Evolutionary design of optimum distillation column sequence

Abstract

Synthesis of the optimum distillation column sequence (DCS), which incorporates a huge search space composed of both conventional and complex arrangements, is a highly complicated combinatorial problem in the field of chemical process design and optimisation. In this study, a novel procedure for the synthesis of optimum DCS proposed by Boozarjomehry et al. [Boozarjomehry et al., Can. J. Chem. Eng. 87, 477–492 (2009)] is expanded to include the complex distillation arrangements. The method is based on evolutionary algorithms, and the total annual cost (TAC) is the main criterion used to screen alternatives. Efficient procedure has been proposed for encoding mechanism to include and classify various complex arrangements together with conventional distillation columns. All columns existing in each DCS alternative are designed using the most recommended short‐cut methods to estimate the TAC of the DCS. Four standard benchmark case studies are carried out to clearly demonstrate the excellent performance of the proposed method. The produced results for these problems indicate that the proposed method outperforms the other existing approaches in terms of flexibility, accuracy and comprehensiveness. © 2011 Canadian Society for Chemical Engineering

Automatic Design and Optimization of Column Sequences and Column Stacking Using a Process Simulation Automation Server

“…The case study used in this example is taken from King . Boozarjomehry et al , proposed a genetic algorithm to obtain an optimal column sequence using short-cut column models. Andrecovich and Westerberg , have discussed the heat-integrated distillation using both constant Q Δ T and nonconstant Q Δ T approaches to calculate minimum utility use within utility bound.…”

“…If it is found that Aspen Plus has not responded after a certain amount of time (about 15 seconds), the Aspen Plus task is killed (using the Windows command taskkill), Aspen Plus is restarted, the simulation file is reloaded, and the The case study used in this example is taken from King. 39 Boozarjomehry et al 16,17 proposed a genetic algorithm to obtain an optimal column sequence using short-cut column models. Andrecovich and Westerberg 27,28 have discussed the heat-integrated distillation using both constant QΔT and nonconstant QΔT approaches to calculate minimum utility use within utility bound.…”

“…Optimization algorithms, including the simulated annealing algorithm (hydrodealkylation process, , process synthesis, − heat exchanger network, pressure swing distillation, and reactive distillation), genetic algorithm (dividing wall columns and process synthesis − ), and tabu search (hydrodealkylation process and synthesis of reaction/separation systems), have successfully been used in process design and optimization problems.…”

Constrained evolutionary optimization of a distillation train in chemical engineering

“…Bozorgmehry [39], presents an approach based on the prior modeling by short-cut methods of a permissible set of column architectures, and then applies a Genetic Algorithm to find the best combination of the allowed structures that separates an input mixture and minimizes the Total Annual Cost. Also, Bozorgmehry [40], introduces a novel procedure for the optimum synthesis of distillation column sequences. In the mentioned works of Bozorgmehry, the constraint handling is not an issue because the approach is based on short-cut models, therefore, calculating the equations is computational intensive and complex but the process stops on approximately desired specifications.…”