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

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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.