Filtern
Erscheinungsjahr
Dokumenttyp
- Zeitschriftenartikel (23)
- Posterpräsentation (23)
- Vortrag (18)
- Beitrag zu einem Tagungsband (14)
- Handbuch (1)
- Sonstiges (1)
- Forschungsbericht (1)
- Forschungsdatensatz (1)
Sprache
- Englisch (82) (entfernen)
Schlagworte
- Process Analytical Technology (26)
- Online NMR spectroscopy (14)
- Process analytical technology (12)
- Reaction monitoring (10)
- Benchtop-NMR (9)
- CONSENS (9)
- NMR spectroscopy (9)
- Online NMR Spectroscopy (9)
- Prozessanalytik (9)
- Quantitative NMR spectroscopy (8)
- Benchtop NMR (7)
- qNMR (6)
- Hydroformylation (5)
- Gas-phase NMR (4)
- Indirect hard modeling (4)
- Industry 4.0 (4)
- Modular Production (4)
- Modular production (4)
- Process control (4)
- Reaction Monitoring (4)
- Artificial Neural Networks (3)
- Cyber-Physical Systems (3)
- EuroPACT (3)
- Industrie 4.0 (3)
- Interlaboratory Comparison (3)
- Liquefied petroleum gas (3)
- Mass Spectrometry (3)
- Metrology (3)
- Microemulsions (3)
- Mini-plant (3)
- Nuclear Magnetic Resonance Spectroscopy (3)
- Process Control (3)
- Process Monitoring (3)
- Quantitative NMR Spectroscopy (3)
- Smart Sensors (3)
- AAA (2)
- Amino acid analysis (2)
- Calibration (2)
- Chemometrics (2)
- Compact NMR (2)
- Compact NMR Spectroscopy (2)
- Data processing (2)
- ISO 17025 (2)
- Indirect Hard Modeling (2)
- Low field NMR spectroscopy (2)
- Medium-resolution NMR (2)
- Modular production units (2)
- NIR (2)
- NMR (2)
- NMR Spectroscopy (2)
- Online NMR (2)
- Online-NMR spectroscopy (2)
- Partial Least Squares Regression (2)
- Partial least squares regression (2)
- Protein hydrolysis (2)
- Quality Control (2)
- Quantitative NMR (2)
- Raman spectroscopy (2)
- Reference gas mixtures (2)
- Reference materials (2)
- Silanes (2)
- Temperature Control (2)
- Traceability (2)
- 19F (1)
- 19F-NMR (1)
- 1H (1)
- 1H-NMR (1)
- AAAA (1)
- Absorption (1)
- Accreditation (1)
- Affinity chromatography (1)
- Antibodies (1)
- Aromatic amino acid analysis (1)
- Artificial neural networks (1)
- Automation (1)
- Benchtop NMR Spectroscopy (1)
- Benzene-1,3,5-tricarboxylic acid (1)
- Biochemistry (1)
- Bisphenol-A (1)
- Bovine serum albumin (BSA) (1)
- Carbon capture (1)
- Cell studies (1)
- Chemical Process Control (1)
- Chemical Production (1)
- Compact NMR spectroscopy (1)
- Compound-independent calibration (1)
- Continuous Manufacturing (1)
- Continuous Processes (1)
- Continuous processes (1)
- Corundum (1)
- Cyber-physical systems (1)
- DFT (1)
- Data Fusion (1)
- Data evaluation (1)
- Dendrimer (1)
- Digital Transformation (1)
- Dispersion (1)
- Dye (1)
- Emulsions (1)
- Emuslions (1)
- Ergot alkaloids (1)
- Esterification (1)
- Field integration (1)
- First Principles (1)
- First principles (1)
- Flow NMR (1)
- Fluorescence spectroscopy (1)
- Gas analysis (1)
- Gas metrology (1)
- Gas-phase NMR spectroscopy (1)
- General Medicine (1)
- GxP (1)
