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There have been an increasing number of publications on flow chemistry applications of compact NMR. Despite this, there is 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 analyses 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 CFD simulation. Subsequently, the flow cell was additively manufactured from ceramic material 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 production involving Benchtop NMR: Current application examples driven by digitalization
(2021)
The demand for increasing product diversity in the chemical and pharmaceutical industry calls for new production processes that enable greater flexibility. Therefore, plants are needed which can be adapted to new processes in a fast manner and be scaled up and down easily to volatile market demands. Modular production techniques in combination with advanced process analytical technology (PAT) are considered as a promising solution able to fulfil these requirements.
The success and acceptance of modular concepts in both new and existing plants is dependent of its reliability, easy applicability, and standardization. In recent past, enormous efforts were made to overcome existing barriers in a superordinate level, e.g. DEXPI [1], ENPRO [2], or MTP [3] naming just a few. Here, we’d like to present a few, more hands-on, application examples which are shown in Figure 1 aiming to increase process flexibility and applicability.
This includes:
a) The development of an additively manufactured mixer—flow reactor combination for the application inside of NMR instruments. [4]
b) The application of automated, model-based approaches for model development and spectra evaluation.
c) The application of machine-assisted spectral model building as a genuine alternative to classical model-based approaches [5]
d) Improvement of NIR calibration through online available NMR reference data. [6]
These examples represent miscellaneous use cases but result of the same fact: the increased use and availability of data through advanced PAT and therefore new opportunities utilizing them.
The future competitiveness of the process industry and their providers depends on its ability to deliver high quality and high value products at competitive prices in a sustain-able fashion, and to adapt quickly to changing customer needs. The transition of pro-cess industry due to the mounting digitalization of technical devices and their provided data used in chemical plants proceeds. Though, the detailed characteristics and con-sequences for the whole chemical and pharmaceutical industry are still unforeseeable, new potentials arise as well as questions regarding the implementation. As the digitali-zation gains pace fundamental subjects like the standardization of device interfaces or organization of automation systems must be answered. Still, process industry lack of sufficient system and development concepts with commercial advantage from this trend.
Compared to traditional batch processes, intensified continuous production allows new and difficult to produce compounds with better product uniformity and reduced con-sumption of raw materials and energy. Flexible (modular) chemical plants can produce various products using the same equipment with short down-times between cam-paigns, and quick introduction of new products to the market.
Full automation is a prerequisite to realize such benefits of intensified continuous plants. In continuous flow processes, continuous, automated measurements and closed-loop control of the product quality are required. Consequently, the demand for smart sensors, which can monitor key variables like component concentrations in real-time, is increasing. Low-Field NMR spectroscopy presents itself as such an upcoming smart sensor1,2 (as addressed, e.g., in the CONSENS project3).
Systems utilizing such an online NMR analyzer benefits through short development and set-up times when applied to modular production plants starting from a desired chemi-cal reaction3. As an example for such a modular process unit, we present the design and validation of an integrated NMR micro mixer based on computational modelling suited for a desired chemical reaction. This method includes a proper design of a con-tinuous reactor, which is optimized through computational fluid dynamics (CFD) for the demands of the NMR sensor as well as for the given reaction conditions. The system was validated with a chemical reaction process.
Modular production involving benchtop NMR Current Application Examples Driven by Digitalization
(2022)
The demand for increasing product diversity in the chemical and pharmaceutical industry calls for new production processes that enable greater flexibility. Therefore, plants are needed which can be adapted to new processes in a fast manner and be scaled up and down easily to volatile market demands. Modular production techniques in combination with advanced process analytical technology (PAT) are considered as a promising solution able to fulfil these requirements.
The success and acceptance of modular concepts in both new and existing plants is dependent of its reliability, easy applicability, and standardization. In recent past, enormous efforts were made to overcome existing barriers in a superordinate level, e.g. DEXPI, ENPRO, or MTP naming just a few. Here, we’d like to present a few, more hands-on, application examples which are shown in Figure 1 aiming to increase process flexibility and applicability.
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.
Calibration-Free Chemical Process and Quality Control Units as Enablers for Modular Production
(2021)
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 standardised physical modules and bringing them to life (i.e., parameterising 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.
