A novel approach is introduced for the fast, reliable, and low-cost recognition and quantification of plasticizers in plasticizers mixtures. It uses benchtop 1H NMR spectroscopy and indirect hard modeling, a mechanistic multivariate regression technique. The approach is demonstrated on five different PVC plasticizers having similar spectral signatures in proton NMR spectra. With only 16 scans per spectrum, i.e., 2 min 40 s measurement time, quantification limits down to 0.14 mg mL-1, or 0.35 wt% plasticizer in PVC, were achieved. Apart from the rapid data acquisition, the use of spectral hard modeling enabled the quantification of plasticizer mixtures while using only 4 to 6 training samples per component. Despite strongly overlapping signals in the NMR spectra, various plasticizers were differentiated and quantified, as exemplarily demonstrated for binary mixtures. A commercial PVC specimen with three different layers was also examined, confirming the applicability of benchtop NMR spectroscopy. Additionally, the use of the proposed method to validate official regulations concerning the plasticizer content in PVC is assessed. The presented results demonstrate that the combination of benchtop NMR and spectral hard modeling is a very promising analytical tool for rapid PVC plasticizer recognition and quantification with high analytical throughput. Moreover, the results indicate a high potential for benchtop NMR and spectral hard modeling for microchemical analysis, even for complex samples.

Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are critical components of every industrial chemical process as they provide information on the concentrations of individual compounds and by-products. These processes are carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been developed to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra to train an ANN with better prediction performance and speed than state-of-the-art analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a possible strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control.

A big problem with the chemistry literature is that it is not standardized with respect to precise operational parameters, and real time corrections are hard to make without expert knowledge. This lack of context means difficult reproducibility because many steps are ambiguous, and hence depend on tacit knowledge. Here we present the integration of online NMR into an automated chemical synthesis machine (CSM aka. "Chemputer" which is capable of small-molecule synthesis using a universal programming language) to allow automated analysis and adjustment of reactions on the fly. The system was validated and benchmarked by using Grignard reactions which were chosen due to their importance in synthesis. The system was monitored in real time using online-NMR, and spectra were measured continuously during the reactions. This shows that the synthesis being done in the Chemputer can be dynamically controlled in response to feedback optimizing the reaction conditions according to the user requirements.

The digitization of chemistry requires that synthesis procedures can be written and optimized in a chemical programming language to perform reliable "chemputation" for the synthesis on the "chemputer" robot, a universal synthesis machine. The cover image shows how a universal chemical synthesis engine equipped with an NMR sensor can be used to optimize reactions producing the automated synthesis procedure as an optimized process code ensuring that the chemputation can be improved and is reliable. Details are reported by Leroy Cronin, Franziska Emmerling et al. in their Communication on page 23202.

AbstractEin Problem der chemischen Literatur ist die fehlende Standardisierung bezüglich genauer Bedingungen, auch Echtzeit‐Korrekturen sind ohne Expertenwissen nur schwer möglich. Dieser Mangel an Details erschwert experimentelle Reproduzierbarkeit, da Schritte oft mehrdeutig sind und daher von implizitem Wissen abhängen. Hier präsentieren wir die Integration von Online‐NMR Spektroskopie in eine automatisierte chemische Syntheseplattform (CSM aka. “Chemputer”, unter Verwendung einer universellen Programmiersprache zur Synthese kleiner Moleküle fähig), um eine automatisierte Analyse und Anpassung von Reaktionen im laufenden Betrieb zu ermöglichen. Das System wurde anhand von Grignard‐Reaktionen, die aufgrund ihrer Bedeutung für die Synthese ausgewählt wurden, validiert und einem Härtetest unterzogen. Synthesen wurden in Echtzeit mit Online‐NMR überwacht, und die Spektren wurden während der Reaktionen kontinuierlich aufgenommen und analysiert. Dies zeigt, dass der Chemputer dynamisch mittels einer Regelung kontrolliert werden kann, um die Reaktionsbedingungen entsprechend den Anforderungen des Benutzers zu optimieren.

Modular chemical production is a tangible translation of the digital transformation of the process industry for specialty chemicals. In particular, it enables the speeding-up of process development and, thus, a quicker time to market by flexibly connecting and orchestrating standardized physical modules and bringing them to life (i.e., parameterizing them) with digitally accumulated process knowledge. We focus on the specific challenges of chemical process and quality control, which in its current form is not well suited for modular production and provide possible approaches and examples of the change towards direct analytical methods, analytical model transfer or machine-supported processes.

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.

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

There have been an increasing number of publications on flow chemistry applications of compact NMR Despite this, there has been so far no comprehensive workflow for the technical design of flow cells. Here, we present an approach that is suitable for the design of an NMR flow cell with an integrated static mixing unit. This design moves the mixing of reactants to the active NMR detection region within the NMR instrument, presenting a feature that analyzes chemical reactions faster (5-120 s region) than other common setups. During the design phase, the targeted mixing homogeneity of the components was evaluated for different types of mixing units based on computational fluid dynamics simulation. Subsequently, the flow cell was additively manufactured from ceramic materials and metal tubing. Within the targeted working mass flow range, excellent mixing properties as well as narrow line widths were confirmed in validation experiments, comparable to common glass tubes.

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.

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.

