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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. Monitoring specific information (i.e., “chemical” such as physico-chemical properties, chemical reactions, etc.) is the key to “chemical” process control.
The talk introduces a smart online NMR sensor module provided in an explosion proof housing as example. This sensor was developed for an intensified industrial process (pharmaceutical lithiation reaction step) funded by the EU’s Horizon 2020 research and innovation programme (www.consens-spire.eu). Due to NMR spectroscopy as an “absolute analytical comparison method”, independent of the matrix, it runs with extremely short set-up times in combination with “modular” spectral models. Such models can simply be built upon pure component NMR spectra within a few hours (i.e., assignment of the NMR signals to the components) instead of tedious calibrations runs.
The talk also generally covers current aspects of high-field and low-field online NMR spectroscopy for reaction monitoring and process control.
Quantitative Online NMR spectroscopy as a direct ratio method of analysis represents a perfect reference for calibration of further process analytical techniques, e.g., optical spectroscopic techniques. In this work we present a combined approach including Online NMR and Raman spectroscopy on a laboratory setup for development of suitable models for process monitoring. Therefore we investigated the absorption process of carbon dioxide in solutions of monoethanolamine, as well as the homogeneous catalyzed hydroformylation reaction of 1-dodecene taking place in an emulsion stabilized by a technical surfactant. The models for Raman spectroscopy were tested and approved during several days of operational studies of miniplant-scale setups for both mentioned applications.
Chemical companies must find new paths to successfully survive in a changing environment. The potential of digital technologies belongs to these. Flexible and modular chemical plants can produce various high-quality products using multi-purpose equipment with short downtimes between campaigns and reduce time to market for new products. Intensified continuous production plants allow for difficult to produce compounds.
Therefore, fully automated “chemical” process control along with real-time quality control are prerequisites to such concepts and thus should be based on “chemical” information. The advances of a fully automated NMR sensor were exploited, using a given pharmaceutical lithiation reaction as an example process within a modular pilot plant. A commercially available benchtop NMR spectrometer was integrated to the full requirements of an automated chemical production environment such as, e.g., explosion safety, field communication, and robust evaluation of sensor data. It was thereof used for direct loop advanced process control and real-time optimization of the process. NMR appeared as preeminent online analytical tool and allowed using a modular data analysis tool, which even served as reliable reference method for further PAT applications. In future, such fully integrated and intelligently interconnecting “smart” systems and processes can speed up the high-quality production of specialty chemicals and pharmaceuticals.
The combination of different silanes as starting materials and as a product of hydrolysis by several alcohols or water creates a range of hundreds of technical products for a wide range of applications. In recent years, functional trialkoxysilanes have proven to be multi-purpose organosilanes. Applications range from weather protection of buildings to additives for glass fiber industry, sealants, adhesives, coatings and paints to the modification of polymers.
Commercial benchtop NMR spectrometers have the potential to be used in silane chemistry as an online method for reaction monitoring and quality control. NMR nuclei of interest for silane products are 1H and 29Si. In a joint research cooperation between EVONIK and BAM, the applicability of low-field NMR spectroscopy for the chemical analysis of silanes was evaluated. It was shown how it can extend the application range where existing technologies like NIR, Raman, UV/VIS, etc. cannot be used quantitatively due to a lack of reference data.
In a first case study the process of hydrolysis and condensation was observed using online NMR analysis. For this purpose, the substituents of a trialkoxysilane are first hydrolyzed by adding water and corresponding silanols are formed, which can then bind to materials via SiOH functions and crosslink to form siloxane units. Another case study was dealing with the kinetics of the cleavage of a cyclic silane compound. Online NMR analysis was used both in the laboratory and in the manufacturing plant. For this purpose, a fully automated containment system was used, which enables the use of a commercial NMR spectrometer in ATEX-environments. In the third case study presented, quantitative 1H-NMR spectra were acquired on product mixtures of a trialkoxysilane and other components such as organic stabilizers, organotin compounds, an aromatic amine and organic peroxides. An automatic evaluation method based on Indirect Hard Modeling (IHM) was developed.
Chemical companies must find new paths to successfully survive in a changing environment. The potential of digital technologies belongs to these. Flexible and modular chemical plants can produce various high-quality products using multi-purpose equipment with short downtimes between campaigns and reduce time to market for new products. Intensified continuous production plants allow for difficult to produce compounds.
Therefore, fully automated “chemical” process control along with real-time quality control are prerequisites to such concepts and thus should be based on “chemical” information. The advances of a fully automated NMR sensor were exploited, using a given pharmaceutical lithiation reaction as an example process within a modular pilot plant. A commercially available benchtop NMR spectrometer was integrated to the full requirements of an automated chemical production environment such as, e.g., explosion safety, field communication, and robust evaluation of sensor data. It was thereof used for direct loop advanced process control and real-time optimization of the process. NMR appeared as preeminent online analytical tool and allowed using a modular data analysis tool, which even served as reliable reference method for further PAT applications. In future, such fully integrated and intelligently interconnecting “smart” systems and processes can speed up the high-quality production of specialty chemicals and pharmaceuticals.
Chemical companies must find new paths to stay productive in a rapidly changing environment. One of these is the potential of digital technologies. Flexible and modular chemical plants can produce various high-quality products using multi-purpose equipment with short downtimes between campaigns and reduce time to market. Process safety is improved due to smaller amounts processed and the abilities of efficient heat-transfer allow for otherwise difficult-to-produce compounds.
To exploit these advantages, a fully automated process control along with real-time quality control is mandatory and should be based on “chemical” information. The advances of a fully automated NMR analyzer were demonstrated, using a given pharmaceutical reaction step operated within a modular pilot plant. A commercially available benchtop NMR spectrometer was integrated to the requirements of an automated chemical production environment such as explosion safety, field communication, and robust data evaluation. Obtained results were used for direct loop advanced process control and real-time optimization of the process.
NMR spectroscopy appeared as excellent online analytical tool and allowed a modular data analysis approach, which even served as reliable reference method for further PAT applications. Using the available datasets, a second data analysis approach based on artificial neural networks (ANN) was evaluated.Therefore, amount of data was augmented to be sufficient for training. The results show comparable performance, while improving the calculation time tremendously. In future, such fully integrated and interconnecting “smart” systems and processes can increase the efficiency of the production of specialty chemicals and pharmaceuticals.
The use of benchtop-NMR instruments is constantly increasing during the recent years. Advantages of being affordable, portable and easy-to-operate without the need for trained staff make them especially interesting for industrial applications in quality control. However, applications of NMR spectroscopy as an online PAT tool are still very rare but offer a huge potential for process optimization and control. A key task to exploit this potential is hardware field integration of the lab-instruments in a rough environment of a chemical plant. Additionally, developments in automation and data evaluation are mandatory to ensure a robust unattended operation with low maintenance requirements. Here, we show an approach of a fully automated analyzer enclosure considering explosion safety, field communication, as well as environmental conditions in the field.
Temperature sensitivity is still a limitation of benchtop-NMR instruments in flow applications. Recent developments of manufacturers allow for limited operation at static temperature levels, however, a dynamic system for continuous operation is still not available. Using a prototype system offering a larger bore, active temperature shielding studies with thermostated air were performed evaluating the performance.
Automated data evaluation of NMR spectra using a modular indirect hard modeling (IHM) approach showed good results and flexibility. A second data analysis approach based on artificial neural networks (ANN) was evaluated.Therefore, amount of data was augmented to be sufficient for training. The results show comparable performance, while improving the calculation time tremendously, offering new ways to simultaneously evaluating large numbers of different models.