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Eingeladener Vortrag
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Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are valuable analytical and quality control methods for most industrial chemical processes as they provide information on the concentrations of individual compounds and by-products. These processes are traditionally 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 realized 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 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.
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.
Ergot alkaloids are mycotoxins formed by fungi of the Claviceps genus, which are some of the most common contaminants of food and feed worldwide. These toxins are a structurally heterogeneous group of compounds, sharing an ergoline backbone. Six structures and their corresponding stereoisomers are typically quantified by either HPLC-FLD or HPLC-MS/MS and the values subsequently summed up to determine the total ergot alkaloid content. For the development of a screening method targeting all ergot alkaloids simultaneously, the alkaloids need to be transferred to one homogeneous structure: a lysergic acid derivative. In this study, two promising cleaving methods—acidic esterification and hydrazinolysis—are compared, using dihydroergocristine as a model compound. While the acidic esterification proved to be unsuitable, due to long reaction times and oxidation sensitivity, hydrazinolysis reached a quantitative yield in 40-60 min. Parallel workup of several samples is possible. An increasing effect on the reaction rate by the addition of ammonium iodide was demonstrated. Application of hydrazinolysis to a major ergot alkaloid mix solution showed that all ergopeptines were cleaved, but ergometrine/-inine was barely affected. Still, hydrazinolysis is a suitable tool for the development of a sum parameter screening method for ergot alkaloids in food and feed.
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-theart 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.
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.
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-theart 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.
Industrielle Anwendungen der Niederfeld-NMR-Spektroskopie für die Qualitätskontrolle von Silanen
(2021)
Aus der Kombination verschiedener Silane als Ausgangsstoffe und als Produkt der Hydrolyse durch eine Reihe von Alkoholen oder Wasser ergibt sich eine Palette von hunderten technischen Produkten für verschiedenste Anwendungen. Funktionelle Trialkoxysilane haben sich in den vergangenen Jahren als vielseitig einsetzbare Organosilane erwiesen. Die Einsatzgebiete erstrecken sich vom Witterungsschutz von Bauwerken über haftvermittelnde Eigenschaften in der Glasfaserindustrie, bei Dicht- und Klebstoffen, in Farben und Lacken bis hin zur Modifizierung von polymeren Werkstoffen.
Kommerzielle Benchtop-NMR-Spektrometer haben das Potential auch im Bereich der Silanchemie als Online-Methode zur Reaktionsüberwachung und für die Qualitätskontrolle eingesetzt zu werden. Interessante NMR-Kerne für die oben genanntem Produkte sind 1H und 29Si. In einer gemeinsamen Forschungskooperation zwischen EVONIK und BAM wurde anhand verschiedener Fallstudien die Anwendbarkeit der Niederfeld-NMR-Spektroskopie zur chemischen Analyse von Silanen evaluiert. Die Möglichkeiten der Konzentrationsbestimmung erweitern die Anwendungsgebiete, in denen bestehende Technologien wie z. B. NIR, Raman, UV/VIS, etc. mangels Referenzdaten nicht quantitativ eingesetzt werden können.
Eine Fallstudie setzte dazu an, den Hydrolyse- und Kondensationsverlauf mit geeigneter Online-NMR-Analytik zu beobachten, den Reaktionsfortschritt der Hydrolyse und Kondensation auf dieser Basis besser zu verstehen und zu optimieren. Zu diesem Zweck werden durch Zugabe von Wasser zunächst die Alkoxysubstituenten eines Trialkoxysilans hydrolysiert und entsprechende Silanole gebildet. Diese können dann über eine SiOH-Funktion an den zu modifizierenden Werkstoff anbinden und über weitere Silanolgruppen unter Ausbildung von Siloxaneinheiten vernetzen. In einer weiteren Fallstudie wurde die Kinetik der Aufspaltungen einer cyclischen Silanverbindung untersucht. Die Online-NMR-Analytik kam hierbei sowohl im Labor als auch in der industriellen Produktionsanlage zum Einsatz. Hierfür wurde eine automatisierte Einhausung verwendet, welche den Einsatz eines kommerziellen NMR-Spektrometers in explosionsgeschützten Bereichen ermöglicht.
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-theart 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.
ISO/IEC 17025 is the worldwide quality standard for testing and calibration laboratories. It is the basis for accreditation by an accreditation body. The current version was published in 2018.
Implementing ISO/IEC 17025 as part of laboratory quality initiatives offers both laboratory and business benefits, such as expanding the potential customer base for testing and/or calibration, increasing the reputation and image of the laboratory at national and international level, continuous improvement of the data quality and the effectiveness of the laboratory or creation of a good basis for most other quality systems in the laboratory sector, such as GxP. The main difference between a proper approach to analysis and a formal accreditation is shown in a targeted documentation, especially on the qualification of the personnel, the test equipment and the validation of the analytical methods.
Using quantitative NMR spectroscopy as an example, it is shown how accreditation can be carried out and what documentation is required. In our case, we have described the procedure in an SOP ("Determination of the quantitative composition of simple mixtures of structurally known compounds with 1H-NMR spectroscopy") and supported it with a modular system of organizational and equipment SOPs. The special feature is that the accredited method is independent for the choice of the analyte and the matrix and therefore it is possible to operate with a single validated method. In our case, we have proposed three quality levels ("leagues") with different levels of analytical effort, which differ in their measurement uncertainty, in order to simplify the workflow and analysis design.
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.