Abstract
Abstract The task of selecting a solvent or solvent mixture with desirable combination of physical properties to meet the needs of specific applications, has largely been tackled using a combination of heuristics and costly experimental studies. This material selection problem is here formulated as the combinatorial molecular design problem of choosing a set of structural groups making up a target molecule with the desired properties as predicted by available group contribution techniques. A novel mixed-integer nonlinear programming (MINLP) technique is used to solve the problem yielding compounds with optimum value of an appropriate performance index, subject to material balances, process and design limitations and feasibility of molecular structures. The strategy is applied with excellent results to solvent design examples for liquid-liquid extraction and multicomponent gas absorption using varying combinations of objective functions and constraints to reflect directly a multiplicity of operational objectives.
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