General applicability and quantitative predictability of complex population-based crystallization models

基于复杂群体的结晶模型的普遍适用性和定量可预测性

• 批准号: 2903595
• 金额: --
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2903595
• 关键词: General; applicability; quantitative; predictability; complex

Modeling of crystallization processes offer several advantages. Accurate and predictive mathematical models have been and can be used for understanding the underlying physics, for process design, optimization, and control. Within the domain of crystallization, these models are composed of population balance equations (PBE) for the solid phase and the mass conservation constraint for the liquid phase. The former enables one to model the evolution of the particle size and shape distribution of ensembles of crystals. The latter enables one to model the evolution of the liquid phase concentration/supersaturation. Often, these models are developed in an academic setting at scales and conditions that are far from a realistic industrial scenario. Additionally, even though all the models developed in the literature account for the evolution of "size", majority of them do not account for the evolution of the shape of crystals (e.g., needles, plates, etc.). These factors can have potential implications on the general applicability and quantitative predictability of crystallization process models across different scales (lab to industrial scale). The overarching goal of the proposed collaborative project between Bayer and University of Manchester is two-fold. These areG1. To quantitatively evaluate the limits of the predictive capability of lab-scale models.G2. To propose a sound experimentally validated framework that facilitates developing predictive crystallization process models applicable over different scales. These goals will be addressed using four interconnected computational and experimental work packages (WPs). The reference case will be a non-proprietary binary system (one solute and one solvent) under pure growth conditions in lab-scale (500 mL to 1 L) at low suspension densities (0.1-1 w/w%). Subsequently, additional phenomena (dissolution, secondary nucleation), complex solvent systems, higher suspension densities (4-10 w/w%), and scales (100 mL to 20 L) will be considered. It is anticipated that this project will help Bayer and the broader crystallization community to judge whether pursuing significant efforts in developing process models for process design and operation is worthwhile in terms of its predictability at different scales and the time and resources invested.

结晶过程的建模提供了几个优点。精确和预测的数学模型已经并可以用于理解基础物理，用于过程设计，优化和控制。在结晶领域内，这些模型由固相的粒子数平衡方程（PBE）和液相的质量守恒约束组成。前者使人们能够模拟晶体系综的粒度和形状分布的演变。后者使人们能够模拟液相浓度/过饱和度的演变。通常，这些模型是在学术环境中开发的，其规模和条件与现实的工业场景相去甚远。此外，即使文献中开发的所有模型都考虑了“尺寸”的演变，但它们中的大多数都没有考虑晶体形状的演变（例如，针、板等）。这些因素可能对不同规模（实验室到工业规模）的结晶过程模型的普遍适用性和定量可预测性产生潜在影响。拜耳和曼彻斯特大学之间拟议的合作项目的总体目标是双重的。这是G1。定量评估实验室规模模型预测能力的限度。提出一个合理的实验验证框架，有利于开发适用于不同规模的预测结晶过程模型。这些目标将使用四个相互关联的计算和实验工作包（WP）来解决。参考案例将是实验室规模（500 mL至1 L）纯生长条件下低悬浮液密度（0.1-1 w/w%）的非专有二元系统（一种溶质和一种溶剂）。随后，将考虑其他现象（溶解、二次成核）、复杂溶剂系统、较高悬浮液密度（4-10 w/w%）和规模（100 mL至20 L）。预计该项目将帮助拜耳和更广泛的结晶界判断在不同规模的可预测性以及投入的时间和资源方面，在开发用于工艺设计和操作的工艺模型方面进行重大努力是否值得。