The application of compact NMR instruments to hot flowing samples or exothermically reacting mixtures is limited by the temperature sensitivity of permanent magnets. Typically, such temperature effects directly influence the achievable magnetic field homogeneity and hence measurement quality. The internal-temperature control loop of the magnet and instruments is not designed for such temperature compensation. Passive insulation is restricted by the small dimensions within the magnet borehole. Here, we present a design approach for active heat shielding with the aim of variable temperature control of NMR samples for benchtop NMR instruments using a compressed airstream which is variable in flow and temperature. Based on the system identification and surface temperature measurements through thermography, a model predictive control was set up to minimise any disturbance effect on the permanent magnet from the probe or sample temperature. This methodology will facilitate the application of variable-temperature shielding and, therefore, extend the application of compact NMR instruments to flowing sample temperatures that differ from the magnet temperature.
The future competitiveness of the process industry and their providers depends on its ability to deliver high quality and high value products at competitive prices in a sustainable fashion, and to adapt quickly to changing customer needs. The transition of process industry due to the mounting digitalization of technical devices and their provided data used in chemical plants proceeds. Though, the detailed characteristics and consequences for the whole chemical and pharmaceutical industry are still unforeseeable, new potentials arise as well as questions regarding the implementation. As the digitalization gains pace fundamental subjects like the standardization of device interfaces or organization of automation systems must be answered. Still, process industry lack of sufficient system and development concepts with commercial advantage from this trend.
Intensified continuous processes are in focus of current research. Compared to traditional batch processes, intensified continuous production allows new and difficult to produce compounds with better product uniformity and reduced consumption of raw materials and energy. Flexible (modular) chemical plants can produce various products using the same equipment with short down-times between campaigns, and quick introduction of new products to the market.
Full automation is a prerequisite to realize such benefits of intensified continuous plants. In continuous flow processes, continuous, automated measurements and closed-loop control of the product quality are required. Consequently, the demand for smart sensors, which can monitor key variables like component concentrations in real-time, is increasing. Low-Field NMR spectroscopy presents itself as such an upcoming smart sensor (as addressed, e.g., in the CONSENS project – http://www.consens-spire.eu/).
Systems utilizing such an online NMR analyzer benefits through short development and set-up times when applied to modular production plants starting from a desired chemical reaction. As an example for such a modular process unit, we present the design and validation of an integrated NMR micro mixer based on computational modelling suited for a desired chemical reaction. This method includes a proper design of a continuous reactor, which is optimized through computational fluid dynamics (CFD) for the demands of the NMR sensor as well as for the given reaction conditions. The system was validated with a chemical reaction process.
Intensified continuous processes are in focus of current research. Compared to traditional batch processes, intensified continuous production allows the synthesis of new and difficult producible compounds with better product uniformity and reduced consumption of raw materials and energy. Flexible (modular) chemical plants can produce various products using the same equipment with short down-times between campaigns, and quick introduction of new products to the market.
Full automation is a prerequisite to realize such benefits of intensified continuous plants. In continuous flow processes, continuous, automated measurements and closed-loop control of the product quality are required. Consequently, the demand for smart sensors, which can monitor key variables like component concentrations in real-time, is increasing. Low-Field NMR spectroscopy presents itself as such an upcoming smart sensor (as addressed, e.g., in the CONSENS project1,2).
Systems utilizing such an online NMR analyzer benefits through short development and set-up times when applied to modular production plants starting from a desired chemical reaction. As an example for such a modular process unit, we present the design and validation of an integrated NMR micro mixer based on computational modelling suited for a desired chemical reaction. This method includes a proper design of a continuous reactor, which is optimized through computational fluid dynamics (CFD) for the demands of the NMR sensor as well as for the given reaction conditions. The system was validated with a chemical reaction process.
Data set of low-field NMR spectra of continuous synthesis of nitro-4’-methyldiphenylamine (MNDPA). 1H spectra (43 MHz) were recorded as single scans.
Two different approaches for the generation of artificial neural networks training data for the prediction of reactant concentrations were used: (i) Training data based on combinations of measured pure component spectra and (ii) Training data based on a spectral model.
Synthetic low-field NMR spectra
First 4 columns in MAT-files represent component areas of each reactant within the synthetic mixture spectrum.
Xi (“pure component spectra dataset”)
Xii (“spectral model dataset”)
Experimental low-field NMR spectra from MNDPA-Synthesis
This data set represents low-field NMR-spectra recorded during continuous synthesis of nitro-4’-methyldiphenylamine (MNDPA). Reference values from high-field NMR results are included.