-control structure configuration. The focus of the work is maintained in the formal mathematical presentation and implementation of the data mining procedure and the controllability assessment of different solvents as well as in the use of rigorous process design models. In this respect alternative solvents and process designs are evaluated based on their economic and static controllability performance. (C) 2016 Elsevier Ltd. All rights reserved.