Crystallisation optimisation and control using surrogate modelling and adaptive model predictive control

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Crystallisation is inherently a challenging process to control as a result of a number of interacting parameters including nucleation, agglomeration, breakage, hydrodynamics, and so on. Due to molecular nature of the underlying mechanisms, robust modelling, control and optimisation is not straightforward. This can result in inconsistent batches, higher downstream processing costs, and difficulties controlling quality long-term. Population Balance Models (PBMs) and Model Predictive Control (MPC) exist for these systems but have limitations. PBMs can be computationally complex and as such real-time control is difficult. Due to the lack of detailed understanding of the full scope of interactions in crystallisation, PBMs will be of reduced complexity than the real system. This project proposes the use of surrogate models such as physics informed neural networks and reinforcement learning, utilising their nonlinear and adaptive predictive capabilities, to capture the full scope of behaviour contained within crystallisation system data. By combining PBMs and experimental data in the training of these surrogate models, it is proposed that a robust control and optimisation method can be developed that can be applied to a range of crystallisation systems. The models can enable real-time control and optimisation of a desired crystal size distribution, whilst balancing other constraints and objectives through multi-objective optimisation. The final goal is a control system that realises an established digital twin, with limited experimental data required.