Filtern
Dokumenttyp
- Vortrag (18) (entfernen)
Sprache
- Englisch (18)
Referierte Publikation
- nein (18) (entfernen)
Schlagworte
- Process monitoring (18) (entfernen)
Organisationseinheit der BAM
- 8 Zerstörungsfreie Prüfung (11)
- 8.0 Abteilungsleitung und andere (10)
- 9 Komponentensicherheit (7)
- 1 Analytische Chemie; Referenzmaterialien (6)
- 9.3 Schweißtechnische Fertigungsverfahren (6)
- 1.4 Prozessanalytik (4)
- 1.9 Chemische und optische Sensorik (2)
- 5 Werkstofftechnik (2)
- 5.4 Multimateriale Fertigungsprozesse (2)
- 8.4 Akustische und elektromagnetische Verfahren (2)
Eingeladener Vortrag
- nein (18)
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. Robust field integration of NMR systems have to face explosion protection or integration into process control systems with short set-up times. Tis paves the way for industrial automation in real process environments.
Automated data preparation and analysis are cornerstones for a breakthrough of NMR techniques for process control. Particularly, robust chemometrics as well as automated signal processing methods have to be (further) developed especially for NMR spectroscopy in process control. This becomes even more important for so called “smart sensors” providing the basis for the future project “Industrie 4.0”, and Industrial Internet of Things (IIoT), along with current requirements to process control, model based control, or soft sensing. Data analysis techniques are available but currently mostly used for off-line data analysis to detect the causes of variations in the product quality.
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.
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. We present a range of approaches for the automated spectra analysis moving from statistical approach, (i.e., Partial Least Squares Regression) to physically motivated spectral models (i.e., Indirect Hard Modelling and Quantum Mechanical calculations). 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. Here we introduce our smart online NMR sensor module provided in an explosion proof housing as example.Based on concentration measurements of reagents and products by the NMR analyzer 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.
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. Here we introduce our smart online NMR sensor module provided in an explosion proof housing as example.
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. We present a range of approaches for the automated spectra analysis moving from statistical approach, (i.e., Partial Least Squares Regression) to physically motivated spectral models (i.e., Indirect Hard Modelling and Quantum Mechanical calculations).
Based on concentration measurements of reagents and products by the NMR analyzer 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).
Process monitoring with online NMR spectroscopy – An enabler for “Industrie 4.0” in process industry
(2018)
Improvement in deep process understanding is a mandatory prerequisite for the application of modern concepts like Industrial Internet of Things (IIoT) or “Industrie 4.0”. This is particularly relevant in new process concepts such as intensified production in modularized plants. The direct hyphenation with online methods of process analytical technology (PAT) allows profound insights into the actual reactions within chemical and pharmaceutical production steps and provides necessary information for associated control strategies.
While the industrial application of online Raman spectroscopy has already been successfully demonstrated, low-field NMR spectroscopy is not yet adequately developed as an online method for use in process industry. The high information content combined with the low calibration effort makes NMR spectroscopy a highly promising method for modern process automation with a high flexibility due to short set-up times and low requirements regarding validation. This is a major advantage especially within multi-purpose production plants, as well as for processes suffering from fluctuating quality of raw materials. NMR spectroscopy has a high potential for direct quantitative information, while cutting the calibration and validation needs to a minimum and thus exhibiting short set-up times.
Within the EU project CONSENS, an NMR analyzer for direct implementation in an industrial process environment was developed based on a commercially available laboratory instrument. The challenge was not only the hyphenation to the production plant itself, but also to fulfill all requirements of chemical industry, e.g., explosion safety regulations (ATEX), robust automation and modern, as well as classical communication interfaces. The presented NMR module involves a compact spectrometer together with an acquisition unit and a programmable logic controller for automated data preparation (phasing, baseline correction), and evaluation. The module transforms the acquired online spectra of various technically relevant reactions to either conventional 4‒20 mA signals as well as WiFi based OPC-UA communication protocols.
The concept was evaluated on two processes of pharmaceutical and chemical industry. As the first example the continuous synthesis of 2-nitrodiphenylamine starting from aniline and o-fluoronitrobenzene, activated by an organometallic lithium reagent, was studied. This application is highly demanding for a reliable automated evaluation of the obtained NMR spectra, which was realized by developing a physically motivated model-based approach.
