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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.
The GERG-2008 equation of state is the approved ISO standard (ISO 20765-2) for the calculation of thermophysical properties of natural gas mixtures. The composition of natural gas can vary considerably due to the diversity of origin. Further diversification was generated by adding hydrogen, biogas, or other non-conventional energy gases. In this work, high-precision experimental (p, rho, T) data for two gravimetrically prepared synthetic natural gas mixtures are reported. One mixture resembled a conventional natural gas of 11 components (11 M) with a nominal mixture composition (amount-of-substance fraction) of 0.8845 for methane as the matrix compound with the other compounds being 0.005 for oxygen, 0.04 for nitrogen, 0.015 for carbon dioxide, 0.04 for ethane, 0.01 for propane, 0.002 each for n- and isobutane, and ultimately 0.0005 each for isopentane, n-pentane, and nhexane. The other mixture was a 13-component hydrogen-enriched natural gas with a low calorific value featuring a nominal composition of 0.7885 for methane, 0.03 for hydrogen, 0.005 for helium, 0.12 for nitrogen, 0.04 for carbon dioxide, 0.0075 for ethane, 0.003 for propane, 0.002 each for n- and isobutane, and 0.0005 each for neopentane, isopentane, npentane, and n-hexane. Density measurements were performed in an isothermal operational mode at temperatures between 260 and 350 K and at pressures up to 20 MPa by using a single-sinker densimeter with magnetic suspension coupling. The data were compared with the corresponding densities calculated from both GERG-2008 and AGA8-DC92 equations of state to test their performance on real mixtures. The average absolute Deviation from GERG-2008 (AGA8-DC92) is 0.027% (0.078%) for 11 M and 0.095% (0.062%) for the 13-component H2-enriched mixture, respectively. The corresponding maximum relative Deviation from GERG-2008 (AGA8-DC92) amounts to 0.095% (0.127%) for 11 M and 0.291% (0.193%) for the H2-enriched mixture.
The limited availability of accurate experimental data in wide ranges of pressure, temperature, and composition is the main constraining factor for the proper development and assessment of thermodynamic models and equations of state. In the particular case of carbon capture and storage (CCS) processes, there is a clear need for data sets related to the (carbon dioxide + oxygen) mixtures that this work aims to address. This work provides new experimental (p, rho, T) data for three binary (CO2 + O2) mixtures with mole fractions of oxygen x(O2) = (0.05, 0.10, and 0.20) mol/mol, in the temperature range T = (250 to 375) K and pressure range p = (0.5 to 13) MPa. The measurements were performed with a high-precision single-sinker densimeter with magnetic suspension coupling. The density data were obtained with estimated expanded relative uncertainties of 0.02 % for the highest densities and up to 0.3 % for the lowest ones.The results were compared to the corresponding results calculated by the current reference equations of state for this kind of mixtures, namely the EOS-CG (combustion gases) and the GERG-2008 equation of state, respectively. The EOS-CG yields better estimations in density than the GERG-2008 equation of state. The results from the EOS-GC model show no systematic temperature dependence. For the GERG-2008 model, however, this criterion is significantly less fulfilled.
Most of the experimental density data of the methane−hydrogen binary system available at the time of the development of the equation of state for natural gases and related mixtures, GERG-2008, at temperatures above 270 K were limited to hydrogen contents higher than 0.20 (amount-of-substance fraction). On the contrary, for mixtures with hydrogen contents lower than 0.20, experimental data were limited to temperatures below 270 K. This work intends to close the gap and provides accurate experimental (p, ρ, and T) data for three binary mixtures of methane and hydrogen, (0.95 CH4 + 0.05 H2), (0.90 CH4 + 0.10 H2), and (0.50 CH4 + 0.50 H2), at temperatures of 240, 250, 260, 275, 300, 325, and 350 K, thus extending the range of available experimental data to higher temperatures for mixtures with hydrogen contents lower than 0.20 and, accordingly, to lower temperatures for mixtures with hydrogen contents higher than 0.20. The density measurements were performed by using a single-sinker densimeter with magnetic suspension coupling at pressures up to 20 MPa. Experimental data were compared to the corresponding densities calculated from the GERG-2008 and the AGA8-DC92 equations of state, respectively. The experimental data are within the uncertainty of both equations of state, except at the lower temperatures of 240 and 250 K and pressures over 14 MPa for the mixtures with a hydrogen content of 0.05 and 0.10, respectively. The virial coefficients B(T, x) and C(T, x), as well as the second interaction virial coefficient B12(T) for the methane−hydrogen binary system, were also calculated from the experimental data set at temperatures from 240 to 350 K using the virial equation of state.
The work presents speed of sound data of an 11-component synthetic natural gas as well as of a hydrogen-enriched natural gas measured at temperatures between 260 and 350 K and pressures up to 13 MPa. The results are compared with the corresponding values from two equation-of-state models, namely AGA8-DC92 and GERG-2008.
The demand for increasing product diversity in the chemical process industry calls for new production processes that enable greater flexibility. Therefore, plants are needed that produce significant quantities for market supply that can be scaled up to several tons per year. Compared to traditional batch processes, intensified continuous production enables not only flexibility but also the production of compounds that are difficult to produce. Custom-designed small-scale reactors significantly improve heat and mass transfer through micro mixing and can improve safety, e.g. in the case of high grade exothermic or high-pressure reactions, and might play an important role for customized, modular production facilities.1 Combined with optimally designed flow cells, compact NMR instruments currently present promising analytical tools for use in flow chemistry applications.2,3 In recent process monitoring applications, the flow cell and the mixing unit are usually separated parts leading to a severe time delay between mixing and first data acquisition.
In this work, we present a comprehensive workflow for the design of a flow cell−mixer combination based on CFD simulation and other design principles.4 Due to the increasing opportunities in additive manufacturing of ceramics, it was possible to realize an optimized SMX-type mixer with a fully integrated NMR flow cell (cf. Fig. 1).
Validation studies exhibited 1H NMR spectra with a quality comparable to common NMR glass tubes. So far, the mixing performance of the system has been evaluated for different mass flow rates within the intended working range of 5–120 seconds region and compared to ideally mixed samples. Thus, the integrated flow cell−static mixer combination can be used for different purposes such as evaluation of fluid properties, equilibration studies, or reaction monitoring of two instantaneously mixed samples.
The ceramic flow cell was additively manufactured and analyzed through x-ray microtomography revealing surface characteristics due to the manufacturing process (cf. Fig. 2). Furthermore, the working range of the whole system was characterized leading to an operational specification for further applications.
In summary, the role of custom-designed components for modular, chemical production, amongst other essential factors like fast development of reliable evaluation models is discussed.
References
[1] Bornemann-Pfeiffer et al., Chem. Ing. Tech. (2021), 93: 1–10
[2] Kern et al., Anal. Bioanal. Chem. (2018) 410: 3349–3360
[3] Kern et al., Anal. Bioanal. Chem. (2019) 411: 3037–3046
[4] Bornemann et al., Ind. Eng. Chem. Res. (2019), 58: 19562−19570
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 used for process characterisation and optimisation.
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 online NMR spectroscopy 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 module 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).
Eine der vielen Vorzüge Smarter Sensoren und Aktoren ist Bereitstellung zusätzlicher Informationen, auf die zukünftig neben den Hauptsignalen zugegriffen werden kann. In den Arbeitskreisen 3.6 und 3.7 wird dieses im wechselseitigen Austausch mit Geräte- und Softwareherstellern und Forschungseinrichtungen vorangetrieben.
Wir berichten kurz über den Stand der Diskussionen anhand von Beispielen und möchten in einem Open Space Workshop Meinungen und Ideen aufgreifen.
Der Vortrag stellt einige aktuelle Herausforderungen für die Prozessanalytik und mögliche Antworten vor.
Gepulste Raman-Spektrometer akkumulieren das Raman-Signal mit Hilfe schneller optischer Schalter im Picosekunden-Bereich, bevor langlebigere Fluoreszenzanregung entsteht. Damit lassen sich stark fluoreszierende Materialien untersuchen, die bislang nicht zugänglich sind. Eine weitere interessante Entwicklung ist etwa die Shifted excitation Raman difference spectroscopy (SERDS) die besonders für biologische Anwendungen interessant ist.
Flexible, modulare Produktionsanlagen stellen einen vielversprechenden Ansatz für die kontinuierliche Produktion von Fein- und Spezialchemikalien dar. In einem EU-Projekt wurde die Feldintegration eines Online-NMR-Sensormoduls als smartes Modul für die Prozesskontrolle vorangebracht. Dieses Modul basiert auf einem kommerziell erhältlichen Niederfeld-NMR-Spektrometer, welche zurzeit für die Anwendung im Laborbereich erhältlich ist. Für die Feldintegration wurde ein ATEX-zertifiziertes, explosionsgeschütztes Gehäuse entwickelt sowie Automationsschemen für den unbeaufsichtigten Betrieb und für die kalibrierfreie spektrale Datenauswertung erstellt.
Eine sehr gut anwendbare analytische Messtechnik zur Kontrolle der elementaren Zusammensetzung von verschiedensten Materialien ist die laserinduzierte Plasmaspektroskopie (LIPS, engl. LIBS - Laser-induced Breakdown Spectroscopy). Bei der LIBS wird ein kurz gepulster Laser auf eine Probe fokussiert, um ein Leuchtplasma zu erzeugen. Das dabei erzeugte Atomemissionsspektrum ermöglicht eine qualitative und quantitative Analyse der Zusammensetzung der Probe bezüglich praktisch aller Elemente des Periodensystems. In einem aktuellen Projekt wird diese Methode neben anderen zur Online-Analyse von Ackerböden für die ortsspezifischer Bewirtschaftung (Precision Agriculture) weiterentwickelt und bewertet.
Already Analyzing or Still Calibrating? - Demonstration of an online NMR analyzer in pilot scale
(2018)
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 CLEAN ENERGY Flagship is an initiative designed to utilize recent game changing developments in digital, materials and manufacturing technologies to catalyze a radical paradigm shift towards clean, reliable, efficient and cost-optimal energy.
