Multi-objective optimization of sequential simulated moving bed for multi-component separation based on multi-parameter modeling and its determination

This study investigates optimum solution for difficult multi-component separation and purification process based on sequential simulated moving bed (SSMB) technology. Xylo-oligosaccharides (XOS), containing XOS with polymerization degree 2-6, xylose and arabinose, was chosen as the main system due to its complex composition and low degree resolution. Multi-objective optimization (MOO) was carried out for three cases with different combinations of objective functions, decision variables and constraints. Multi-component system studied in this work is not simplified as a binary mixture while all the kinetics and isotherm parameters are considered in the SSMB modeling process. Several groups of SSMB experiments were conducted to verify the accuracy of parameter determination and to prove further the necessity and superiority of application of multi-parameter simulation. Furthermore, the MOO based on multi-parameter simulation were done, which shows that better Pareto optimal curves could be obtained when product purity, recovery, water consumption and unit throughput are simultaneously optimized with meaningful constraints. The corresponding trends of flow rate ratios, operating conditions, and internal concentration profiles for both multi-parameter and binary systems were compared and analyzed to determine a reasonable strategy for multi-component separation.