No abstract

Monitoring specific chemical properties is the key to chemical process control. Today, mainly optical online methods are applied, which require time- and cost-intensive calibration effort. NMR spectroscopy, with its advantage being a direct comparison method without need for calibration, has a high potential for enabling closed-loop process control while exhibiting short set-up times. Compact NMR instruments make NMR spectroscopy accessible in industrial and rough environments for process monitoring and advanced process control strategies. We present a fully automated data analysis approach which is completely based on physically motivated spectral models as first principles information (indirect hard modeling-IHM) and applied it to a given pharmaceutical lithiation reaction in the framework of the European Union's Horizon 2020 project CONSENS. Online low-field NMR (LF NMR) data was analyzed by IHM with low calibration effort, compared to a multivariate PLS-R (partial least squares regression) approach, and both validated using online high-field NMR (HF NMR) spectroscopy. Graphical abstract NMR sensor module for monitoring of the aromatic coupling of 1-fluoro-2-nitrobenzene (FNB) with aniline to 2-nitrodiphenylamine (NDPA) using lithium-bis(trimethylsilyl) amide (Li-HMDS) in continuous operation. Online 43.5 MHz low-field NMR (LF) was compared to 500 MHz high-field NMR spectroscopy (HF) as reference method.

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 Electronic supplementary material The online version of this article (10.1007/s00216-019-01752-y) contains supplementary material, which is available to authorized users.

The effect of several imidazolium-based ionic liquids on the mechanism of a classical ligand substitution reaction of [Pt(terpyridine)Cl] (+) with thiourea was investigated. A detailed kinetic study as a function of the nucleophile concentration and temperature was undertaken under pseudo-first-order conditions using stopped-flow techniques. Polarity measurements were performed for the employed ionic liquids on the basis of solvatochromic effects, and they show similarities with conventional polar solvents. Density-functional theory calculations (RB3LYP/LANL2DZp) were employed to predict the ion-pair stabilization energy between the ionic components of the ionic liquids and/or between the anions of the ionic liquids and the cationic Pt (II) complex. These data illustrate how the anions of the ionic liquids can affect the investigated substitution reaction. In general, the substitution mechanism in ionic liquids was found to have an associative character similar to that in conventional solvents. The observed deviations reflect the influence of the ionic liquid on the interaction between the anionic component of the liquid and the positively charged complex.

Industry 4.0 is all about interconnectivity, sensor-enhanced process control, and data-driven systems. Process analytical technology (PAT) such as online nuclear magnetic resonance (NMR) spectroscopy is gaining in importance, as it increasingly contributes to automation and digitalization in production. In many cases up to now, however, a classical evaluation of process data and their transformation into knowledge is not possible or not economical due to the insufficiently large datasets available. When developing an automated method applicable in process control, sometimes only the basic data of a limited number of batch tests from typical product and process development campaigns are available. However, these datasets are not large enough for training machine-supported procedures. In this work, to overcome this limitation, a new procedure was developed, which allows physically motivated multiplication of the available reference data in order to obtain a sufficiently large dataset for training machine learning algorithms. The underlying example chemical synthesis was measured and analyzed with both application-relevant low-field NMR and high-field NMR spectroscopy as reference method. Artificial neural networks (ANNs) have the potential to infer valuable process information already from relatively limited input data. However, in order to predict the concentration at complex conditions (many reactants and wide concentration ranges), larger ANNs and, therefore, a larger training dataset are required. We demonstrate that a moderately complex problem with four reactants can be addressed using ANNs in combination with the presented PAT method (low-field NMR) and with the proposed approach to generate meaningful training data. Keywords Online NMR spectroscopy . Real-time process monitoring . Artificial neural networks . Automation . Process industry Abbreviations A hf Measured high-field NMR areas (reference) A hf,test Measured high-field NMR areas (reference) of test dataset A hf,val Measured high-field NMR areas (reference) of validation dataset A lf,ANN Predicted areas of ANN model A lf,ANN,val Predicted areas of ANN model for validation dataset (S lf,val ) A lf,IHM Predicted areas of IHM A lf,synth Areas of pure components of synthetic mixture spectra k Index for component model K Total number of pure component models NN i/ii