Unifying and drastically accelerating radically new energy material design, processing and integration across the entire value chain addressing energy production, conversion, storage and systems.
CLEAN ENERGY participants are all distinguished research organisations that each benefit from their own industry networks and contacts with regions and state-level activities and have a long history of collaborating with each other (for 10 years now under the umbrella of EERA) within a European collaborative framework.
Through EERA, CLEAN ENERGY aims to become a crucial partner in the SET-Plan, supporting long-lasting approaches through its established networks and internal collaborations.
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 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 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 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.
Industry 4.0, IIoT, or Lab 4.0 will enable us to handle more complex processes in shorter time. Intensified production concepts require for adaptive analytical instruments and control technology to realize short set-up times, modular control strategies. They are based on a digitized Laboratory 4.0.
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 giving an overview from direct dissolution studies of API cocrystals to studies of emulsions for a hydroformylation.
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.
Low melting zinc borate glasses awake interest to replace lead borate glasses in the silver metallization pastes for solar cells or microelectronics. In the current study, characteristic properties of alkali zinc borate glasses (X2O-ZnO-B2O3, X = Li, Na, K, Rb) were compared to an earth alkali zinc borate glass (CaO-ZnO-B2O3). Additionally, zinc oxide is partially substituted by lead oxide or cooper oxide in the borate glasses (Li2O-PbO-B2O3, Na2O ZnO CuO-B2O3). The alkali zinc borate glasses indicate less differences in Raman spectra, and thus in structural properties, in comparison to the Ca and Pb ions influence. LPbB (Tg = 401 °C) has a lower viscosity than LZB (Tg = 468 °C) and CaZB has the highest glass transition temperature (Tg = 580 °C). The Angell plot for the alkali zinc borate glasses shows a high fragility m = 80. Besides Tg, the density measured by means of the Archimedean principle, molar volume, and coefficient of thermal expansion (CTE) of the glasses were investigated. Trends could be found according to alkali ions or intermediate oxides. The density increases with decreasing alkali ion size from KZB (2.632 g/cm3) to LZB (2.829 g/cm3) and increases from LZB to LPbB (3.764 g/cm3). CTE ranges between 7.09 10-6 K-1 for CaZB and 11.5 10 6 K 1 for KZB and RZB. The differential thermal analysis (DTA) and X ray diffraction (XRD) indicate crystallization of various crystalline phases during heating with 5 K/min in most cases.
Analysis of dynamic systems
(2019)
Monitoring specific information (i.e., physico-chemical properties, chemical reactions, etc.) is the key to chemical process control when looking at dynamic systems, and quantitative online NMR spectroscopy is the method of choice for the investigation and understanding of dynamic multi-component systems. NMR provides rapid and non-invasive information, and due to the inherent linearity between sample concentration and signal intensity, peak areas can be directly used for quantification of multiple components in a mixture (without the need for any further calibration). This is one of the most attractive features of quantitative NMR spectroscopy. With the launch of devices covering magnetic field strengths from 40 to 90 MHz, so called compact or benchtop NMR systems, this analytical method is now reaching a sufficient degree of compactness and operability for an application outside of very specialized laboratories.
Whilst there are also many other tools available to examine various analytical parameters from dynamic processes, such as mass spectrometry, (near) infrared or Raman spectroscopy, each of these tools can only really be used independently. How can we examine and compare all data describing a particular chemical reaction? How can we visualize information rich, specific, or direct methods together with less specific but established analytical methods? And how can we transfer calibration information to the most appropriate process analytical method or method combination? Quantitative NMR spectroscopy (qNMR) has the potential to substitute offline laboratory analysis for calibration purposes by delivering quantitative reference data as an online method.
The workshop briefly presents the current state of the art of the analysis of dynamic systems by online NMR spectroscopy and analytical data fusion, with the remaining time being used for questions and open discussion with the attendees.
Working towards a comprehensive understanding of introduction pathways, number, and fate of micro¬plastics in the environment, suitable analytical methods are a precondition. Micro-spectroscopic methods are probably the most widely used techniques. Besides their ability to measure single spectra of a particle or fiber, most modern FTIR- and Raman microscopes are also capable of two-dimensional imaging. This is very appealing to microplastics research because it allows to simultaneously characterize the analytes chemically as well as their size (distribution) and shape.
Two-dimensional imaging on extensive sample areas with FTIR-micros¬copes is facilitated by focal plane array (FPA) detectors resulting in large data sets comprised of up to several million spectra. With numbers too large for manual inspection of each individual spectrum, automated data evaluation is inevitable. Identifying different polymers based on the comparison with known reference spectra (library search) has proven to be a suitable approach. For that purpose, FTIR-spectra of common plastics can be collected to create an individual reference library.
To Supplement this ‘targeted analysis’, looking for known substances via library search, an exploratory approach was tested. Principal component analysis (PCA) proved to be a helpful tool to drastically reduce the size of the data set while maintaining the significant information. Subsequently, cluster analysis was used to find groups of similar spectra. Spectra found in different clusters could be assigned to different polymer types. The variation observed within clusters gives a hint on chemical variability of microplastics of the same polymer found in the sample. Spectra labeled according to the respective cluster/polymer type were used to build a classification model which allowed to quickly predict the polymer type based on the FTIR spectrum. Classification was tested on a second, independent data set and results were compared to the spectral library search procedure.
Der Vortrag gibt einen Überblick über die aktuellen Diskussionen zu Feldgerätekommunikation in der Prozessindustrie. Diese verfolgt seitens der Anwender in der NAMUR das NOA-Konzept (NOA = NAMUR Open Architecture). In gemeinsamen Aktivitäten mit dem ZVEI werden mit Zielstellung Mitte/Ende 2019 folgende Konzepte vorangebracht: NOA Informationsmodell in OPC UA,
Informationssicherheit und das Konzept des „Verification of Request“. Gemeinsam mit Geräte- und Softwareherstellern sollen auch M+O-Sensoren (Maintenance und Optimization) konzipiert werden.
Aus dem NAMUR AK 3.7 heraus wurde im Oktober ein Erweiterter Arbeitskreis „Smarte Sensoren, Aktoren und Kommunikation“ kontituiert, dem auch Geräte- und Softwarehersteller und das BSI angehören. Hier werden konkrete Anwendungsfälle für smarte Feldgeräte und Anforderungen an M+O-Sensoren erarbeitet, um die Bereitstellung und den Einsatz smarter Automatisierungskomponenten zu beschleunigen.
Zukünftig werden das „Smarte Labor“ und die „Smarte Produktion“ stärker zusammenwachsen.
Spatial Heterodyne Spectroscopy (SHS) is a spectrometric technique that combines both dispersive and interferometric features into a customizable instrument. The Basis of SHS is a Michelson interferometer with its mirrors replaced by diffraction gratings and with no moving parts. The output signal from SHS is the interferogram, which is recorded with a 1D or 2D pixel array detector. The spatial periodicity of the fringes on the interferogram is a function of the wavelength of the diffracted light. Using the Fast Fourier Transform, the original optical spectrum that enters SHS is retrieved. The light that is analyzed by SHS can come from a variety of sources. In our work, we used Raman scattering and Laser-Induced Plasma to perform quantitative and qualitative analyses. Figure 1 compares the performance of the SHS with that of high Resolution echelle and portable low-resolution asymmetrically crossed Czerny-Turner spectrometers (OO in Fig.1). The analyzed light came from the plasma induced on a stainless-steel reference material. The SHS exhibits the resolution comparable to that of the echelle spectrometer used, about 8000. Due to a high throughput of the SHS (theoretically, ~200 times higher than that of grating instruments), the number of spectra needed to be accumulated for comparable signal-to-noise ratios is much smaller than in the case of the echelle and comparable to OO spectrometers.
Examples of Raman SHS applied to several pure liquids are given in Fig. 2. Raman SHS was used in three different settings: (i) for classification of six types of oils, (ii) for univariate/multivariate analysis of binary mixture cyclohexane-isopropanol, and (iii) for multivariate analysis of glycerol solution in water. For the last two settings, chemometric analysis of the spectra yielded linear calibration plots over the range 1-90% of concentrations of isopropanol in cyclohexane, and 0.5-10% of glycerol in water.
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.
Industry 4.0 is all about interconnectivity, sensor-enhanced process control, and data-driven systems. Process analytical technology (PAT) such as online nuclear magnetic resonance (NMR) spectroscopy is gaining in importance, as it increasingly contributes to automation and digitalization in production. In many cases up to now, however, a classical evaluation of process data and their transformation into knowledge is not possible or not economical due to the insufficiently large datasets available. When developing an automated method applicable in process control, sometimes only the basic data of a limited number of batch tests from typical product and process development campaigns are available. However, these datasets are not large enough for training machine-supported procedures. In this work, to overcome this limitation, a new procedure was developed, which allows physically motivated multiplication of the available reference data in order to obtain a sufficiently large dataset for training machine learning algorithms. The underlying example chemical synthesis was measured and analyzed with both application-relevant low-field NMR and high-field NMR spectroscopy as reference method. Artificial neural networks (ANNs) have the potential to infer valuable process information already from relatively limited input data. However, in order to predict the concentration at complex conditions (many reactants and wide concentration ranges), larger ANNs and, therefore, a larger Training dataset are required. We demonstrate that a moderately complex problem with four reactants can be addressed using ANNs in combination with the presented PAT method (low-field NMR) and with the proposed approach to generate meaningful training data.
Atomkerne zählen - Quantitative NMR-Spektroskopie als vielseitige Methode in Labor und Prozess
(2020)
Meist präsentieren sich Laboratorien für NMR-Spektroskopie auf den ersten Blick von außen nur wenig einladend. Es lohnt sich aber einen Blick hinein zu riskieren, denn die Methode ist heute unverzichtbar für die analytische Chemie. Die quantitativen Möglichkeiten werden häufig unterschätzt, erfahren allerdings durch den aufstrebenden Markt kompakter Benchtop-NMR-Spektrometer verstärktes Interesse für Anwendungen von der Laborbank bis hin zum Industrieprozess.
