Self-optimising reactor systems combined with computational strategies to understand and control inorganic particles.

目的：该项目的目的是使用自我优化的流动系统和计算策略相结合，以便更好地了解硫酸铁的晶体生长，并与间歇反应器相比提供更好的控制。小型连续流系统将被用于工艺条件筛选。机器学习将被用于利用高通量筛选数据来优化流程。机械模型将被用来获得预测工具。方法：七水合硫酸铁将使用X射线衍射仪、差示扫描量热仪、小角X射线衍射仪、拉曼光谱和G3形状尺寸分析仪等技术进行表征。这些数据将在未来的分析中作为参考。首先对纯硫酸铁七水体系进行了研究。七水合硫酸铁在50℃时溶于水中，冷却结晶。通过向体系中引入不同浓度的硫酸，结晶将重复进行。然后通过在FeSO4-H2SO4-H2O体系中引入镁、锰和铜来研究杂质对硫酸铁晶体形成的影响。连续流动反应器和数据驱动算法将用于硫酸铁的结晶和反应条件的优化。这将与在线表征技术相结合，如在线X射线衍射、DLS、SAXS、拉曼和UV-Vis。获得的数据将被用来使用人口平衡方程和CFD来创建机械模型。这些模型将被用作大型反应堆系统的预测工具，并用于创建相图。潜在影响：该项目将使用混合实验-建模方法来了解和控制硫酸铁结晶，这将使人们更好地了解不同工艺参数(如温度、pH或溶剂)对关键颗粒特性(如大小、形状、晶型、晶体结构和组成)的影响。更好的结晶控制也将允许形成具有所需性能的产品，有助于过滤和干燥过程。此外，自我优化的连续结晶器将允许实现所需产品的高产量。能够更好地理解晶体生长过程，将有利于精细化工行业。该项目将支持多个阶段的制造，从在特定反应条件下产生的颗粒属性的相图到基于模型的大规模工艺开发。预期成果：更好地了解七水合硫酸铁的晶体生长。一种新的机械模型，它将作为大规模系统的预测工具。一种新的连续流反应器设计。

Aims: The aim of this project is to use self-optimising flow systems combined with computational strategies to allow better understanding of crystal growth of iron sulphate and to provide better control in comparison to batch reactors. Small scale continuous flow systems will be used to process condition screening. Machine learning will be used to utilise high-throughput screening data to optimise processes. Mechanistic models will be used to obtain predictive tools. Methodology:Iron sulphate heptahydrate will be characterised using techniques such as XRD, DLS, SAXS, Raman and G3 shape and size analyser. This data will be used as a reference in future analysis. The pure iron sulphate heptahydrate system will be investigated first. Iron sulphate heptahydrate will be dissolved in water at 50c and crystallised by cooling. The crystallisation will be repeated by introducing various concentrations of sulphuric acid into the system. The effect of impurities on iron sulphate crystal formation will then be investigated by introducing magnesium, manganese and copper into the FeSO4-H2SO4-H2O system. Continuous flow reactors paired with data driven algorithms will be used for the crystallisation of iron sulphate and optimisation of the reaction conditions. This will be paired with online characterisation techniques such as online XRD, DLS, SAXS, Raman and UV-Vis. The data obtained will be used to create mechanistic models using population balance equations paired with CFD. These models will be used as a predictive tool for large scale reactor systems and for the creation of phase maps. Potential Impact:This project will use a hybrid experimental-modelling approach to understand and control iron sulphate crystallisation, which will provide a better understanding of the influence of different process parameters such as temperature, pH or solvents on crucial particle properties such as size, shape, polymorphs, crystal structure and composition. Better control in crystallisation would also allow products with desired properties to be formed aiding both filtering and drying processes. Additionally, self-optimising continuous crystallisers will allow high throughput of the desired product to be achieved. The ability to understand the crystal growth better would be beneficial for fine chemical industries. This project will support manufacturing at multiple stages, ranging from the creation of phase maps of the resulting particle properties at specified reaction conditions to model based large-scale process development. Expected Deliverables: A better understanding of crystal growth of iron sulphate heptahydrate. A new mechanistic model, which would act as a predictive tool for large-scale systems. A new continuous flow reactor design.