Weeden George S, Wang Nien-Hwa Linda
School of Chemical Engineering, Purdue University, West Lafayette, IN 47907, United States.
School of Chemical Engineering, Purdue University, West Lafayette, IN 47907, United States.
J Chromatogr A. 2015 Oct 30;1418:54-76. doi: 10.1016/j.chroma.2015.08.042. Epub 2015 Aug 29.
Size-exclusion simulated moving beds (SEC-SMB) have been used for large-scale separations of linear alkanes from branched alkanes. While SEC-SMBs are orders of magnitude more efficient than batch chromatography, they are not widely used. One key barrier is the complexity in design and optimization. A four-zone SEC-SMB for a binary separation has seven material properties and 14 design parameters (two yields, five operating parameters, and seven equipment parameters). Previous optimization studies using numerical methods do not guarantee global optima or explicitly express solvent consumption (D/F) or sorbent productivity (PR) as functions of the material properties and design parameters. The standing wave concept is used to develop analytical expressions for D/F and PR as functions of 14 dimensionless groups, which consist of 21 material and design parameters. The resulting speedy standing wave design (SSWD) solutions are simplified for two limiting cases: diffusion or dispersion controlled. An example of SEC-SMB for insulin purification is used to illustrate how D/F and PR change with the dimensionless groups. The results show that maximum PR for both diffusion and dispersion controlled systems is mainly determined by yields, equipment parameters, material properties, and two key dimensionless groups: (1) the ratio of step time to diffusion time and (2) the ratio of diffusion time to pressure-limited convection time. A sharp trade off of D/F and PR occurs when the yield is greater than 99%. The column configuration for maximum PR is analytically related to the diffusivity ratio and the selectivity. To achieve maximum sorbent productivity, one should match step time, diffusion time, and pressure-limited convection time for diffusion controlled systems. For dispersion controlled systems, the axial dispersion time should be about 10 times the step time and about 50 times the pressure-limited convection time. Its value can be estimated from given yields, material properties, and column configuration. Among the material properties, selectivity and particle size have the largest impact on D/F and PR. Particle size and 14 design parameters can be optimized for minimum D/F, maximum PR, or minimum cost on a laptop computer.
体积排阻模拟移动床（SEC-SMB）已被用于从支链烷烃中大规模分离线性烷烃。虽然SEC-SMB的效率比间歇色谱法高几个数量级，但它们并未得到广泛应用。一个关键障碍是设计和优化的复杂性。用于二元分离的四区SEC-SMB有七个物料特性和14个设计参数（两个产率、五个操作参数和七个设备参数）。以前使用数值方法的优化研究不能保证全局最优，也没有明确将溶剂消耗（D/F）或吸附剂生产率（PR）表示为物料特性和设计参数的函数。驻波概念被用于推导D/F和PR作为14个无量纲组函数的解析表达式，这14个无量纲组由21个物料和设计参数组成。所得的快速驻波设计（SSWD）解决方案针对两种极限情况进行了简化：扩散或分散控制。以用于胰岛素纯化的SEC-SMB为例，说明D/F和PR如何随无量纲组变化。结果表明，扩散和分散控制系统的最大PR主要由产率、设备参数、物料特性以及两个关键无量纲组决定：（1）步长时间与扩散时间之比，以及（2）扩散时间与压力限制对流时间之比。当产率大于99%时，D/F和PR会出现明显的权衡。最大PR的柱配置与扩散率比和选择性在解析上相关。为了实现最大吸附剂生产率，对于扩散控制系统，应使步长时间、扩散时间和压力限制对流时间相匹配。对于分散控制系统，轴向分散时间应约为步长时间的10倍和约为压力限制对流时间的50倍。其值可根据给定的产率、物料特性和柱配置进行估算。在物料特性中，选择性和粒径对D/F和PR的影响最大。粒径和14个设计参数可在笔记本电脑上进行优化，以实现最小D/F、最大PR或最低成本。