Um die internationale Spitzenposition deutscher Unternehmen der Prozessindustrie auch in Zukunft sicher zu stellen, müssen die Unternehmen ihre Prozesse und Geschäftsabläufe digitalisieren und gemeinsam mit der Forschung innovative neue Methoden, Apparate, Anlagen, Sensoren und Automatisierungstechnik sowie Datenkonzepte entwickeln. All dies erfordert den Aufbau neuer Fähigkeiten, Investition in Talente, interdisziplinäre Kommunikation zwischen verschiedenen Personen und Abteilungen und eine Bereitschaft zur Veränderung.
Im Rahmen der von der BAM und anderen Partnern ins Leben gerufenen Initiative „Digitale Qualitätsinfrastruktur“ liegt der Fokus u.a. auf der Entwicklung von neuen Zertifizierungsworkflows. Dabei ist eine besondere Bedeutung dem Zusammenführen von Operational Technology (OT) und Informationstechnik (IT) beizumessen. Zur Datenintegration aus der Sensor-Feldebene einer Versuchs-wasserstofftankstelle wurde eine solche Infrastruktur zunächst in einer Laborumgebung aufgebaut.
Im Vortrag werden Anforderungen und Lösungsvorschläge für das Labor der Zukunft diskutiert. Industrie 4.0 bzw. das Labor 4.0 hilft uns, komplexere Prozesse schneller umzusetzen. Entwicklung von Anlagen und Prozessen beginnt im Labor 4.0. Dazu werden offene, nicht proprietäre Schnittstellen und Standards bei Laborgeräten und Feldgeräten dringend benötigt.
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.
Intelligent sensor systems, certified reference materials and instrumental analytical-chemical methods contribute to safety and functionality in hydrogen technologies.
This article gives a brief overview of SensRef activities in the Competence Centre H2Safety@BAM on the issues: Analytical methods for the determination of hydrogen purity, certified reference materials as measurement standards with regard to gas quality (primary calibrators) of BAM, test methods for gas sensor systems to detect hydrogen in air as well as the application of fibre-optic sensor systems to monitor the expansion and ageing behaviour of composite containers in hydrogen technologies.
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.
In den letzten Jahren erfreut sich die Benchtop-NMR-Spektroskopie steigender Beliebtheit. Zusätzlich zu Anwendungen im Labor werden die kompakten Geräte auch zunehmend in industriellen Bereichen eingesetzt. Neben geringeren Investitions- und Betriebskosten sind insbesondere die Robustheit und einfache Bedienbarkeit dieser Systeme entscheidende Faktoren. Ein prozessnaher Einsatz kann sowohl nach Probenahme im Rahmen der Betriebsanalytik erfolgen als auch als Online-Analytik mit direkter Ankopplung an Prozesse. Insbesondere bei der direkten Kopplung mittels Durchflusszellen spielt der Probentransfer eine entscheidende Rolle. Dies zeigt sich auch am Beispiel eines Extraktionsprozesses im Labormaßstab. Das in einem Bioprozess aus tierischen Altfetten hergestellte Copolymer PHB(Co-HHX) wurde in einem Batch-Prozess mit organischen Lösungsmitteln aus gefrier-getrockneten Zellen extrahiert. Unter Variation der Parameter wurde der Prozess direkt in der mehrphasigen Suspension mittels Benchtop-NMR-Spektroskopie verfolgt.
Eine große Limitierung für den Einsatz als Online-Methode ist die Temperaturempfindlichkeit der Magnetsysteme. Während die Umgebungstemperatur gut kontrollierbar ist, stellt sich die Thematik der Probentemperatur komplexer dar. Bei größeren Abweichungen von der Magnettemperatur kommt es bereits nach kurzer Zeit zu Drift-Effekten, die die Lock-Systeme nur begrenzt kompensieren können. Ein Ansatz ist die Nutzung temperierter Luftströme, ähnlich bekannter VT-Einheiten ergänzt um eine aktive Isolierung zum Magneten. An einem Modellaufbau wurden dazu verschiedene Konstruktionen zunächst mittels Thermographie charakterisiert und anschließend die Anwendbarkeit in einem Prototyp-Gerät überprüft.
In der rauen industriellen Umgebung von Produktionsanlagen sind weitere Maßnahmen zum Betrieb eines Laborgerätes nötig (z.B. Temperierung, Witterungsschutz). Zusätzlich bestehen häufig strikte Anforderungen an den Explosionsschutz. Zu diesem Zweck wurde gemeinsam mit der Evonik Rheinfelden eine Einhausung entwickelt, die es ermöglicht neben der NMR-Spektroskopie auch eine weitere analytische Methode möglichst flexibel direkt an großtechnische Anlagen anzubinden.
Im DFG-Schwerpunktprogramm 1374, Teilprojekt FunWood IV, erfolgt in Kooperation mit dem Institut für Bioanalytik an Hochschule für angewandte Wissenschaften, Coburg und dem Helmholtz-Zentrum für Umweltforschung (UFZ), Leipzig die Untersuchung des Ligningehalts von natürlich gealterten Hölzern. Zu diesem Zweck soll an der BAM eine NIR spektroskopische Methode entwickelt werden, die auch zur Untersuchung großer Stückzahlen geeignet ist. Insgesamt liegen exponierte Proben von 13 verschiedenen Hölzern vor, die sukzessive durch die Kooperationspartner kryovermahlen und getrocknet werden. Für jede der 13 Arten wurden jeweils 300 Holzstücken ausgebracht, exponiert und im Sommer 2018 wieder eingesammelt. Die Ergebnisse sollen als Grundlage für die Bewertung eines Langzeitversuchs zum Einfluss von Waldbewirtschaftungsintensität und Baumartenidentität über mehrere geographische Dimensionen dienen.
Bestimmung von Makro- und Mikronährstoffen in Böden mittels laserinduzierter Plasmaspektroskopie
(2018)
In respect of an efficient cultivation of agricultural cropland, a site-specific fertility management is necessary. Therefore, affordable and extensive mapping methods are needed. The research projects I4S (intelligence for soil) has the goal to develop a system for this purpose. I4S is one of ten interdisciplinary research project associations of the innovation programme called BonaRes, which is funded by the German Federal Ministry of Education and Research (BMBF).
The system includes a sensor platform, which contains different sensors, like XRF, VIS-NIR, Gamma and LIBS. The main task of LIBS measurements in this project is the real-time determination of the elemental contents of major and minor nutrients in soils, like calcium, magnesium, potassium. LIBS (laser-induced breakdown spectroscopy) is known as a fast and simultaneous multi-element analysis with little or no sample preparation. The main task of LIBS measurements in this project is the real-time determination of the elemental contents of nutrients in soils, like calcium, magnesium, potassium. For this purpose, a special setup has been designed. The sample uptake operates with the help of a rotatable sample plate which circulates with different velocities to simulate the application on the field. To provide a higher intensity and a better reproducibility of the obtained signal, a double-pulse Nd:YAG laser (1064 nm) was used. In order to minimize dust formation from the soil during the operation of the laser, a dust removal by suction has been integrated. When using relative methods such as LIBS, a suitable calibration curve is needed for absolute quantification. The complex matrix of soils, as well as the influence of moisture and grain size in soils makes the absolute quantification by LIBS challenging. To overcome these influences, chemometric methods were used. With the principal component analysis (PCA) a classification of soils into different soil types was performed and a calibration curve based on partial least squares regression (PLSR) was generated. With this calibration model’s elemental distribution maps for different German agricultural fields were created.
Gegenstand der vorzustellenden Arbeiten ist die Prüfung der Umwelt-beständigkeit und -verträglichkeit von Materialien und Produkten hinsichtlich der Emission von potenziellen Schadstoffen in die Umwelt. Hierzu werden chemisch-physikalische Einflüsse (Bewitterung) und mikrobielle Beanspruchungen an Modellmaterialien evaluiert. So werden die Freisetzungsraten von Schadstoffen in Abhängigkeit der Beanspruchung beschrieben. Als Modellmaterialien kommen die Polymere Polystyrol (PS) und Polypropylen (PP) zum Einsatz. Synergistische Effekte der Bewitterungsparameter und der mikrobiologischen Beanspruchung sollen dabei ebenso betrachtet werden, wie die gezielte Alterung. Auch findet eine Beschreibung des Verhaltens der ausgetragenen Schadstoffe in den Umweltkompartimenten Boden oder Wasser statt. Hier sind mit Hilfe der zu entwickelnden Screening- und non-Target-Analyseverfahren die Transformation und der Metabolismus durch Mikroorganismen zu beschreiben. Aus den Ergebnissen sollen Korrelationen zwischen den künstlichen Alterungsverfahren und realen Szenarien abgeleitet werden.
Gegenstand der vorzustellenden Arbeiten ist die Prüfung der Umweltbeständigkeit und -verträglichkeit von Materialien und Produkten hinsichtlich der Emission von potenziellen Schadstoffen in die Umwelt. Hierzu werden chemisch-physikalische Einflüsse (Bewitterung) und mikrobielle Beanspruchungen an Modellmaterialien evaluiert. So werden die Freisetzungsraten von Schadstoffen in Abhängigkeit der Beanspruchung beschrieben. Als Modellmaterialien kommen die Polymere Polystyrol (PS) und Polypropylen (PP) zum Einsatz, die jeweils mit polybromierten Flammschutzmitteln (PBFSM) versehen sind. Synergistische Effekte der Bewitterungsparameter und der mikrobiologischen Beanspruchung sollen dabei ebenso betrachtet werden, wie die gezielte Alterung. Auch findet eine Beschreibung des Verhaltens der ausgetragenen Schadstoffe in den Umweltkompartimenten Boden oder Wasser statt. Hier sind mit Hilfe der zu entwickelnden Screening- und non-Target-Analyseverfahren die Transformation und der Metabolismus durch Mikroorganismen zu beschreiben. Auch soll mit Hilfe der RFA und der LA-ICP-MS die Abreicherung der PBFSM in den Modellmaterialien beschrieben werden. Aus den Ergebnissen sollen Korrelationen zwischen den künstlichen Alterungsverfahren und realen Szenarien abgeleitet werden.
