Optimal design of Kaibel dividing wall columns based on improved particle swarm optimization methods

Abstract

Dividing wall columns (DWCs) are able to reduce operating costs and save capital costs for distillation columns, which verifies that DWC is a useful strategy in terms of distillation process intensification. DWCs are better choices than the corresponding conventional distillation sequences from both economical and environmental point of views. As DWC saves energy considerably and reduces CO2 emissions, it proves to be a breakthrough towards sustainable distilling. The four-product Kaibel DWC can further intensify the distillation process, and save energy cost about 40%. However, due to the complicated structures and strong interactions, the optimal design of the four-product Kaibel DWC is challenging to solve through conventional optimization methods. Therefore, this paper investigates the applicability of probabilistic global optimization algorithms, including standard particle swarm optimization (PSO), chaotic PSO, cellular PSO, and quantum-behaved PSO algorithms. This paper aims to explore the most appropriate improved PSO algorithm for the Kaibel DWC. The standard PSO and chaotic PSO show the premature convergence in the optimization process, while the cellular PSO and quantum-behaved PSO can prevent premature convergence. The chaotic PSO algorithm is more appropriate for the optimization of the continuous function, and it is less suitable for the optimization of the Kaibel DWC. The quantum-behaved PSO algorithm is relatively not appropriate for the optimization of the Kaibel DWC because of the long calculation time for rigorous simulation. The cellular PSO algorithm, based on the concept of cellular neighborhood, is the most appropriate algorithm for the optimization of the Kaibel DWC.