Control of a complex multistep process for the production of mesalazine. (February 2023) - Castillo, Ismael; Rehrl, Jakob; Sagmeister, Peter; Lebl, René; Kruisz, Julia; Celikovic, Selma; Sipek, Martin; Kirschneck, Dirk; Horn, Martin; Sacher, Stephan; Cantillo, David; Williams, Jason D.; et al.
Abstract: This paper presents the application of Data Driven Modelling (DDM) and Non-Linear Model Predictive Control (NMPC) for the control implementation of a continuous reactor for the production of the Active Pharmaceutical Ingredient (API), Mesalazine. The contribution of this work is to present the overall control architecture, the step-by-step controller design based on DDM, i.e. Neuro-Fuzzy/Local Linear Model Tree Models (LoLiMoT) and NMPC. We demonstrate the advantages of DDM and NMPC, in the presence of non-linear, distributed parameter, multi-variable systems as a suitable, powerful and practical way to approach complex process control challenges. Compared to conventional concepts, the inherent optimization structure of NMPC allows to obtain the desired behaviour of the multi-variable non-linear plant considering the physical constraints of operation regions. The control concept has been implemented in the real system on one central computer, XamControl (Evon), 1 with all measurement devices and actuators centrally interconnected. Highlights: Integration of a multistep continuous flow process with Process Analytical Technology Multistep continuous flow synthesis of Active Pharmaceutical Ingredient, Mesalazine Control of a multistep continuous flow process by a single process unit XamControl Digital Twin development via Data Driven Neuro Fuzzy Model and Local Linear Model Tree Nonlinear Model Predictive Control including reactor physical-chemical constraints