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.
Solubility data of carbon dioxide (CO2) in the two ionic liquids 1-butyl-3-methylimidazolium tetrafluoroborate [BMIM][BF4] and 1-butyl-3-methylimidazolium tetrachloroferrate [BMIM][FeCl4] at T = (273.15-413.15) K and pressures up to p = 4.5 MPa are presented. In Addition to the experiments, a literature review was done to compare the new results with published solubility data. The measurements were carried out using an isochoric method which operates in decrements of deltaT = 20 K within the investigated temperature range and at selected four different pressure steps ranging from a pressure p of around 4.5 MPa to around 0.5 MPa. The solubility of CO2 decreases in both ionic liquids with increasing temperatures. Within the p,T-range investigated, CO2 displayed a solubility in [BMIM][BF4] from a mole fraction x = 0.0117 and a corresponding molality m = 0.0526 mol/kg at T = 413.15 K and p = 0.417 MPa up to x = 0.4876 and m = 4.2094 mol/kg at T = 293.15 K and p = 4.349 MPa. The corresponding values for the solubility in [BMIM][FeCl4] start at a mole fraction x = 0.0268 and a corresponding molality m = 0.0818 mol/kg at T = 413.15 K and p = 0.443 MPa and end at x = 0.5126 and m = 3.1216 mol/kg at T = 293.15 K and p = 4.478 MPa. At a constant temperature, CO2 is better soluble in [BMIM][FeCl4] than in [BMIM][BF4] and the mean value of the solubility difference related to mole fraction x over the pressure range investigated amounts to about 4 % at T = 273.15 K and monotonously increases to about 92 % at T = 413.15 K. Henry's law constant as well as derived thermodynamic properties, such as the Gibbs energy of solvation, the enthalpy of solvation, the entropy of solvation, and the heat capacity of solvation, were calculated and discussed regarding the solute-solvent molecular interactions.
The quantification of the elemental content in soils with laser-induced breakdown spectroscopy (LIBS) is challenging because of matrix effects strongly influencing the plasma formation and LIBS signal. Furthermore, soil heterogeneity at the micrometre scale can affect the accuracy of analytical results. In this paper, the impact of univariate and multivariate data evaluation approaches on the quantification of nutrients in soil is discussed. Exemplarily, results for calcium are shown, which reflect trends also observed for other elements like magnesium, silicon and iron. For the calibration models, 16 certified reference soils were used. With univariate and multivariate approaches, the calcium mass fractions in 60 soils from different testing grounds in Germany were calculated. The latter approach consisted of a principal component analysis (PCA) of adequately pre-treated data for classification and identification of outliers, followed by partial least squares regression (PLSR) for quantification. For validation, the soils were also characterised with inductively coupled plasma optical emission spectroscopy (ICP OES) and X-ray fluorescence (XRF) analysis. Deviations between the LIBS quantification results and the reference analytical results are discussed.
Volatile organic compound (VOC) emissions from consumer products contribute to human inhalation exposure and may cause adverse health effects. Existing methods to determine long-term VOC emissions from e.g. building products need to be verified for their suitability to reliably detect initial VOC emissions from consumer products within the first hours and days of use, which would facilitate realistic inhalation exposure assessments. To investigate this issue, VOCs emitted from a test sample were determined in a large-scale emission test chamber and in two micro-chambers of different volumes, and the results were compared.
In addition to previously reported results on the accelerated weathering of polystyrene samples (PS) containing 1 wt.% hexabromocyclododecane (HBCD), we present the first results of our investigations of polypropylene samples (PP) containing 0.1 wt.% BDE-209. All studied polymer samples were exposed to a defined weathering schedule in a climate chamber in accordance to regulation EN ISO 4892-3:2006. For the determination of BDE-209 in the collected raining water the samples were prepared in accordance to a validated protocol. Before the analyses each sample was spiked with isotopically labeled BDE-209. Subsequently the samples were extracted with isooctane. The obtained extracts were concentrated, and the resulting solutions were analyzed by GC/MS. Additionally, the total bromine content was monitored for the weathered and untreated samples using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) and X-ray fluorescence analysis (XRF) as a non-destructive and rapid method. In general, the resulting data from the accelerated weathering will be compared to those from the natural weathering experiments. Here, the surfaces of the test pieces were analyzed by LA-ICP-MS and XRF as well. Moreover, soil bed tests were conducted in a well characterized model soil. This soil (boulder-clay, sand with 12 % loam, particle size in total 0.2-4 mm) is filled in a free-draining basin inside of an air-conditioned cellar. In this manner, TOC, water capacity and humidity are recorded parameters. To induce a leaching process from the samples by the raining water, the target water content is calculated to 8%. The actual humidity is measured by a tensiometer, assuring the duration of the raining period. A defined humidity is a fundamental parameter for a biological activity in the soil. The test polymers were placed up to the half in the soil. Microbial activity of the soil is monitored by a reference polymer (polyurethane) and should induce the release of HBCD and BDE-209 out of the test materials. These released analytes will be captured by passive samplers (silicone tubes) placed in a distinct distance to the polymer samples in the soil. The soil bed experiments are complementary to the weathering experiments due to the biological activity in the soil.
Lignin is a highly aromatic low value biomass residue, which can be utilized for chemicals, fuels and materials production. In recent years, significant attention has focused on adsorber materials based on lignin. However, only 5% of the available lignin is exploited worldwide, thus significant opportunities for materials development still exist. Lignin has been vastly utilized as a feedstock for the synthesis of adsorbers for the removal of dyes and toxic organic molecules . To improve adsorption capacity, lignin can be chemically modified with oxygen-, nitrogen-, or sulfur-containing functional groups. Most frequently the phenolic groups of lignin were modified. In order to extend the application of lignin as adsorbers, we are especially interested in the chemical modification of this natural resource with a bio-based linear difluoro oligomers LOx (di- or monofunctional) or a heterocyclic monomer (Figure).
The present study investigated decoration of Cu(OH)₂ with different morphologies by copper precursors on 3D nickel foam. The Cu(OH)₂-A (nano flower)electrode showed an excellent capacitance of 1332 Fg⁻¹ at current density of 2 Ag⁻¹ compared to the Cu(OH)₂-C (nano ribbon, 1100 Fg⁻¹) and Cu(OH)₂-S (nano Long leaf, 1013 Fg⁻¹) electrodes. An asymmetric supercapacitor (ASC) was fabricated and showed a Maximum capacitance of 165 Fg⁻¹ at current density of 2 Ag⁻¹ with high energy density of 66.7 Wh kg⁻¹ and power density of 5698 W kg⁻¹ with excellent stability of 80 % after 10,000 cycles.
Chemische Prozesskontrolle für modulare Produktionskonzepte mit direkten analytischen Methoden
(2020)
Flexible Produktionskonzepte werden oft mit modularen, schnell rekonfigurierbaren Einheiten verbunden. Um die Vorteile dieser flexiblen Produktion tatsächlich nutzen zu können ist neben einer geeigneten Infrastruktur auch eine ebenso anpassungsfähige Qualitätskontrolle unverzichtbar. Analysengeräte und Sensoren werden heutzutage immer kompakter und kostengünstiger in ihrer Anschaffung. Dadurch wird es möglich eine große Anzahl von Messstellen innerhalb einer Anlage zu realisieren. Die erfassten Datenmengen erfordern zuverlässige und robuste Auswerteverfahren, die sich in das Gesamtkonzept der Automatisierung einer derart flexiblen Produktion nahtlos integrieren. Referenzdaten aus direkten Methoden wie der NMR-Spektroskopie können hier einen wertvollen Beitrag zur Generierung von Prozesswissen für die Entwicklung neuer Modelle für kostengünstigere Analysenverfahren leisten.
Metal-organic framework-based biocomposites (MOF-biocomposites) are promising materials for biosensing, biocatalysis, and delivery of biopharmaceuticals. One of the most studied MOFs for bioapplications is ZIF-8 (zeolitic imidazolate framework 8) due to its high surface area, high thermal and chemical stability, and low cytotoxicity. The conventional synthesis of ZIF-8-biocomposites called biomimetic mineralization includes mixing selected biomolecules 2-methylimidazole, and soluble Zn2+ source in water.[3] Despite the high efficiency of the method, it does not allow for large-scale production and is restricted to hydrophilic biomolecules. Aimed at developing a scalable and versatile approach, we adapted our recently-reported ZIF-8 reactive extrusion for biocomposite production.
We selected bovine serum albumin (BSA) as an inexpensive model biomacromolecule for the preparation of biocomposites. The synthesis of BSA@ZIF-8 was performed using a twin-screw extruder ZE 12 HMI (Three-Tec Gmbh) at a mild temperature of 40 °C. Automatic volumetric feeder ZD 12B (Three-Tec GmbH) was used to supply the reagent mixture consisting of 2-methylimidazole, zinc source, and BSA. To initiate the reaction, a catalytic amount of EtOH was added using a peristaltic pump BT-L (Lead Fluid, China). Powder X-Ray diffraction (PXRD), thermogravimetric analysis (TGA), FTIR, and N2 adsorption were used to characterize the extrudates.
Highly crystalline and pure BSA@ZIF-8 with different BSA loadings was isolated after washing the extrudate with EtOH and sodium dodecyl sulfate. The EtOH feeding rate was optimized by following the protein encapsulation efficiency at a BSA mass fraction of 10%. A continuous extruder operation under optimized conditions showed good reproducibility and capability of producing biocomposites on the kilograms scale. These results provide highly valuable information for cheap and large-scale production of ZIF-8-based biocomposites. Due to the lack of restrictions on molecule size and solubility, our proof-of-concept study may significantly expand the selection of biomolecules for immobilization in ZIF-8, making the method applicable to various functional applications
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.
