A mixed-integer dynamic optimization (MIDO) framework for solvent design in batch processes is presented. Performance measures reflecting process economics and computed on the basis of process dynamics are used to validate candidate solvent structures built from the UNIFAC molecular groups. These define the discrete space in the overall material formulation and are combined according to molecular design feasibility rules to guarantee realistic molecular representations. The algorithm is based on the decomposition of the MIDO primal subproblem into several steps that are solved successively. This allows unsuitable solvents to be detected and discarded quickly and without significant computational cost. Emphasis is placed on problem formulation in order to match accuracy of process model and physical property predictions. The algorithm is applied successfully to an industrial case study dealing with a three-phase dehydration column and a decantation unit for solvent recovery. The proposed algorithm can be regarded as a promising initial step toward an integrated and simultaneous methodology for material process design in batch separation systems.