Filtern
Dokumenttyp
- Vortrag (142)
- Zeitschriftenartikel (108)
- Posterpräsentation (63)
- Beitrag zu einem Tagungsband (9)
- Buchkapitel (4)
- Sonstiges (4)
- Dissertation (3)
- Forschungsdatensatz (3)
- Forschungsbericht (2)
- Monografie (1)
Sprache
- Englisch (247)
- Deutsch (91)
- Mehrsprachig (4)
Schlagworte
- Process Analytical Technology (38)
- Prozessanalytik (33)
- Prozessindustrie (29)
- Online NMR Spectroscopy (23)
- CONSENS (22)
- Soil (18)
- Digitalisierung (16)
- NMR spectroscopy (16)
- Process analytical technology (16)
- Benchtop-NMR (15)
Organisationseinheit der BAM
- 1.4 Prozessanalytik (342) (entfernen)
Paper des Monats
- ja (4)
Eingeladener Vortrag
- nein (142)
Digitalisierung und Industrie 4.0 verändern komplette Geschäftsmodelle, heben neue Effizienzpotenziale und stärken die Wettbewerbsfähigkeit. Auf dem 57. Tutzing-Symposion vom 15.–18.04.2018 wurde mit Vorträgen und Kreativworkshops erkundet, welche speziellen Anforderungen die Prozessindustrie hat, welche digitalen Innovationen bereits umgesetzt wurden und wo noch Handlungsbedarf besteht. Ein Workshop befasste sich mit den Themenfeldern Datenkonzepte, Datenanalyse, Big Data und künstliche Intelligenz. Es geht nicht um die Digitalisierung von heute. Im Angesicht der wachsenden Digitalisierung unserer Prozesse stellt sich die Frage, ob wir den Prozess wirklich gut kennen. Ob alle Verfahrensschritte detailliert hinterlegt wurden. Nur mit einem heuristischen Ansatz kann das vorhandene Wissen nicht digitalisiert werden.
Sehr schnell werden die Mechanismen eines Massenmarktes mit denen einer Nische verwechselt. Nicht jeder Mechanismus, den wir von großen Suchmaschinen oder Einkaufsportalen kennen, gibt uns einen Hinweis auf Nutzen und Verfügbarkeit für die Prozess- oder pharmazeutische Industrie. Eine gute Analyse der Anforderungen in der Zukunft mit einem Abgleich der derzeitigen technischen Möglichkeiten ist Voraussetzung für eine Verbesserung der derzeitigen digitalen Umsetzung. Dabei ist es sinnvoll unkonventionelle Methoden einzusetzen.
Digitalisierung und Industrie 4.0 verändern komplette Geschäftsmodelle, heben neue Effizienzpotenziale und stärken die Wettbewerbsfähigkeit. Auf dem 57. Tutzing-Symposion vom 15.–18.04.2018 wurde mit Vorträgen und Kreativworkshops erkundet, welche speziellen Anforderungen die Prozessindustrie hat, welche digitalen Innovationen bereits umgesetzt wurden und wo noch Handlungsbedarf besteht. Ein Workshop befasste sich mit den Themenfeldern Datenkonzepte, Datenanalyse, Big Data und künstliche Intelligenz.
Physical adsorption at cryogenic temperature (cryoadsorption) is a reversible mechanism that can reduce the pressure of conventional compressed gas storage systems. Metal–organic framework (MOF) materials are remarkable candidates due to the combination of high specific surface area and density which, in some cases, provide a high volumetric storage capacity. However, such extensive use of MOFs for this application requires the selection of affordable structures, easy to produce and made from feasible metallic and organic components. Herein, we introduce a MOF database detailing the crystallographic and porous properties of 3600 existing MOFs made from industrially relevant metals and their organic composition. The comparison of the available minimum costs of linkers allowed the creation of a database to select affordable structures with high potential for volumetric hydrogen storage by cryoadsorption, considering their composition based on individual or mixed building blocks. A user interface, available online, facilitates the selection of MOFs based on the properties or names of structures and linkers.