Compact NMR Spectroscopy in the field: A Versatile PAT Tool for Production of Specialty Chemicals
(2023)
Companies of chemical industry find themselves more often in a rapidly changing environment, e.g., due to variability of raw material quality or energy costs and efficiency. Process optimization and new process concepts become more and more important. For example, 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 like exothermic reactions with high heat dissipation.
Highly automated “chemical” process control along with real-time quality control are prerequisites to such concepts and thus should be based on “chemical” information. A commercially available benchtop NMR spectrometer was integrated to the full requirements of an automated chemical production environment such as explosion safety, field communication, and robust evaluation of sensor data. Field studies in modular and conventional production plant setups show promising results gaining process knowledge for further optimization. NMR appeared as preeminent online analytical method and allow using a modular data analysis approach, which can even serve as reliable reference method for further calibration-dependent PAT applications (e.g., NIR or Raman spectroscopy).
Based on experiences from earlier field studies an improved NMR analyzer enclosure setup was developed and built, including the option of a secondary method (e.g., optical spectroscopy). Integrated control systems allow for a flexible implementation based on the available automation infrastructure at the chemical plant or pilot plant setup. In the future, fully integrated and intelligently interconnecting “smart” PAT systems and processes have the potential speed up the setup of production equipment for chemicals and pharmaceuticals and therefore help to reduce the time-to-market.
Compact NMR Spectroscopy in the field: A Versatile PAT Tool for Production of Specialty Chemicals
(2022)
Companies of chemical industry find themselves more often in a rapidly changing environment, e.g., due to variability of raw material quality or energy costs and efficiency. Process optimization and new process concepts become more and more important. For example, 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 like exothermic reactions with high heat dissipation.
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. A commercially available benchtop NMR spectrometer was integrated to the full requirements of an automated chemical production environment such as explosion safety, field communication, and robust evaluation of sensor data. Field studies in modular and conventional production plant setups show promising results gaining process knowledge for further optimization. NMR appeared as preeminent online analytical method and allow using a modular data analysis approach, which can even serve as reliable reference method for further calibration-dependent PAT applications (e.g., NIR spectroscopy). Based on experiences from these field studies an improved analyzer enclosure setup was developed and built, including the option of a secondary method besides NMR spectroscopy. Integrated control systems allow for a flexible implementation based on the available automation infrastructure at the chemical plant or pilot plant setup. In the future, fully integrated and intelligently interconnecting “smart” PAT systems and processes have the potential speed up the setup of production equipment for chemicals and pharmaceuticals and therefore help to reduce the time-to-market.
Compact NMR Spectroscopy in the field: A Versatile PAT Tool for Production of Specialty Chemicals
(2022)
Chemical companies must find new paths to successfully survive in a changing environment, especially by utilizing 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 for new products.
Therefore, fully automated process control along with real-time quality control are prerequisites to such concepts and thus should be based on “chemical” information. A commercially available benchtop NMR spectrometer was integrated to the full requirements of chemical production environment such as explosion safety, field communication, and robust evaluation of sensor data. It was used for direct loop advanced process control and real-time optimization of the process. Field studies in modular and conventional production plants show promising results gaining process knowledge. NMR appeared as preeminent online analytical method and allow using a modular data analysis tool, which even served as reliable reference method for further PAT applications (e.g. NIR spectroscopy). In the future, fully integrated and interconnecting “smart” systems and processes can speed up the high-quality production of specialty chemicals and pharmaceuticals.
Compact NMR Spectroscopy in the field: A Versatile PAT Tool for Production of Specialty Chemicals
(2022)
Chemical companies must find new paths to successfully survive in a changing environment, especially by utilizing 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 for new products.
Therefore, fully automated process control along with real-time quality control are prerequisites to such concepts and thus should be based on “chemical” information. A commercially available benchtop NMR spectrometer was integrated to the full requirements of chemical production environment such as explosion safety, field communication, and robust evaluation of sensor data. It was used for direct loop advanced process control and real-time optimization of the process. Field studies in modular and conventional production plants show promising results gaining process knowledge. NMR appeared as preeminent online analytical method and allow using a modular data analysis tool, which even served as reliable reference method for further PAT applications (e.g. NIR spectroscopy). In the future, fully integrated and interconnecting “smart” systems and processes can speed up the high-quality production of specialty chemicals and pharmaceuticals.
Compact NMR Spectroscopy in the field: A Versatile Tool for Automated Continuous- Flow Production
(2021)
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. 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. Field studies in modular and conventional production plant setups show promising results gaining process knowledge for further optimization. NMR appeared as preeminent online analytical method and allow using a modular data analysis tool, which even served as reliable reference method for further PAT applications (e.g. NIR spectroscopy). In the future, such fully integrated and intelligently interconnecting “smart” systems and processes can speed up the high-quality production of specialty chemicals and pharmaceuticals.
Compact NMR Spectroscopy in the field: A Versatile Tool for Automated Continuous-Flow Production
(2022)
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. 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. Field studies in modular and conventional production plant setups show promising results gaining process knowledge for further optimization. NMR appeared as preeminent online analytical method and allow using a modular data analysis tool, which even served as reliable reference method for further PAT applications (e.g. NIR spectroscopy). In the future, such fully integrated and intelligently interconnecting “smart” systems and processes can speed up the high-quality production of specialty chemicals and pharmaceuticals.
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.
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.
Recently, AI procedures have also been successfully used for NMR data evaluation. In order to overcome the typical limitation of too small data sets from process developments, a new method was tested, which allows a physically motivated multiplication of the available reference data together with context information in order to obtain a sufficiently large data set for the training of machine learning algorithms.
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 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.
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 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 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.
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.
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.
Um in einem veränderten Umfeld erfolgreich bestehen zu können, müssen Chemieunternehmen neue Pfade beschreiten. Dazu gehört insbesondere das Potential digitaler Technologien. Mit flexiblen, modularen chemischen Vielzweck-Produktionsanlagen lassen sich häufig wechselnde Produkte mit kürzeren Vorlauf- und Stillstandzeiten zwischen den Kampagnen und dennoch hoher Qualität realisieren. Intensivierte, kontinuierliche Produktionsanlagen erlauben auch den Umgang mit schwierig zu handhabenden Substanzen.
Grundvoraussetzung für solche Konzepte ist eine hochautomatisierte "chemische" Prozesskontrolle zusammen mit Echtzeit-Qualitätskotrolle, die "chemische" Informationen über den Prozess bereitstellt. In einem Anwendungsbeispiel wurde eine pharmazeutische Lithiierungsreaktion aus einer modularen Pilot-Anlage betrachtet und dabei die Vorzüge eines vollautomatischen NMR-Sensors untersucht. Dazu wurde ein kommerziell erhältliches Benchtop-NMR-Spektrometer mit Permanentmagnet auf die industriellen Anforderungen, wie Explosionsschutz, Feldkommunikation und vollautomatischer, robuster Datenauswertung angepasst. Der NMR-Sensor konnte schließlich erfolgreich im vollautomatischen Betrieb nach fortschrittlichen Regelkonzepten und für die Echtzeitoptimierung der Anlage getestet werden. Die NMR-Spektroskopie erwies sich als hervorragende Online-Methode und konnte zusammen mit einer modularen Datenauswertung sehr flexibel genutzt werden. Die Methode konnte überdies als zuverlässige Referenzmethode zur Kalibrierung konventioneller Online-Analytik eingesetzt werden.
Zukünftig werden voll integrierte und intelligent vernetzte "smarte" Sensoren und Prozesse eine kontinuierliche Produktion von Chemikalien und Pharmazeutika mit vertretbaren Qualitätskosten möglich machen.
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.
Aim of the Federal Institute for Materials Research and Testing (BAM) in the frame of I4S - “Intelligence for Soil” is the characterization of an X-ray fluorescence (XRF) based sensor for robust online-application of arable land.Fast soil mapping for agricultural purpose allows the site-specific optimized introduction of plant essential nutrients like S, K, Ca, and Fe. This is important given that the distribution of minor and trace elements varies widely. The non-destructive and contactless XRF is suitable for rapid in-situ analysis on the field due to minimal sample preparation and simultaneous multi-element analysis.
Soils are already considered as a complex matrix due to their wide range of elements in different contents, especially light elements with low atomic numbers (Z<19). Problems by measuring soil samples also arise from heterogeneity of the sample and matrix effects. Large grain size distribution causes strong inhomogeneity and matrix effects occur through physical properties related to high concentration of main components. Matrix-specific calibration strategies for determination of total major and minor plant essential nutrients are particularly important regarding these difficulties. For accurate calibration, data treatment and evaluation must also be considered. Empirical univariate and multivariate data analysis were compared regarding their analytical figures of merit. Using principal component analysis (PCA) it was possible to classify German soils in different groups as sand, clay and silt. A calibration curve was obtained by partial least squares regression (PLSR) and the elemental content of German soils was predicted. Elemental distribution maps for different German arable lands were created and the results compared to reference measurements. The correlation between predicted values and reference values were in good agreement for most major and minor nutrients.
As part of the BonaRes research initiative funded by the German Federal Ministry of Education and Research (BMBF), strategies are being developed to use soil as a sustainable resource in the bioeconomy. The interdisciplinary subproject I4S - “Intelligence for soil” - is responsible for the development of an integrated system for site-specific management of soil fertility. For this purpose, a platform is constructed and various sensors are installed. Real-time data will be summarized in models and decision-making algorithms will be used to control fertilization and accordingly improve soil functions. This would allow investigations in close meshed dynamic grid and fast analysis of large areas to generate higher yields. This is important given that the distribution of minor and trace elements varies widely. Aim of the Federal Institute for Materials Research and Testing (BAM) in the frame of I4S is the characterization of an X-ray fluorescence (XRF) based sensor for robust online-analysis of arable land. The non-destructive and contactless XRF is suitable for rapid in-situ analysis on the field due to minimal sample preparation and simultaneous multi-element analysis.
