Design and Optimization of Heat-Integrated Distillation Column Schemes through a New Robust Methodology Coupled with a Boltzmann-Based Estimation of Distribution Algorithm
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Abstract
The technology of the heat-integrated distillation column (HIDiC) has shown to be a potential energy-saving alternative for separating close-boiling mixtures. However, the economic aspects are a critical factor to extensive use of this distillation sequence in real applications. In this research, a novel stochastic optimization algorithm called Boltzmann univariate marginal distribution algorithm (BUMDA) with constraints handling has been implemented to optimize HIDiC sequences. Three binary mixtures were examined: butanol–isobutanol, n-heptane–cyclohexane, and benzene–toluene. The evaluation was performed by applying a new robust methodology using the interface Matlab–Excel–Aspen Plus. The model Radfrac was used in the simulations in Aspen Plus. The minimization of the total annual cost was established as the fitness function of the problem. Results showed the great robustness presented by the BUMDA algorithm for solving successfully this kind of complex optimization problem. Thus, the HIDiC design to separate the alcohol mixture showed energy savings of 84% and a cost that was lower (2%) than that of the conventional column. In addition, the hydrocarbon mixtures reached energy savings of 62.5% (n-heptane–cyclohexane) and 52.5% (benzene–toluene). Nevertheless, the total annual cost (TAC) of the HIDiC is larger than the TAC of the conventional column (32% and 35%, respectively).
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