In order to identify the best porous materials for the cryogenic physisorption of hydrogen, high-throughput calculations are performed starting, i.e., from the collected information in crystallographic databases. However, these calculations, like molecular simulations, require specific training and significant computational cost. Herein, a relatively simple procedure is proposed to estimate and compare hydrogen uptakes at 77 K and pressure values from 40 bar starting from the porous properties of MOF materials, without involving simulation tools. This procedure uses definitions for adsorption and considers the adsorbed phase as an incompressible fluid whose pressure-density change is that for the liquid phase at 19 K. For the 7000 structures from the CoRE MOF database, the average error of the predictions is only of 1% from reference values at 100 bar, with an SD of ±8%. This accuracy is lower than that from simulation tools, but involving lower computational cost and training.
Mechanical flexibility in single crystals of covalently bound materials is a fascinating and poorly understood phenomenon. We present here the first example of a plastically flexible one-dimensional (1D) coordination polymer. The compound [Zn(m-Cl)2(3,5-dichloropyridine)2]n is flexible over two crystallographic faces. Remarkably, the single crystal remains intact when bent to 1808. A combination of microscopy, diffraction, and spectroscopic studies have been used to probe the structural response of the crystal lattice to mechanical bending. Deformation of the covalent polymer chains does not appear to be responsible for the observed macroscopic bending. Instead, our results suggest that mechanical bending occurs by displacement of the coordination polymer chains. Based on experimental and theoretical evidence, we propose a new model for mechanical flexibility in 1D coordination polymers. Moreover, our calculations propose a cause of the different mechanical properties of this compound and a structurally similar elastic material
A process spectroscopic approach for the monitoring of microplastics in environmental samples
(2018)
The potential of Raman and NIR process-spectroscopic as a rapid approach for the estimation of microplastics (MP) in soil matrix were tested. For this purpose, artificial MP/soil mixture samples consisting of standard soils or sand with defined ratios of MP (0,5 – 10 mass% polymer) from polyethylene (PE), polypropylene (PP), polystyrene (PS) and polyethylene terephthalate (PET) were prepared. MP particles with diameters < 2 mm and < 125 µm were obtained from industrial polymer pellets after cryo-milling. Spectra of these mixtures were collected by (i) a process FT-NIR spectrometer equipped with a reflection probe, (ii) by a cw-process Raman spectrometer and (iii) by a time-gated Raman spectrometer using fiber-optic probes. The evaluation of process-spectra was performed by chemometric methods. Whereas MP could be detected by Raman spectroscopy in coastal sand at 0.5 mass%, in standard soil detection of MP was limited to 10 – 5 mass% with the large fraction, but samples containing particles of the 125µm mass-fraction yielded no positive result at all. One reason for the lacking sensitivity could be fluorescence by soil organic matter and thus, in a next test time-gated Raman spectroscopy was applied. However, although being indeed more sensitive to the small particles this method failed at MP < 5 mass% indicating that fluorescence was not the major problem. Finally, FT-NIR was tested. Depending on the polymer, MP contents of 0,5 or 1 mass%, respectively, could be detected in standard soils and polymers identified. Furthermore, this approach could be used for the investigation of mixtures of up to four polymers and in real-world samples from bio-waste fermenter residues.
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 down-times 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 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.
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 sus-tainable fashion, and to adapt quickly to changing customer needs. The transition of process industry due to the mounting digitalization of technical devices and their pro-vided data used in chemical plants proceeds. Though, the detailed characteristics and consequences for the whole chemical and pharmaceutical industry are still unfore-seeable, 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 in-dustry 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 consumption of raw materials and energy. Flexible (modular) chemical plants can pro-duce 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 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 chemical 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 mod-elling 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.
References:
[1] M. V. Gomez et al., Beilstein J. Org. Chem. 2017, 13, 285-300
[2] K. Meyer et al., Trends Anal. Chem. 2016, 83, 39-52
[3] S. Kern et al., Anal Bioanal Chem. 2018, 410, 3349-3360
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.