Soils are already considered as a complex matrix due to their wide range of elements in different contents, especially light elements with low atomic numbers (Z<19). Problems by measuring soil samples also arise from heterogeneity of the sample and matrix effects. Large grain size distribution causes strong inhomogeneity and matrix effects occur through physical properties related to high concentration of main components. Matrix-specific calibration strategies for determination of total major and minor plant essential nutrients are particularly important regarding these difficulties. For accurate calibration, data treatment and evaluation must also be considered. Univariate and multivariate data analysis were compared regarding their analytical figures of merit. Using principal component analysis (PCA) it was possible to classify German soils in different groups as sand, clay and silt. Calibration models were obtained by partial least squares regression (PLSR) and the content of macro- and micronutrients in German soils was predicted. Elemental distribution maps for different German arable lands were created and the results compared to reference measurements. The correlation between predicted values and reference values were in good agreement for most major and minor nutrients.
The application of Raman spectroscopy as a monitoring technique for bioprocesses is severely limited by a large background signal originating from fluorescing compounds in the culture media. Here, we compare time-gated Raman (TG-Raman)-, continuous wave NIRprocess Raman (NIR-Raman), and continuous wave micro-Raman (micro-Raman) approaches in combination with surface enhanced Raman spectroscopy (SERS) for their potential to overcome this limit. For that purpose, we monitored metabolite concentrations of Escherichia coli bioreactor cultivations in cell-free supernatant samples. We investigated concentration transients of glucose, acetate, AMP, and cAMP at alternating substrate availability, from deficiency to excess. Raman and SERS signals were compared to off-line metabolite analysis of carbohydrates, carboxylic acids, and nucleotides. Results demonstrate that SERS, in almost all cases, led to a higher number of identifiable signals and better resolved spectra. Spectra derived from the TG-Raman were comparable to those of micro-Raman resulting in well-discernable Raman peaks, which allowed for the identification of a higher number of compounds. In contrast, NIR-Raman provided a superior performance for the quantitative evaluation of analytes, both with and without SERS nanoparticles when using multivariate data analysis.
Competence Centre H2Safety@BAM – Safeguarding the Quality Infrastructure in the Hydrogen Economy
(2022)
Hydrogen is a central component of the energy transition and the European Green Deal for a climate-neutral Europe. To achieve the goals defined for 2050, the EU and the German government have developed a framework for action and are making long-term investments in research, development, and the rapid implementation of innovative hydrogen technologies.
With the Competence Centre "H2Safety@BAM", BAM is creating the safety-related prerequisites for the successful implementation of hydrogen technologies at national as well as European level.
To this end, BAM conducts research, testing and consulting in a holistic and inter-disciplinary manner under one roof – in the Competence Centre H2Safety@BAM. The activities of the competence centre focus on ensuring the safety of infra-structures, plants, and processes as well as innovative hydrogen transport and storage systems based on digital quality testing and the experimental testing of components and systems. It thus creates the prerequisites for the implementation and adaptation of legal framework conditions as well as for standardization. Closely related to this are investigations into the properties and compatibility of metallic materials and polymers as well as friction systems for the safe operation of components, plants, processes, and systems. This interdisciplinary and holistic approach is complemented by the development of gas analytics and suitable sensors, online measurements of gas properties in process control, test scenarios under real conditions as well as impact assessments of accidents and risk assessments and risk management systems derived from them. With its portfolio of tasks and competences, BAM builds trust in the safety and reliability of technical system solutions for hydrogen.
The application of compact NMR instruments to continuously flowing samples at higher temperatures or exothermically reacting mixtures is limited by the temperature sensitivity of permanent magnets. Typically, such temperature effects directly influence the achievable magnetic field homogeneity and hence measurement quality. The internal-temperature control loop of the magnet and instruments is typically not designed for compensation of such effects. Passive insulation is restricted by the small dimensions within the magnet borehole.
Here, we present a design approach for active heat shielding with the aim of variable temperature control of NMR samples for benchtop NMR instruments using a compressed airstream which is variable in flow and temperature. Based on the system identification and surface temperature measurements using an optical thermo-graphy setup, a model predictive control was set up to minimize any disturbance effect on the permanent magnet from the probe or sample temperature.
This methodology will facilitate the application of variable-temperature shielding and, therefore, potentially extend the application of compact NMR instruments to flowing samples at higher temperature than the permanent magnet.
The application of compact NMR instruments to hot flowing samples or exothermically reacting mixtures is limited by the temperature sensitivity of permanent magnets. Typically, such temperature effects directly influence the achievable magnetic field homogeneity and hence measurement quality. The internal-temperature control loop of the magnet and instruments is not designed for such temperature compensation. Passive insulation is restricted by the small dimensions within the magnet borehole.
Here, we present a design approach for active heat shielding with the aim of variable temperature control of NMR samples for benchtop NMR instruments using a compressed airstream which is variable in flow and temperature. Based on the system identification and surface temperature measurements through thermography, a model predictive control was set up to minimise any disturbance effect on the permanent magnet from the probe or sample temperature.
This methodology will facilitate the application of variable-temperature shielding and, therefore, extend the application of compact NMR instruments to flowing sample temperatures that differ from the magnet temperature.
The application of compact NMR instruments to hot flowing samples or exothermically reacting mixtures is limited by the temperature sensitivity of permanent magnets. Typically, such temperature effects directly influence the achievable magnetic field homogeneity and hence measurement quality. The internal-temperature control loop of the magnet and instruments is not designed for such temperature compensation. Passive insulation is restricted by the small dimensions within the magnet borehole. Here, we present a design approach for active heat shielding with the aim of variable temperature control of NMR samples for benchtop NMR instruments using a compressed airstream which is variable in flow and temperature. Based on the system identification and surface temperature measurements through thermography, a model predictive control was set up to minimise any disturbance effect on the permanent magnet from the probe or sample temperature. This methodology will facilitate the application of variable-temperature shielding and, therefore, extend the application of compact NMR instruments to flowing sample temperatures that differ from the magnet temperature.
Nonporous corundum powder was functionalized covalently with protein binders to isolate and enrich specific proteins from complex matrices.The common crosslinker glutaraldehyde was compared with a hyperbranched polyglycerol (PG) of around 10 kDa. The latter was oxidized with periodate to generate aldehyde groups that can covalently react with the amines of the surface and the amino groups from the protein via a reductive amination process. The amount of bound protein was quantified via aromatic amino acid analysis (AAAA). As a proof of concept, IgG was extracted with protein A from crude human plasma. The advantages of corundum include the very low price, extremely high physical and chemical stability, pressure resistance, favorable binding kinetics, convenient handling, and flexible application.
The competitiveness of the chemical and pharmaceutical industry is based on ensuring the required product quality while making optimum use of plants, raw materials, and energy. In this context, effective process control using reliable chemical process analytics secures global competitiveness. The setup of those control strategies often originate in process development but need to be transferable along the whole product life cycle. In this series of two contributions, we want to present a combined view on the future of PAT (process analytical technology), which is projected in smart labs (part 1) and smart sensors (part 2). In laboratories and pilot plants, offline chemical analytical methods are frequently used, where inline methods are also used in production. Here, a transferability from process development to the process in operation would be desirable. This can be obtained by establishing PAT methods for production already during process development or scale-up. However, the current PAT (Bakeev 2005, Org Process Res 19:3–62; Simon et al. 2015, Org Process Res Dev 19:3–62) must become more flexible and smarter. This can be achieved by introducing digitalization-based knowledge management, so that knowledge from product development enables and accelerates the integration of PAT. Conversely, knowledge from the production process will also contribute to product and process development. This contribution describes the future role of the laboratory and develops requirements therefrom. In part 2, we examine the future functionality as well as the ingredients of a smart sensor aiming to eventually fuel full PAT functionality—also within process development or scale-up facilities (Eifert et al. 2020, Anal Bioanal Chem).
Current and future requirements to industrial analytical infrastructure—part 2: smart sensors
(2020)
Complex processes meet and need Industry 4.0 capabilities. Shorter product cycles, flexible production needs, and direct assessment of product quality attributes and raw material attributes call for an increased need of new process analytical technologies (PAT) concepts. While individual PAT tools may be available since decades, we need holistic concepts to fulfill above industrial needs. In this series of two contributions, we want to present a combined view on the future of PAT (process analytical technology), which is projected in smart labs (Part 1) and smart sensors (Part 2). Part 2 of this feature article series describes the future functionality as well as the ingredients of a smart sensor aiming to eventually fuel full PAT functionality. The smart sensor consists of (i) chemical and process information in the physical twin by smart field devices, by measuring multiple components, and is fully connected in the IIoT 4.0 environment. In addition, (ii) it includes process intelligence in the digital twin, as to being able to generate knowledge from multi-sensor and multi-dimensional data. The cyber-physical system (CPS) combines both elements mentioned above and allows the smart sensor to be self-calibrating and self-optimizing. It maintains its operation autonomously. Furthermore, it allows—as central PAT enabler—a flexible but also target-oriented predictive control strategy and efficient process development and can compensate variations of the process and raw material attributes. Future cyber-physical production systems—like smart sensors—consist of the fusion of two main pillars, the physical and the digital twins. We discuss the individual elements of both pillars, such as connectivity, and chemical analytics on the one hand as well as hybrid models and knowledge workflows on the other. Finally, we discuss its integration needs in a CPS in order to allow is versatile deployment in efficient process development and advanced optimum predictive process control.
Von der zunehmenden Digitalisierung sind alle Bereiche von Wirtschaft, Industrie und Gesellschaft betroffen. Die Digitalisierung führt zu einer verstärkten virtuellen Nutzung von Ressourcen und zu einer hochdynamischen Entwicklung der zugehörigen Forschungs- und Technologiefelder. Neue Technologien sind die Basis für die erfolgreiche Weiterentwicklung des Wirtschaftsstandortes Deutschland und für eine Wertschöpfung in globalen Märkten. Die nachhaltige Sicherheit neuer Technologien schafft das Vertrauen der Bürger in den Wandel und sichert unsere Zukunft.
