Continuous Flow Synthesis of Mesalazine via data-driven Nonlinear Model Predictive Control

The synthesis of Mesalazine via data-driven modelling and a control scheme for the underlying complex continuous flow chemistry process is presented. The challenges of modelling continuous flow synthesis of Mesalazine are overcome
by the usage of Neuro-Fuzzy Models together with the so-called Local Linear Model Tree (NFM-LoLiMoT) training algorithm based on data from a highly detailed simulator for the reactor. A state-space representation of the NFM-LoLiMoT allows the implementation of a Non-linear Model Predictive Control
(NMPC) strategy in order to perform output tracking and fulfil all input and output constraints. The NMPC scheme guarantees stability through the approximation of an Infinite Horizon cost function using a terminal cost and terminal state constraints. The proposed method provides a systematic approach that can be applied for different setup configurations and reduces the time-consuming process of first-principles modelling of the chemical processes. Simulations of a hydrogenation reactor for the synthesis of Mesalazine are presented to show the performance of the introduced method.