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首页> 美国卫生研究院文献>Springer Open Choice >Optimizing Column Length and Particle Size in Preparative Batch Chromatography Using Enantiomeric Separations of Omeprazole and Etiracetam as Models: Feasibility of Taguchi Empirical Optimization
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Optimizing Column Length and Particle Size in Preparative Batch Chromatography Using Enantiomeric Separations of Omeprazole and Etiracetam as Models: Feasibility of Taguchi Empirical Optimization

机译：以奥美拉唑和依替西坦的对映体分离为模型在分批制备色谱中优化柱长和粒径：田口实证优化的可行性

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The overreaching purpose of this study is to evaluate new approaches for determining the optimal operational and column conditions in chromatography laboratories, i.e., how best to select a packing material of proper particle size and how to determine the proper length of the column bed after selecting particle size. As model compounds, we chose two chiral drugs for preparative separation: omeprazole and etiracetam. In each case, two maximum allowed pressure drops were assumed: 80 and 200 bar. The processes were numerically optimized (mechanistic modeling) with a general rate model using a global optimization method. The numerical predictions were experimentally verified at both analytical and pilot scales. The lower allowed pressure drop represents the use of standard equipment, while the higher allowed drop represents more modern equipment. For both compounds, maximum productivity was achieved using short columns packed with small-particle size packing materials. Increasing the allowed backpressure in the separation leads to an increased productivity and reduced solvent consumption. As advanced numerical calculations might not be available in the laboratory, we also investigated a statistically based approach, i.e., the Taguchi method (empirical modeling), for finding the optimal decision variables and compared it with advanced mechanistic modeling. The Taguchi method predicted that shorter columns packed with smaller particles would be preferred over longer columns packed with larger particles. We conclude that the simpler optimization tool, i.e., the Taguchi method, can be used to obtain “good enough” preparative separations, though for accurate processes, optimization, and to determine optimal operational conditions, classical numerical optimization is still necessary.Electronic supplementary materialThe online version of this article (10.1007/s10337-018-3519-z) contains supplementary material, which is available to authorized users.
机译：这项研究的主要目的是评估在色谱实验室中确定最佳操作条件和色谱柱条件的新方法，即，如何最好地选择具有适当粒径的填料，以及如何在选择颗粒后确定色谱柱床的适当长度。尺寸。作为模型化合物，我们选择了两种手性药物进行制备性分离：奥美拉唑和乙拉西坦。在每种情况下，假定两个最大允许压降：80和200 bar。使用全局优化方法，使用通用速率模型对过程进行数值优化（机械建模）。数值预测在分析和中试规模上均得到了实验验证。较低的允许压降表示使用标准设备，而较高的允许压降表示更先进的设备。对于这两种化合物，使用装有小粒径填料的短柱可实现最大的产率。增加分离中允许的背压可提高生产率并减少溶剂消耗。由于实验室可能无法使用高级数值计算方法，因此我们还研究了基于统计的方法，即田口方法（经验建模），以查找最佳决策变量并将其与高级机械模型进行比较。 Taguchi方法预测，填充较小颗粒的较短色谱柱比填充较大颗粒的较长色谱柱更可取。我们得出的结论是，更简单的优化工具（即Taguchi方法）可用于获得“足够好”的制备分离物，尽管对于精确的过程，优化和确定最佳操作条件，仍然需要经典的数值优化方法。本文的在线版本（10.1007 / s10337-018-3519-z）包含补充材料，可供授权用户使用。

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