Im Vortrag werden Anforderungen und Lösungsvorschläge für das Labor der Zukunft diskutiert. Industrie 4.0 bzw. das Labor 4.0 hilft uns, komplexere Prozesse schneller umzusetzen. Entwicklung von Anlagen und Prozessen beginnt im Labor 4.0. Dazu werden offene, nicht proprietäre Schnittstellen und Standards bei Laborgeräten und Feldgeräten dringend benötigt. Der Standard OPC-UA wird derzeit als gesetzt gesehen. Als nächstes ist die Festlegung der Semantik (Companion Specification) erfordert. Als Bitte an die Zulieferer und Geräterhersteller wird gerichtet, möglichst keine Alleingänge hinsichtlich Schnittstellen, Standards oder GUI zu unternehmen, sondern diese mit den Anwendern abzustimmen. Auf diese Weise ergibt sich das in Industrie 4.0 geforderte "durchgehende Engineering“.
Die Sicherung der Wettbewerbsfähigkeit des Standorts Deutschland/Europa ergibt sich dann gleich in doppelter Hinsicht: Sichere, verfügbare und effiziente Herstellung international wettbewerbsfähiger Produkte für die Anwender sowie weltweiter Export von Mess- und Regeltechnik für die Messtechnikbranche.
There have been an increasing number of publications on flow chemistry applications of compact NMR. Despite this, there is so far no comprehensive workflow for the technical design of flow cells. Here, we present an approach that is suitable for the design of an NMR flow cell with an integrated static mixing unit. This design moves the mixing of reactants to the active NMR detection region within the NMR instrument, presenting a feature that analyses chemical reactions faster (5–120 s region) than other common setups. During the design phase, the targeted mixing homogeneity of the components was evaluated for different types of mixing units based on CFD simulation. Subsequently, the flow cell was additively manufactured from ceramic material and metal tubing. Within the targeted working mass flow range, excellent mixing properties as well as narrow line widths were confirmed in validation experiments, comparable to common glass tubes.
Intensified continuous processes are in focus of current research. Compared to traditional batch processes, intensified continuous production allows the synthesis of new and difficult producible 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 project1,2).
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.
Knowing the exact nutrient composition of organic fertilizers is a prerequisite for their appropriate application to improve yield and to avoid environmental pollution by over-fertilization.
Traditional standard chemical analysis is cost and time-consuming and thus it is unsuitable for a rapid analysis before manure application. As a possible alternative, a handheld X-ray fluorescence (XRF) spectrometer was tested to enable a fast, simultaneous, and on-site analysis of several elements.
A set of 62 liquid pig and cattle manures as well as biogas digestates were collected, intensively homogenized and analysed for the macro plant nutrients phosphorus, potassium, magnesium, calcium, and sulphur as well as the micro nutrients manganese, iron, copper, and zinc using the standard lab procedure. The effect of four different sample preparation steps (original, dried, filtered, and dried filter residues) on XRF measurement accuracy was examined. Therefore, XRF results were correlated with values of the reference analysis. The best R2 s for each element ranged from 0.64 to 0.92. Comparing the four preparation steps, XRF results for dried samples showed good correlations (0.64 and 0.86) for all elements. XRF measurements using dried filter residues showed also good correlations with R2 s between 0.65 and 0.91 except for P, Mg, and Ca. In contrast, correlation Analysis for liquid samples (original and filtered) resulted in lower R2 s from 0.02 to 0.68, except for K (0.83 and 0.87, respectively). Based on these results, it can be concluded that handheld XRF is a promising measuring system for element analysis in manures and digestates.
In respect of an efficient cultivation of agricultural cropland, a site-specific fertility management is necessary. Therefore, affordable and extensive mapping methods are needed. The research projects I4S (intelligence for soil) has the goal to develop a system for this purpose. I4S is one of ten interdisciplinary research project associations of the innovation programme called BonaRes, which is funded by the German Federal Ministry of Education and Research (BMBF).
The system includes a sensor platform, which contains different sensors, like XRF, VIS-NIR, Gamma and LIBS. The main task of LIBS measurements in this project is the real-time determination of the elemental contents of major and minor nutrients in soils, like calcium, magnesium, potassium. LIBS (laser-induced breakdown spectroscopy) is known as a fast and simultaneous multi-element analysis with little or no sample preparation. The main task of LIBS measurements in this project is the real-time determination of the elemental contents of nutrients in soils, like calcium, magnesium, potassium. For this purpose, a special setup has been designed. The sample uptake operates with the help of a rotatable sample plate which circulates with different velocities to simulate the application on the field. To provide a higher intensity and a better reproducibility of the obtained signal, a double-pulse Nd:YAG laser (1064 nm)was used. In order to minimize dust formation from the soil during the operation of the laser, a dust removal by suction has been integrated. When using relative methods such as LIBS, a suitable calibration curve is needed for absolute quantification. The complex matrix of soils, as well as the influence of moisture and grain size in soils makes the absolute quantification by LIBS challenging. To overcome these influences, chemometric methods were used. With the principal component analysis (PCA) a classification of soils into different soil types was performed and a calibration curve based on partial least squares regression (PLSR) was generated. With this calibration model’s elemental distribution maps for different German agricultural fields were created.
In respect of an efficient cultivation of agricultural cropland, a site-specific fertility management is necessary. Therefore, affordable and extensive mapping methods are needed. For this purpose, the research project I4S (intelligence for soil) has the goal to develop an integrated system. This system includes a sensor platform, which contains different sensors, like XRF, VIS-NIR, Gamma and LIBS.
LIBS (laser-induced breakdown spectroscopy) is known as a fast and simultaneous multi-element analysis with little or no sample preparation. The main task of LIBS measurements in this project is the real time determination of the elemental contents of nutrients in soils, like calcium, magnesium, potassium. For this purpose, a special setup has been designed. The sample uptake operates with the help of a rotatable sample plate which circulates with different velocities to simulate the application on the field. To provide a higher intensity and a better reproducibility of the obtained signal, a double-pulse Nd:YAG laser (1064 nm) was used. In order to minimize dust formation from the soil during the operation of the laser, a dust removal by suction has been integrated.[1] When using relative methods such as LIBS, a suitable calibration curve is needed for absolute quantification. The complex matrix of soils, as well as the influence of moisture and grain size in soils makes the absolute quantification by LIBS challenging. To overcome these influences, chemometric methods were used. With the principal component analysis (PCA) a classification of soils into different soil types was performed and a calibration curve based on partial least squares regression (PLSR) was generated. With this calibration model’s elemental distribution maps for different German agricultural fields were created.
Near-infrared (NIR) spectroscopy is a promising candidate for low-cost, nondestructive, and high-throughput mass quantification of micro¬plastics in environmental samples. Widespread application of the technique is currently hampered mainly by the low sensitivity of NIR spectroscopy compared to thermo-analytical approaches commonly used for this type of analysis. This study shows how the application of NIR spectroscopy for mass quantification of microplastics can be extended to smaller analyte levels by combining it with a simple and rapid microplastic enrichment protocol. For this purpose, the widely used flotation of microplastics in a NaCl solution, accelerated by centrifugation, was chosen which allowed to remove up to 99 % of the matrix at recovery rates of 83–104 %. The spectroscopic measurements took place directly on the stainless-steel filters used to collect the extracted particles to reduce sample handling to a minimum. Partial least squares regression (PLSR) models were used to identify and quantify the extracted microplastics in the mass range of 1–10 mg. The simple and fast extraction procedure was systematically optimized to meet the requirements for the quantification of microplastics from common PE-, PP-, and PS-based packaging materials with a particle size < 1 mm found in compost or soils with high natural organic matter content (> 10 % determined by loss on ignition). Microplastics could be detected in model samples at a mass fraction of 1 mg g-1. The detectable microplastic mass fraction is about an order of magnitude lower compared to previous studies using NIR spectroscopy without additional enrichment. To emphasize the cost-effectiveness of the method, it was implemented using some of the cheapest and most compact NIR spectrometers available.
Near-infrared (NIR) spectroscopy is a promising candidate for low-cost, nondestructive, and highthroughput mass quantification of microplastics in environmental samples. Widespread application of the technique is currently hampered mainly by the low sensitivity of NIR spectroscopy compared to thermoanalytical approaches commonly used for this type of analysis. This study shows how the application of NIR spectroscopy for mass quantification of microplastics can be extended to smaller analyte levels by combining it with a simple and rapid microplastic enrichment protocol. For this purpose, the widely used flotation of microplastics in a NaCl solution, accelerated by centrifugation, was chosen which allowed to remove up to 99 % of the matrix at recovery rates of 83–104 %. The spectroscopic measurements took place directly on the stainless-steel filters used to collect the extracted particles to reduce sample handling to a minimum. Partial least squares regression (PLSR) models were used to identify and quantify the extracted microplastics in the mass range of 1–10 mg.
The simple and fast extraction procedure was systematically optimized to meet the requirements for the quantification of microplastics from common PE-, PP-, and PS-based packaging materials with a particle size < 1 mm found in compost or soils with high natural organic matter content (> 10 % determined by loss on ignition). Microplastics could be detected in model samples at a mass fraction of 1 mg g-1. The detectable microplastic mass fraction is about an order of magnitude lower compared to previous studies using NIR spectroscopy without additional enrichment. To emphasize the cost-effectiveness of the method, it was implemented using some of the cheapest and most
compact NIR spectrometers available.
As part of the BonaRes research initiative, funded by the BMBF, strategies are being developed to use soil as a sustainable resource in the bio economy. The interdisciplinary subproject I4S - “Intelligence for soil” - is responsible for the development of an integrated system for site-specific management of soil fertility. For this purpose, a platform is constructed and various sensors are installed. Real-time data will be summarised in models and decision-making algorithms will be used to control fertilisation and accordingly improve soil functions. Aim of the BAM in the frame of I4S is the characterisation of an energy-dispersive X-ray fluorescence (EDXRF) based sensor for robust determination of plant essential nutrients in soil.
