Evaluation of stochastic global optimization methods in the design of complex distillation configurations

Distillation is a widely used separation process and is a very large consumer of energy. In process design, a significant amount of research work has been done to improve the energy efficiency of distillation systems in terms of either the design of optimal distillation schemes or for improving internal column efficiency. Still, the optimal design of multicomponent distillation systems remains one of the most challenging problems in process engineering (Kim & Wankat, 2004). The economic importance of distillation separations has been a driving force for the research in synthesis procedures for more than 30 years. For the separation of an N-component mixture into N pure products, as the number of components increases, the number of possible simple column configurations sharply increases. Therefore, the design and optimization of a distillation column involves the selection of the configuration and the operating conditions to minimize the total investment and operation cost (Yeomans & Grossmann, 2000). The global optimization of a complex distillation system is usually characterized as being of large problem size, since the significant number of strongly nonlinear equations results in serious difficulty in solving the model. Moreover, good initial values are needed for solving the NLP subproblems. Until now, several strategies have been proposed to address this optimization problem. For example, Andrecovich & Westerberg (1985) proposed a mixed-integer linear programming (MILP) model for synthesizing sharp separation sequences. Later, Paules & Floudas (1990) and Aggarwal & Floudas (1990) developed mixed-integer nonlinear programming (MINLP) models for heat-integrated and nonsharp distillation sequences using linear mass balances. In other study, Novak et al. (1996) proposed superstructure MINLP optimization approaches using short-cut models for heat-integrated distillation. Smith & Pantelides (1995) and Bauer & Stichlmair (1998) developed MINLP models using rigorous tray-by-tray models for zeotropic and azeotropic mixtures. Also, Dunnebier & Pantelides (1999) have used rigorous tray-by-tray MINLP models to solve complex column configuration

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