In the second example, a stage of the synthesis of the industrially important solvent tetrahydrofurane consisting of the catalytic hydrogenation of 2-butine-1,4-diol was monitored. This reaction is proceeding via an intermediate product and suffers from competitive reaction paths. In this application different spectroscopic methods were combined with the data obtained from classical process sensors, e.g., pressure, temperature and flow transducers for the development of innovative control concepts.
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. Here we introduce our smart online NMR sensor module provided in an explosion proof housing as example.
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. We present a range of approaches for the automated spectra analysis moving from statistical approach, (i.e., Partial Least Squares Regression) to physically motivated spectral models (i.e., Indirect Hard Modelling and Quantum Mechanical calculations).
Based on concentration measurements of reagents and products by the NMR analyzer 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 project ProMoAM is presented. The goal of the project is to evaluate which NDT techniques or combination of techniques is suited for in-situ quality assurance in additive manufacturing of metals. To this end, also 3d-data fusion and visualization techniques are applied. Additional ex-situ NDT-techniques are used as references for defect detection and quantification. Feasability studies for NDT-techniques that are presently not applicable for in-situ use are performed as well.
The presentation gives a brief overview of the whole project and the different involved NDT-techniques.
For metal-based additive manufacturing, sensors and measuring systems for monitoring of the energy source, the build volume, the melt pool and the component geometry are already commercially available. Further methods of optics, spectroscopy and non-destructive testing are described in the literature as suitable for in-situ application, but there are only a few reports on practical implementations.
Therefore, a new BAM project aims to develop process monitoring methods for the in-situ evaluation of the quality of additively manufactured metal components. In addition to passive and active thermography, this includes optical tomography, optical emission and absorption spectroscopy, eddy current testing, laminography, X-ray backscattering and photoacoustic methods. These methods are used in additive manufacturing systems for selective laser melting, laser metal deposition and wire arc additive manufacturing. To handle the sometimes huge amounts of data, algorithms for efficient preprocessing are developed and characteristics of the in-situ data are extracted and correlated to defects and inhomogeneities, which are determined using reference methods such as computer tomography and metallography. This process monitoring and fusion of data of different measurement techniques should result in a significant reduction of costly and time-consuming, destructive or non-destructive tests after the production of the component and at the same time reduce the production of scrap.
Here, first results of simultaneous measurements of optical emission spectroscopy and thermography during the laser metal deposition process using 316L as building material are presented. Temperature values are extracted from spectroscopic data by fitting of blackbody emission spectra to the experimental data and compared with results from a thermographic camera. Measurements with and without powder flow reveal significant differences between welding at a pristine metal surface and previously melted positions on the build plate, illustrating the significant influence of the partial oxidation of the surface during the first welding process on subsequent welding. The measurement equipment can either be mounted stationary or following the laser path. While first results were obtained in the stationary mode, future applications for online monitoring of the build of whole parts in the mobile mode are planned.
This research was funded by BAM within the focus area Material.
Laser powder bed fusion (L-PBF) is one of the most promising additive manufacturing (AM) technologies for the production of complex metallic real part components. Due to the multitude of factors influencing process conditions and part quality and due to the layer-wise characteristic of the process, monitoring of process signatures seems to be mandatory in case of the production of safety critical components. Here, the iterative process nature enables unique access for in-situ monitoring during part manufacture. In this talk, the successful test of the synchronous use of a high-frequency infrared camera and a camera for long time exposure, working in the visible spectrum (VIS) and equipped with a near infrared filter (NIR), will be introduced as a machine manufacturer independent thermal detection monitoring set-up. Thereby, the synchronous use of an infrared camera and a VIS NIR camera combines the advantages of high framerate and high spatial resolution. The manufacture of a 316L stainless steel specimen, containing purposely seeded defects and volumes with forced changes of energy inputs, was monitored during the build. The measured thermal responses are analysed and compared with a defect mapping obtained by micro X-ray computed tomography (CT).
The first results regarding methods for data analysis, derived correlations between measured signals and detected defects as well as sources of possible data misinterpretation are presented in this talk.
Thermography is one on the most promising techniques for in-situ monitoring for metal additive manufacturing processes. The high process dynamics and the strong focus of the laser beam cause a very complex thermal history within the produced specimens, such as multiple heating cycles within single layer expositions. This complicates data interpretation, e.g., in terms of cooling rates. A quantity that is easily calculated is the time a specific area of the specimen is at a temperature above a chosen threshold value (TOT). Here, we discuss variations occurring in time-over-threshold-maps during manufacturing of a defect free cuboid specimen.