Traditionally process design and molecular design have been treated as two separate problems, with little or no feedback between the two approaches. Introduction of the property integration framework has allowed for representation of process and products from a properties perspective. Utilizing this methodology enables identification of the desired solvent properties by targeting the optimum process performance without committing to any components during the solution step. The identified property targets can then be used as inputs for solving a molecular design problem, which returns the corresponding components. We have previously combined property clustering techniques and first order group contribution methods (GCM) to facilitate simultaneous consideration of the property performance requirements as well as process and molecular constraints. To increase the accuracy of the implemented prediction methods and to enable isomer distinction, we are incorporating second order estimation to the property clustering methodology. For systems which can be satisfactorily described by three properties, the process and molecular design problems are solved simultaneously and visually by plotting the molecular fragments on a ternary cluster diagram. However for systems where more than three properties are required an algebraic clustering approach has been developed. The most significant aspect of the aforementioned method is that the application range and reliability of the property clustering technique will be considerably increased by incorporating second order estimation. This contribution will illustrate the developed methods and highlight their use through a case study.