In case study one of the CONSENS project, two aromatic substances were coupled by a lithiation reaction, which is a prominent example in pharmaceutical industry. The two aromatic reactants (Aniline and o-FNB) were mixed with Lithium-base (LiHMDS) in a continuous modular plant to produce the desired product (Li-NDPA) and a salt (LiF). The salt precipitates which leads to the formation of particles. The feed streams were subject to variation to drive the plant to its optimum. The uploaded data comprises the results from four days during continuous plant operation time. Each day is denoted from day 1-4 and represents the dates 2017-09-26, 2017-09-28, 2017-10-10, 2017-10-17. In the following the contents of the files are explained. NIR_data_AQ15_raw.zip: Contains Bruker binary files (0-Files) of NIR spectrometer at AQ15 (Location is located subsequently to NMR spectrometer) NMR_spectra_raw.zip: Contains Spinsolve files (Binarys of FID and Spectrum, DX-Files) of NMR spectrometer. The use of DX-files files is not recommended. PCS_data_csv.zip: Contains csv-files of the process control system (PCS) including data of mass flow controlers (*_Bilanz.csv), filling level (*_FillLe.csv), pressures (*_pres), temperatures (*_Temp), position of valves (*_Valves). Relevant labels are: BP13 = LiHMDS storage tank, BP12 = aniline storage tank, BP12 = o-FNB storage tank, CM003 and CM004 = tubular reactors, T0041 and T005 = Temperature at reactor exits, P009 and P003 = Pressure at reactor inlets, P006 = Pressure at reactor exits. housing_data_NMR.csv: Contrains data of NMR enclosure of all four days. Each columns from left to right represent timestamps, bypass pressure (bar), bypass temperature (°C), Gasalarm (logical), bypass actual flowrate (g min-1), bypass flowrate setpoint (g min-1), bypass density (kg m3) matlab_variables_explanation.xlsx: Explanation of variables used in matlab structure "data_validation_run". data_validation_run.mat: Matlab structure containing most relevant process data including NMR results, NIR results, housing data of NMR, and process control system data.

Driven mostly by the search for chemical syntheses under biocompatible conditions, so called "click" chemistry rapidly became a growing field of research. The resulting simple one-pot reactions are so far only scarcely accompanied by an adequate optimization via comparably straightforward and robust analysis techniques possessing short set-up times. Here, we report on a fast and reliable calibration-free online NMR monitoring approach for technical mixtures. It combines a versatile fluidic system, continuous-flow measurement of H-1 spectra with a time interval of 20 s per spectrum, and a robust, fully automated algorithm to interpret the obtained data. As a proof-of-concept, the thiol-ene coupling between N-boc cysteine methyl ester and ally] alcohol was conducted in a variety of non-deuterated solvents while its time-resolved behaviour was characterized with step tracer experiments. Overlapping signals in online spectra during thiol-ene coupling could be deconvoluted with a spectral model using indirect hard modeling and were subsequently converted to either molar ratios (using a calibration free approach) or absolute concentrations (using 1-point calibration). For various solvents the kinetic constant k for pseudo-first order reaction was estimated to be 3.9 h(-1) at 25 degrees C. The obtained results were compared with direct integration of non-overlapping signals and showed good agreement with the implemented mass balance. (C) 2017 Elsevier Inc. All rights reserved.

The departure from the current automation landscape to next generation automation concepts for the process industry has already begun. Smart functions of sensors simplify their use and enable plug-and-play integration, even though they may appear to be more complex at first sight. Smart sensors enable concepts like self-diagnostics, self-calibration, and self-configuration/parameterization whenever our current automation landscape allows it. Here we summarize the currently discussed general requirements for process sensors 4.0 and introduce a smart online NMR sensor module as example, which was developed for an intensified industrial process funded by the EU’s Horizon 2020 research and innovation programme (www.consens-spire.eu).

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 inexpensive analysers, which feature advantages like 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, 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. Otherwise there is a huge risk of producing large amounts of out-of-spec (OOS) products. This is addressed in the European Union’s Research Project CONSENS (www.consens-spire.eu) 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 with a module size of 57 x 57 x 85 cm 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.

Online NMR spectroscopy is an excellent tool to study complex reacting multicomponent mixtures and gain process insight and understanding. For online studies under process conditions, flow NMR probes can be used in a wide range of temperature and pressure. This paper compiles the most important aspects towards quantitative process NMR spectroscopy in complex multicomponent mixtures and provides examples. After NMR spectroscopy is introduced as an online method and for technical samples without sample preparation in deuterated solvents, influences of the residence time distribution, pre-magnetization, and cell design are discussed. NMR acquisition and processing parameters as well as data preparation methods are presented and the most practical data analysis strategies are introduced.

In this study, an efficient and high-yield simultaneous epoxidation and transesterification of fatty acid methyl esters in a single-pot reaction is presented. Starting from commercially available renewable unsaturated fatty acid methyl esters (FAMEs) from plant oils, the epoxidation catalyzed by Novozym 435 was optimized for a temperature of 50 °C in two different reactor types on the multigram scale. Simultaneous transesterification of the methyl esters to iso-nonyl esters was also studied and optimized for same conditions resulting in an efficient process for synthesis of epoxy fatty acids esters from renewable feedstock.

Compact nuclear magnetic resonance (NMR) instruments make NMR spectroscopy and relaxometry accessible in industrial and harsh environments for reaction and process control. An increasing number of applications are reported. To build an interdisciplinary bridge between “process control” and “compact NMR”, we give a short overview on current developments in the field of process engineering such as modern process design, integrated processes, intensified processes along with requirements to process control, model based control, or soft sensing. Finally, robust field integration of NMR systems into processes environments, facing explosion protection or integration into process control systems, are briefly discussed.

Background Development of bioprocesses for mammalian cells has to deal with different bioreactor types and scales. Bio-reactors might be intended for seed train and production, research, process development, validation or transfer purposes. During these activities, not only the problem of upand downscaling might lead to failure of repro-ducibility, but also the use of different bioreactor geometries and operation conditions. In such cases, the criteria for bioreactor design and process transfer should be re-evaluated in order to avoid an erroneous transfer of cultivation parameters.