- Handbook (1)
- High-pressure NMR (1)
- Histidine (1)
- Hydration (1)
- Hydrazinolysis (1)
- Hydrochloric acid (1)
- Hydrogenation (1)
- IHM (1)
- ISO/IEC 17025 (1)
- Imidazole (1)
- Indirect Hard Modelling (1)
- Industry 4.0, (1)
- Inline Analytics (1)
- Interlaboratory key comparison (1)
- Internal standard (1)
- Internal standards (1)
- Iterative real-time optimization (1)
- Kompost (1)
- Liquefied gases (1)
- Mass spectrometry (1)
- Micoemulsion (1)
- Microemulsion (1)
- Mikroemulsionen (1)
- Mikroplastik (1)
- Model Predictive Control (1)
- Modifier adaptation (1)
- Molecular Biology (1)
- Multivariat (1)
- Multivariate Data Analysis (1)
- NIR Spectroscopy (1)
- NIST (1)
- NMR Method Validation (1)
- NMR spetroscopy (1)
- Near Infrared Spectroscopy (1)
- Nonspecific binding (NSB) (1)
- Nuclear Magnetic Resonance (1)
- Nuclear magnetic resonance spectroscopy (1)
- Online (1)
- Online NMR Spectrsocopy (1)
- Online Raman Spectroscopy (1)
- Online Raman spectroscopy (1)
- Online monitoring (1)
- Online reaction monitoring (1)
- Online-NMR-Spektroskopie (1)
- Online-Raman spectroscopy (1)
- Online-Raman-Spektroskopie (1)
- Online-spectroscopy (1)
- PANIC (1)
- PLS-R (1)
- PLSR (1)
- Particle synthesis (1)
- Phenylalanine (1)
- Phenylketonuria (1)
- Photoreaction (1)
- Plant-model mismatch (1)
- Polyglycerol (1)
- Powder X-ray diffraction (1)
- Primary reference gas mixtures (1)
- Process Analytical Tecnology (1)
- Process Industry (1)
- Process monitoring (1)
- Process-NMR (1)
- Prozess-Spektroskopie (1)
- Purification (1)
- Purity assessment (1)
- Quantitative NMR-Spektroskopie (1)
- Quantitative protein analysis (1)
- Quantum Mechanics (1)
- Quantum mechanics (1)
- Raman (1)
- Reactor control (1)
- Real-time process monitoring (1)
- Real-time quality control (1)
- Salicylic acid (1)
- Salts (1)
- Sapphire (1)
- Self-assembled monolayers (SAM) (1)
- Sensor (1)
- Sensors (1)
- Silica and polystyrene nanoparticles (1)
- Soil (1)
- Solid-phase extraction (SPE) (1)
- Spectral modeling (1)
- SsNMR (1)
- Structural Biology (1)
- Sum parameter method (1)
- Terephthalic acid (1)
- Thermostating (1)
- Tryptophan (1)
- Tyrosine (1)
- Uncertainty Evaluation (1)
- ValidNMR (1)
- Validation (1)
- Weighing Uncertainty (1)
- compost (1)
- desorption control (1)
- pH probe (1)
Organisationseinheit der BAM
- 1 Analytische Chemie; Referenzmaterialien (47)
- 1.4 Prozessanalytik (47)
- 1.5 Proteinanalytik (3)
- 8 Zerstörungsfreie Prüfung (3)
- 8.6 Faseroptische Sensorik (3)
- 1.7 Organische Spuren- und Lebensmittelanalytik (2)
- 4 Material und Umwelt (2)
- 6 Materialchemie (2)
- 6.3 Strukturanalytik (2)
- 1.2 Biophotonik (1)
Paper des Monats
- ja (1)
Eingeladener Vortrag
- nein (18)
The transition 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. Here we introduce our smart online NMR sensor module provided in an explosion proof housing as example.