First a principal component analysis (PCA) was used to identify outliers and to observe the largest variance within the German soil samples. It could be monitored that splitting of the samples was due to their iron content. Given that clay samples contain high amounts of iron and sandy samples low amounts, a classification of the samples by their soil texture according to VD LUFA was possible. Considering the complex composition of soil, a matrix-specific calibration was carried out by univariate and multivariate data analysis. The figures of merit demonstrated that a more robust calibration model with negligible matrix effects can be obtained by a multivariate approach using partial least squares regression (PLSR). A better correlation between predicted values compared to reference values for German soil samples was observed for the chemometric calibration model than for the univariate one.
Different factors can affect the received calibration models such as moisture and particle size distribution which is especially important due to later online analysis.
In first studies the influence of moisture on the detection of plant essential nutrients was investigated. With increasing water content, the characteristic fluorescence peaks decrease and start to increase again at a water content of 15 %. With lower moisture content the soil agglomerates which leads to lower packing of the sample, resulting in a rougher surface which negatively influence the signals. Whereas, agglomerates are not formed at higher water content. This allows the sample to be packed more tightly thus a smoother surface and a better homogeneity is obtained.
Furthermore, particle size distribution leads to significantly higher uncertainties and lower signals when comparing grounded (< 500 μm) and not grounded (< 2 mm) samples. This can be explained by amplifying of the already known inhomogeneity of soils.
Both factors must be included in the chemometric PLSR to obtain robust calibration models for each macro and micro nutrient.
Development of a robust calibration model for determination of nutrients in soils using EDXRF
(2018)
As part of the BonaRes research initiative, funded by the BMBF, strategies are being developed to use soil as a sustainable resource in the bio economy. The interdisciplinary subproject I4S - “Intelligence for soil” - is responsible for the development of an integrated system for site-specific management of soil fertility. For this purpose, a platform is constructed and various sensors are installed. Real-time data will be summarised in models and decision-making algorithms will be used to control fertilisation and accordingly improve soil functions. Aim of the BAM in the frame of I4S is the characterisation of an energy-dispersive X-ray fluorescence (EDXRF) based sensor for robust determination of plant essential nutrients in soil.
First a principal component analysis (PCA) was used to identify outliers and to observe the largest variance within the German soil samples. It could be monitored that splitting of the samples was due to their iron content. Given that clay samples contain high amounts of iron and sandy samples low amounts, a classification of the samples by their soil texture according to VD LUFA was possible. Considering the complex composition of soil, a matrix-specific calibration was carried out by univariate and multivariate data analysis. The figures of merit demonstrated that a more robust calibration model with negligible matrix effects can be obtained by a multivariate approach using partial least squares regression (PLSR). A better correlation between predicted values compared to reference values for German soil samples was observed for the chemometric calibration model than for the univariate one.
Different factors can affect the received calibration models such as moisture and particle size distribution which is especially important due to later online Analysis.
In first studies the influence of moisture on the detection of plant essential nutrients was investigated. With increasing water content, the characteristic fluorescence peaks decrease and start to increase again at a water content of 15 %. With lower moisture content the soil agglomerates which leads to lower packing of the sample, resulting in a rougher surface which negatively influence the signals. Whereas, agglomerates are not formed at higher water content. This allows the sample to be packed more tightly thus a smoother surface and a better homogeneity is obtained.
Furthermore, particle size distribution leads to significantly higher uncertainties and lower signals when comparing grounded (< 500 μm) and not grounded (< 2 mm) samples. This can be explained by amplifying of the already known inhomogeneity of soils. Both factors must be included in the chemometric PLSR to obtain robust calibration models for each macro and micro nutrient.
The project “Intelligence for Soil” (I4S) aims at the design of an integrated system for improvement of soil functions and fertilizer recommendations. This system is composed by different sensors that will provide a detailed assessment of soil properties and processes, which are prerequisites for a site-specific, resource-saving and ecofriendly soil management, considering the soil as a sustainable resource for the bioeconomy. One of these sensors will be an energy-dispersive X-Ray Fluorescence spectrometer. It is a non-destructive technique suitable for in-situ measurements due to a minimum sample preparation and it allows fast multielement analyses. In this work, an automatic system has been developed using a handheld equipment from Olympus (Vanta C series). A polypropylene (PP) film was used to protect the measuring window of the device from dust and possible cross-contamination. To control the stepper motor that unrolls the PP film, a microcontroller was used to ensure that a piece of clean PP is in front of the measuring window for each new analysis.
Preliminary calibration studies using pre-defined methods, Geochem and Soil Methods, were performed with the following Certified Reference Materials (CRMs): NRC Till 1-3, NIST 2710, BAM U110, ERM CC141, BCR 142R, IAEA Soil 7. The CRMs were measured 10 times at different spots. The spot size was 10 mm in diameter, irradiation time was 60 ? s. The averaged data from X ray characteristic emission line intensities for Al, P, Si, Ca, Fe, Mn, Zn, Cu and Ni Kα were in close agreement with certified mass fraction data. The linear correlation coefficients (r) ranged from 0.852 for P to 0.999 for Mn. A second round of calibration studies were performed with the following CRMs: GBW07402, GBW 07405, NCS DC 73023, NCS DC 73030, NCS DC 85109, NCS DC 87104, NIST 1646a, NIST 2704, NIST 2710, NRC Till 1-3, VS 2498-83, and the same pre-defined methods and experimental parameters were employed. The results of the 13 CRMs were now compared with the values obtained by a validated WDXRF method. The linear correlation coefficients (r) ranged from 0.998 for Ca to 0.999 for Zn. Other elements such as S, K and Ti can also be properly determined, but validation still requires more robust calibration models.
Further calibration studies will be performed in order to circumvent matrix effects and to guarantee reliable results. Besides that, the automatic system will be placed on a mobile sensor platform and the system will be tested in the field. A guidance for on site-specific fertilization integrating the results obtained from different sensors placed on the platform is expected.
The need for sustainable and effective usage of limited resources like water and soil becomes more and more important due to increasing populations and their need for food. The research initiative BonaRes funded by the Federal Ministry of Education and Research, develops strategies to use soil as a sustainable resource for the bio-economy. One part of this initiative is the Intelligence for soil (I4S) project. It deals with the development of an integrated system for the site-specific soil fertility management. The goal of the project is to establish a mobile sensor platform for soil mapping as well as to apply soil and crop models to deal with the high amount of produced data. These models are going to be used to formulate recommendations on the use of fertilizers and to gain an improvement on the soil fertility. Using these information’s, the environmental pollution with substances like nitrate contained in fertilizers can be decreased. Furthermore, the costs for farmers can be decreased if only the amounts of fertilizer actually needed are applied to the field.
The task for the Federal Institute for Materials Research and Testing (BAM) is the installation and optimization of a handheld XRF sensor for the online measurement directly on the field. It is a non-destructive technique and quite suitable for the online approach due to a low maintenance in the sample preparation and a fast and simultaneous multi element analysis. The elements of interest in this case are light elements like phosphorus, calcium and potassium. One of the major tasks is the calibration of the sensor for different soils, therefore, calibration models are evolved to deal with different types of soil. Due to the multi element analysis, micro and macro nutrients can be determined simultaneously. Those differ a lot in their contents which makes it inevitable to optimize and adapt the measuring procedure. By analyzing soil, its always important to deal with matrix effects, that is why the influence of different disturbances like the moisturize content and the grain size needs to be investigated.
The XRF is only one of many sensors that combined give customized fertilizing recommendations in the end.
Volatile organic compounds (VOCs) emitted by furniture and building materials can cause health issues. For an improvement of indoor air quality low emitting materials should be used. Quality assurance and –control (QA/QC) measures require an emission reference material (ERM) with a predictable emission rate of VOCs. The idea is to use porous materials as ERM, which store the VOCs inside their pores and emit them constantly.
Um in einem veränderten Umfeld erfolgreich bestehen zu können, müssen Chemie-unternehmen neue Pfade beschreiten. Dazu gehört insbesondere das Potential digi-taler Technologien. Mit flexiblen, modularen chemischen Vielzweck-Produktionsanlagen lassen sich häufig wechselnde Produkte mit kürzeren Vorlauf- und Stillstandzeiten zwischen den Kampagnen und dennoch hoher Qualität realisie-ren. Intensivierte, kontinuierliche Produktionsanlagen erlauben auch den Umgang mit schwierig zu handhabenden Substanzen.
Grundvoraussetzung für solche Konzepte ist eine hochautomatisierte "chemische" Prozesskontrolle zusammen mit Echtzeit-Qualitätskotrolle, die "chemische" Informati-onen über den Prozess bereitstellt. In einem Anwendungsbeispiel wurde eine phar-mazeutische Lithiierungsreaktion aus einer modularen Pilot-Anlage betrachtet und dabei die Vorzüge eines vollautomatischen NMR-Sensors untersucht. Dazu wurde ein kommerziell erhältliches Benchtop-NMR-Spektrometer mit Permanentmagnet auf die industriellen Anforderungen, wie Explosionsschutz, Feldkommunikation und voll-automatischer, robuster Datenauswertung angepasst. Der NMR-Sensor konnte schließlich erfolgreich im vollautomatischen Betrieb nach fortschrittlichen Regelkon-zepten und für die Echtzeitoptimierung der Anlage getestet werden. Die NMR-Spektroskopie erwies sich als hervorragende Online-Methode und konnte zusammen mit einer modularen Datenauswertung sehr flexibel genutzt werden. Die Methode konnte überdies als zuverlässige Referenzmethode zur Kalibrierung konventioneller Online-Analytik eingesetzt werden.
Zukünftig werden voll integrierte und intelligent vernetzte "smarte" Sensoren und Pro-zesse eine kontinuierliche Produktion von Chemikalien und Pharmazeutika mit ver-tretbaren Qualitätskosten möglich machen.