The goal of this project is to develop a systematic methodology for design, optimisation and scale-up of continuous crystallisers without any moving parts. Fluidic oscillator designs will be developed for enhancing their performance as continuous anti-solvent crystallizers for industrially relevant systems. A base line fluidic oscillator will be configured based on prior studies. Appropriate methodology will be developed for in-line analysis of crystalliser performance. Systematic design of experiments will be developed and executed to generate new insights and useful data. Various attributes of produced crystals will be thoroughly characterised. Computational fluid dynamics models with population balance models will be developed to simulate complex solid – liquid flow during crystallisation in fluidic oscillator. Initially the models will be used for simulating shear, mixing, and residence time distribution. The models and the experimental data will be used to obtain key parameters of crystallisation kinetics. The computational models will be used to explore large parameter space. Influence of uncertainty associated with mixing, nucleation and growth kinetics (activation energy, rate coefficients), supersaturation etc. will be evaluated. Machine learning approaches will be developed and optimised. The obtained optimal neural network structures combined with the CFD+PBM models (hybrid models) will be used to obtain quantitative relationship between key design and operating parameters and crystalliser performance. Hybrid models will allow adequate capturing of complex non linear processes influencing performance. These will be used to guide development of optimal configuration as well as number scale-up options without compromising performance. The models and results will be used to develop key insights and specific recommendations for use of fluidic oscillator as continuous crystallisers. The project aims to computationally model the kinetics of cell cultures at varying process scales and will study the effect of different spargers and various flow rates on oxygen mass transfer, CO2 production and cell growth in production bioreactors at various scale.