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Flexible automation with compact NMR spectroscopy for continuous production of pharmaceuticals
(2019)
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.
Modular plants using intensified continuous processes represent an appealing concept to produce pharmaceuticals. It can improve quality, safety, sustainability, and profitability compared to batch processes, and 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 includes a compact Nuclear Magnetic Resonance (NMR) spectrometer for online quality monitoring as well as a new model-based control approach. The NMR sensor is a benchtop device enhanced to the requirements of automated chemical production including ro-bust evaluation of sensor data.
Here, we present alternatives for the quantitative determination of the analytes using modular, physically motivated models. These models can be adapted to new substances solely by the use of their corresponding pure component spectra, which can either be derived from experimental spectra as well as from quantum mechanical models or NMR predictors. Modular means that spec-tral models can simply be exchanged together with alternate reagents and products. Beyond that, we comprehensively calibrated an NIR spectrometer based on online NMR process data for the first time within an industrial plant. The integrated solution was developed for a metal organic reac-tion running on a commercial-scale modular pilot plant and it was tested under industrial conditions.
The recent developments of inovative compact NMR spectrometers are therefore remarkable due to their possible application in process analytical technology (PAT) in an industrial environment without high maintenance requirements. spectroscopic methods, NMR spectroscopy offers some unique features for online reaction monitoring. However, those benefits have not been exploited for PAT applications so far. Within the CONSENS Project, the challenge to adapt a commercially available benchtop NMR spectrometer to the full requirements of an automated chemical production environment was tackled successfully. was provided in an explosion proof housing and involves a compact NMR spectrometer together with an automated data acquisition and evaluation unit, flow control, as well as data communication.
Monitoring chemical reactions is the key to chemical process control. Today, mainly
optical online methods are applied. NMR spectroscopy has a high potential for direct
loop process control. Compact NMR instruments based on permanent magnets
are robust and relatively inexpensive analysers, which feature advantages like low
cost, low maintenance, ease of use, and cryogen-free operation. Instruments for
online NMR measurements equipped with a flow-through cell, possessing a good
signal-to-noise-ratio, sufficient robustness, and meeting the requirements for
integration into industrial plants (i.e., explosion safety and fully automated data
analysis) are currently not available off the rack.
Intensified continuous processes are in focus of current research. Flexible (modular)
chemical plants can produce different products using the same equipment with short
down-times between campaigns and quick introduction of new products to the
market. In continuous flow processes online sensor data and tight closed-loop control
of the product quality are mandatory. If these are not available, there is a huge risk of
producing large amounts of out-of-spec (OOS) products. This is addressed in the
European Unionʼs Research Project CONSENS (Integrated Control and Sensing)
by development and integration of smart sensor modules for process monitoring and
control within such modular plant setups.
The presented NMR module is provided in an explosion proof housing of 57 x 57 x
85 cm module size and involves a compact 43.5 MHz NMR spectrometer together
with an acquisition unit and a programmable logic controller for automated data
preparation (phasing, baseline correction) and evaluation. Indirect Hard Modeling
(IHM) was selected for data analysis of the low-field NMR spectra. A set-up for
monitoring continuous reactions in a thermostated 1/8” tubular reactor using
automated syringe pumps was used to validate the IHM models by using high-field
NMR spectroscopy as analytical reference method.
The goal of this work is to identify the optimal operating input for a lithiation reaction that is performed in a highly innovative pilot scale continuous flow chemical plant in an industrial environment, taking into account the process and safety constraints. The main challenge is to identify the optimum operation in the absence of information about the reaction mechanism and the reaction kinetics. We employ an iterative real-time optimization scheme called modifier adaptation with quadratic approximation (MAWQA) to identify the plant optimum in the presence of plant-model mismatch and measurement noise. A novel NMR PAT-sensor is used to measure the concentration of the reactants and of the product at the reactor outlet. The experiment results demonstrate the capabilities of the iterative optimization using the MAWQA algorithm in driving a complex real plant to an economically optimal operating point in the presence of plant-model mismatch and of process and measurement uncertainties.