Due to NMR spectroscopy as an “absolute comparison method”, independent of the matrix, it runs with very short set-up times in combination with “modular” spectral models. These are based on pure component NMR spectra without the need for tedious calibrations runs. We present approaches from statistical, (i.e., Partial Least Squares Regression) to physically motivated models (i.e., Indirect Hard Modelling).
Based on concentration measurements of reagents and products by the NMR analyser a continuous production and direct loop process control were successfully realized for several validation runs in a modular industrial pilot plant and compared to conventional analytical methods (HPLC, near infrared spectroscopy). The NMR analyser was developed for an intensified industrial process funded by the EU’s Horizon 2020 research and innovation programme (“Integrated CONtrol and SENsing”, www.consens-spire.eu).
The goal of this work is to identify the optimal operating input for a lithiation reaction that is performed in a highly innovative pilot scale continuous flow chemical plant in an industrial environment, taking into account the process and safety constraints. The main challenge is to identify the optimum operation in the absence of information about the reaction mechanism and the reaction kinetics. We employ an iterative real-time optimization scheme called modifier adaptation with quadratic approximation (MAWQA) to identify the plant optimum in the presence of plant-model mismatch and measurement noise. A novel NMR PAT-sensor is used to measure the concentration of the reactants and of the product at the reactor outlet. The experiment results demonstrate the capabilities of the iterative optimization using the MAWQA algorithm in driving a complex real plant to an economically optimal operating point in the presence of plant-model mismatch and of process and measurement uncertainties.
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.
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.
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.
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.
Medium resolution nuclear magnetic resonance spectroscopy (MR-NMR) currently develops to an important analytical tool for both quality control and process monitoring. One of the fundamental acceptance criteria for online MR-MNR spectroscopy is a robust data treatment and evaluation strategy with the potential for automation. The MR-NMR spectra were treated by an automated baseline and phase correction using the minimum entropy method. The evaluation strategies comprised direct integration, automated line fitting, indirect hard modeling, and partial least squares regression.
Medium-resolution nuclear magnetic resonance spectroscopy (MR-NMR) currently develops to an important analytical tool for both quality control and processmonitoring. In contrast to high-resolution onlineNMR (HR-NMR),MR-NMRcan be operated under rough environmental conditions. A continuous re-circulating stream of reaction mixture fromthe reaction vessel to the NMR spectrometer enables a non-invasive, volume integrating online analysis of reactants and products. Here, we investigate the esterification of 2,2,2-trifluoroethanol with acetic acid to 2,2,2-trifluoroethyl acetate both by 1H HR-NMR (500MHz) and 1H and 19F MRNMR (43MHz) as amodel system. The parallel online measurement is realised by splitting the flow,which allows the adjustment of quantitative and independent flow rates, both in the HR-NMR probe as well as in the MR-NMR probe, in addition to a fast bypass line back to the reactor. One of the fundamental acceptance criteria for online MR-MNR spectroscopy is a robust data treatment and evaluation strategy with the potential for automation. The MR-NMR spectra are treated by an automated baseline and phase correction using the minimum entropy method. The evaluation strategies comprise (i) direct integration, (ii) automated line fitting, (iii) indirect hard modelling (IHM) and (iv) partial least squares regression (PLS-R). To assess the potential of these evaluation strategies for MR-NMR, prediction results are compared with the line fitting data derived from the quantitative HR-NMR spectroscopy. Although, superior results are obtained from both IHM and PLS-R for 1H MR-NMR, especially the latter demands for elaborate data pretreatment, whereas IHM models needed no previous alignment.
Accreditation of analytical methods, either according to GxP or ISO regulations, requires a comprehensive quality management system. General quality documents are often already in place, which need to be extended by method-specific documentation. In this presentation we like to show an idea of a modular set of standard operating procedures (SOP) specifically developed meeting the requirements for quantitative NMR. The future goal is to collaborate with different accreditated NMR laboratories to compile a universal set of SOP and other quality documents that can be modified and used as a starting point for developing your own quality system for applications of qNMR in a regulated environment.
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.