Optimal design of structured packing column for cryogenic air separation with particle swarm optimisation algorithm

Large scale cryogenic air separation is currently the most efficient and cost-effective approach to produce high-purity air products. Structured packing columns (SPC) are widely focused and applied due to their characteristics of high efficiency and energy saving in the cryogenic distillation process. The main aims of the design of SPC are to reduce energy consumption and initial investment, while its optimal design is a highly nonlinear and multivariable problem. The coexistence of real variables and integer variables, such as the flow rates and the positions of materials at the inlets/outlets, makes the optimisation become a typical mixed integer nonlinear programming (MINLP) problem. The purpose of this paper is to study the optimal design for the cryogenic SPC using the particle swarm optimisation (PSO) algorithm. With the basis of original research, a modified PSO for handling the MINLP problem (MI-PSO) is proposed. A multi-objective optimal design for the SPC in cryogenic air separation unit with the capacity of 17000 Nm3/h, based on the FORTRAN compiler, is investigated. The multiple inlets/outlets of the model are validated by the limited available measurements, while the calculated distributions of the component molar fraction, temperature, pressure and flow rates along the column are compared with those from the Rate-based module of Aspen plus simulation. By MI-PSO algorithm, the total exergy loss reduces 36.3% and the main condenser heat load decreases 5.4% after optimisation.