Simulated Moving Bed Multiobjective Optimization Using Standing Wave Design and Genetic Algorithm

Multiobjective optimization of simulated moving bed systems for chiral separations is studied by incorporating standing suave design into the nondominated sorting genetic algorithm with jumping genes. It allows simultaneous optimization of wren system and five operating parameters to shore the trade-off between productivity, desorbent requirement (DR), and Yield. If pressure limit, product purity. and yield are fixed, higher productivity can be obtained at a cost of higher DR. If yield is not fixed, it cart he sacrificed to achieve higher productivity or vice versa. Short zones and high feed concentration favor high productivity, whereas long zones favor high yield and low DR. At fixed product purity and yield, increasing the pressure limit allows the use of smaller particles to increase productivity and to decrease DR. The performance of low-pressure simulated moving bed can be improved significantly by using shorter columns and smaller particles than those in conventional systems. (C) 2008 American Institute of Chemical Engineers AIChE J, 54: 2852-2871, 2008