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.

中文翻译：

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基于多参数建模的顺序模拟移动床多组分分离多目标优化及其确定


本研究基于顺序模拟移动床（SSMB）技术，研究困难的多组分分离和纯化过程的最佳解决方案。低聚木糖（XOS）由于其组成复杂、分辨率低，被选为主要体系，其中包含聚合度为2-6的XOS、木糖和阿拉伯糖。针对目标函数、决策变量和约束的不同组合的三种情况进行了多目标优化（MOO）。这项工作中研究的多组分系统并未简化为二元混合物，而是在 SSMB 建模过程中考虑了所有动力学和等温线参数。通过多组SSMB实验验证了参数确定的准确性，进一步证明了应用多参数模拟的必要性和优越性。此外，还进行了基于多参数模拟的MOO，结果表明，在有意义的约束下同时优化产品纯度、回收率、水耗和单位产量时，可以获得更好的帕累托最优曲线。比较和分析多参数和二元系统的流量比、操作条件和内部浓度分布的相应趋势，以确定合理的多组分分离策略。