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 wave design into the nondominated sorting genetic algorithm with jumping genes. It allows simultaneous optimization of seven system, and five operating parameters to show 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 can be 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.