This work presents an in silico tool that supports crystallization process development and optimization studies by means of mechanistic modeling, uncertainty identification, comprehensive sensitivity analysis and a quantified process risk assessment. Kinetic model parameters and operation design parameters are considered as a source of uncertainty and variation. Monte Carlo simulations are performed to propagate input uncertainty/variation to model output in terms of process yield, mean crystal diameter and size distribution. To quantify the individual effects and importance of these parameters, global sensitivity analysis e.g. Morris Screening and variance-based decomposition, is performed. The process risk is defined as failure to reach target product specifications and its consequences for the given design space is quantified. This promising study shows, that global uncertainty and sensitivity analysis coupled with the quantification of process risk assessment is a powerful tool and should be of interest to those participating in effective and efficient crystallization process development.

这项工作提出了一种通过计算机建模，不确定性识别，全面的灵敏度分析和量化的过程风险评估等手段来支持结晶过程开发和优化研究的计算机软件工具。动力学模型参数和操作设计参数被认为是不确定性和变化的来源。进行蒙特卡洛模拟，以将输入不确定性/变化传播到模型产量上，该结果取决于工艺良率，平均晶体直径和尺寸分布。为了量化这些参数的个体影响和重要性，可进行全局敏感性分析，例如进行莫里斯筛选和基于方差的分解。流程风险定义为无法达到目标产品规格，并且对给定设计空间的后果进行了量化。这项有前途的研究表明，全局不确定性和敏感性分析与过程风险评估的量化相结合是一种强大的工具，并且对于那些参与有效而高效的结晶过程开发的人们应该是有意义的。