An approach to hybrid modelling in chromatographic separation processes

Chromatographic separation process models are described by nonlinear partial differential and algebraic equations, often leading to high computational cost that limits their applicability in real-time applications. To address this, in this work we propose a hybrid modelling approach that integrates artificial neural networks with process knowledge to describe the system nonlinear dynamics. Specifically, the separation isotherm is maintained in its mechanistic form, while the need for spatial discretisation is eliminated, reducing computational effort by 97 % in the open-loop simulation. The resulting hybrid model relies solely on experimentally measurable variables and performs well both in interpolation and extrapolation tests. It is further utilised within a process optimisation framework, for the maximisation of process yield and product purity. The results demonstrate that the hybrid model accurately captures the intricate dynamics of chromatographic separations while providing a computationally efficient alternative, making it an effective tool for development in industrial applications.