Modular plants using intensified continuous processes represent an appealing concept for the production of pharmaceuticals. It can improve quality, safety, sustainability, and profitability compared to batch processes; besides, it enables plug-and-produce reconfiguration for fast product changes. To facilitate this flexibility by real-time quality control, we developed a solution that can be adapted quickly to new processes and is based on a compact nuclear magnetic resonance (NMR) spectrometer. The NMR sensor is a benchtop device enhanced to the requirements of automated chemical production including robust evaluation of sensor data. Beyond monitoring the product quality, online NMR data was used in a new iterative optimization approach to maximize the plant profit and served as a reliable reference for the calibration of a near-infrared (NIR) spectrometer. The overall approach was demonstrated on a commercial-scale pilot plant using a metal-organic reaction with pharmaceutical relevance. Graphical abstract

In this paper, we discuss the optimization of the operation of a reductive amination (RA) reaction process in a miniplant without an accurate process model using iterative real-time optimization (RTO). The rhodium-catalyzed RA of undecanal with diethylamine produces a tertiary amine from a long-chain aldehyde and is performed in a thermomorphic multiphase system (TMS) to recover and reuse the expensive catalyst efficiently. An iterative RTO method called modifier adaptation with quadratic approximation (MAWQA) is used in combination with guaranteed model adequacy (GMA) to drive the RA process to its optimum iteratively. MAWQA utilizes online measurements to overcome model deficiencies. GMA ensures that the model used in MAWQA satisfies the model adequacy conditions. The optimal operating conditions of the RA process in the miniplant were identified during an experimental run of the plant, thereby validating the applicability and efficiency of MAWQA with GMA. The results illustrate the benefits of process optimization using iterative RTO methods without accurate process models.