(165c) Reducing Energy Consumption by New Distillation Configuration

Separation processes make up 40%-70% of capital and operating costs of chemical manufacturing. Distillation accounts for more than 60% of the total process energy for the manufacture of commodity chemicals [DOE, 2005]. Optimal complex column configuration is estimated to harness energy savings up to 70% [Engelien and Skogestad, 2005]. Therefore, distillation trains are a meaningful target for energy improvements on an industry-wide scale. There is considerable literature on how to separate a feed mixture containing three or more components into pure product streams. Although past work on multicomponent distillation configurations has generated many extremely valuable information, it is still incomplete in providing the low-energy distillation configurations for a given application. There are two major challenges to hamper us providing the solutions. The first reason is that the search space for the distillation networks has been found to lack a few configurations and no systematic method to elucidate all distillation configurations from a network representation. The second reason is that finding the optimum distillation configuration has been the enormous difficulty of the mathematical task and the size of the problem in terms of computational effort.

In this presentation, a systematic procedure to draw distillation column configurations to separate an ideal to near ideal n-component mixture into n product streams each enriched in one of the components will be presented. The method synthesizes all feasible basic configurations using n-1distillation columns with each column having only a condenser at the top and a reboiler at the bottom. It also generates all feasible thermally coupled schemes with classical two-way liquid and vapor communication between the distillation columns. The method is simple and easy to use. After we obtain an array of the optimal configurations, the stochastic genetic method with novel temperature collocation method was incorporated to systematically find an optimum distillation scheme for a given application. Massive problem size reductions due to temperature collocation ensure the realistic composition profiles of each column in the network without sacrificing the computational and thermodynamic rigor. Feasibility of the synthesized sequences is ascertained by means of the MIDI algorithm as a subroutine to the master GA. In contract to existing approaches, this method synthesizes several clusters of designs solutions, each one corresponding to regions of local optimality.