Improved Genetic Algorithms for Deterministic Optimization and Optimization under Uncertainty. Part II. Solvent Selection under Uncertainty

Abstract

The existence of a combinatorial search space in molecular design poses a challenge for traditional deterministic optimization methods. This paper presents an innovative approach based on improved genetic algorithms to optimally design solvents or extracting agents to separate the binary mixture of acetic acid and water in the presence of separation and physical property constraints. The UNIFAC−VLE model and Hansen's solubility parameter model are used to estimate the mixture properties. Since the mixture properties are predicted using group contribution methods, in which the group properties are derived from the regression of experimental data, uncertainties are inherent in the predictions. To account for these uncertainties, an “uncertainty factor” is introduced, which is defined as the ratio of experimental value to the value computed by the model. This uncertainty factor then is propagated through the model. A deterministic optimization framework is developed to compare the performance of two algorithms: (1) the improved genetic algorithm and (2) efficient stochastic annealing. Uncertainties are propagated through the stochastic framework. The results of the stochastic framework, with respect to the two algorithms, have also been analyzed and compared. As a stochastic optimization technique, the improved genetic algorithm outperforms its counterpart, the stochastic annealing technique. Using this method, new solvents with better targeted properties are found with less computational time.