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Eingeladener Vortrag
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X-ray back scatter imaging is rarely applied compared to classical X-ray projection imaging. 20 years ago the company Philips developed “COMSCAN”, a first application case in the aircraft industry, which allowed even a depth resolution using back scatter imaging. The company AS&E in Boston offers back scatter imaging solutions for the security market. Their principle is to scan the object with a highly collimated X-ray needle beam from one side only and detect the backscattered radiation by a large area detector side by side with the collimation wheel. A new prototype is investigated at BAM for application and optimization in non-destructive testing. As modern industrial application field in-situ testing in additive manufacturing is targeted. The accessibility of the printed part during the production process is very limited. This prevent the application of a two sided X-ray inspection or Computed Tomography, were an rotation of the object is required to acquire projections from 360 degrees. An important advantage for the X-ray back scatter technique are also the materials used in additive manufacturing (polymers, ceramics, light metals like Aluminum or Titanium). These materials with lower density and lower Z values give better scatter signals than metals with higher densities and Z values. The back scatter intensity decreases with increasing density and Z value of the material. But the requirements on spatial resolution and contrast sensitivity are more stringent for non-destructive testing of additive manufactured parts compared to the security area. In NDT sizes of indications smaller than 1 mm have to be detected clearly. The investigation of these limits on a stateof-the-art prototype for X-ray back scattering using rotating collimated X-ray needle beams is a part of the BAM project “ProMoAM”. The contribution shows first results of the optimization for NDT and the achieved application limits for several example cases.
The WelDX research project aims to foster the exchange of scientific data inside the welding community by developing and establishing a new open source file format suitable for documentation of experimental welding data and upholding associated quality standards. In addition to fostering scientific collaboration inside the national and international welding community an associated advisory committee will be established to oversee the future development of the file format. The proposed file format will be developed with regards to current needs of the community regarding interoperability, data quality and performance and will be published under an appropriate open source license.
By using the file format objectivity, comparability and reproducibility across different institutes and experimental setups can be improved.
The talk reflects how PAT could be applied in future developments ofpharma manufacturing. It shows the benefits, increase quality, size and increasing speed of production with significant reduction of quality costs, which are possible. Using Smart Sensors and model based data evaluation methods are the key to reduce set-up times and costs. Industry 4.0 will help shape the Pharmaceutical industry of tomorrow. This is demonstrated by an example using modular production units for Continuous Manufacturing. The development of a smart online NMR analyser is shown.
In the field of non-destructive testing (NDT) in civil engineering, a large number of measurement data are collected. Although they serve as a basis for scientific analyses, there is still no uniform representation of the data. An analysis of various distributed data sets across different test objects is therefore only possible with high manual effort.
We present a system architecture for an integrated data management of distributed data sets based on Semantic Web technologies. The approach is essentially based on a mathematical model - the so-called ontology - which represents the knowledge of our domain NDT. The ontology developed by us is linked to data sources and thus describes the semantic meaning of the data. Furthermore, the ontology acts as a central concept for database access. Non-domain data sources can be easily integrated by linking them to the NDT construction ontology and are directly available for generic use in the sense of digitization. Based on an extensive literature research, we outline the possibilities that this offers for NDT in civil engineering, such as computer-aided sorting, analysis, recognition and explanation of relationships (explainable AI) for several million measurement data.
The expected benefits of this approach of knowledge representation and data access for the NDT community are an expansion of knowledge through data exchange in research (interoperability), the scientific exploitation of large existing data sources with data-based methods (such as image recognition, measurement uncertainty calculations, factor analysis, material characterization) and finally a simplified exchange of NDT data with engineering models and thus with the construction industry.
Ontologies are already the core of numerous intelligent systems such as building information modeling or research databases. This contribution gives an overview of the range of tools we are currently creating to communicate with them.
Ugly Spectra and Lousy interfaces – Challenges for Compact NMR Spectroscopy in Process Control
(2016)
With the introduction of advanced process analytical technology, the closeness of key process variables to their limits can be directly controlled and the product can be classified or even released in real time.
Compact NMR instruments can make NMR spectroscopy accessible in industrial and harsh environments for process control.
Trash to treasure: recovery of transition metal phosphates for (electro-)catalytical applications
(2022)
Wastewaters containing high concentrations of NH4+, PO43- and transition metals are environmentally harmful and toxic pollutants. At the same time phosphorous and transition metals constitute valuable resources. Here, we report the synthesis routes for Co- and Ni-struvites (NH4MPO4∙6H2O, M = Ni2+, Co2+) out of aqueous solutions resembling synthetic/industrial waste water compositions, and allowing for P, ammonia and metal co-precipitation. Furthermore, the as-obtained struvites were further up-cycled. When heated, these transition metal phosphates (TMPs) demonstrate significant changes in the degree of crystallinity/coordination environment involving a high amount of amorphous phases and importantly develop mesoporosity (Figure 1). In this regard, amorphous and mesoporous TMPs are known to be highly promising (electro-)catalysts.
Amorphous phases do not represent a simple “disordered” crystal but more a complex system with a broad range of compositions and physicochemical properties, which remain mostly unknown. Consequently, we investigated the recrystallization and amorphization process during thermal treatment and a resolved the complex amorphous/crystalline structures (Figure 2). As a proof-of-principle for their applicational use, the as-obtained TMPs demonstrate significant proton conductivity properties similar to apatite-like structures from room to high temperatures (>800°C).
Hence, we have developed a promising recycling route in which environmental harmful contaminants like PO43-, NH4+ and 3d metals would be extracted out of waste waters in the form of precursor raw materials. These raw materials can be then further up-cycled through a simple thermal treatment for their specific application in electrocatalysis.
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 and gives also an overview on direct dissolution studies of API cocrystals.
The departure from the current automation landscape to next generation automation concepts for the process industry has already begun. Smart functions of sensors will simplify their use and enable plug-and-play integration, even though they may appear to be more complex at first sight. This is particularly important for concepts like self-diagnostics, self-calibration and self-configuration/parameterization. Intelligent field devices as parts of digital field networks, Inter-net Protocol (IP)-based connectivity and web interfaces, as well as advanced data analysis soft-ware will provide the basis for future projects like Industrie 4.0, Factory of the Future, or Industrial Internet of Things (IIoT). The talk summarizes the currently discussed general requirements for process sensors 4.0 and introduces an online NMR sensor as example. This sensor was developed to provide integrated control and sensing for sustainable operation of flexible intensified processes (CONSENS) funded by the European Union’s Horizon 2020 research and innovation programme.
Simple calibration concept of an online NMR module demonstrated in a modularised production plant
(2017)
Monitoring specific information (such as physico-chemical properties, chemical re-actions, etc.) is the key to chemical process control. Within the CONSENS Project, the challenge to adapt a commercially available benchtop NMR spectrometer to the full requirements of an automated chemical production environment was tack-led. The developed online NMR module was provided in an explosion proof housing and involves a compact 43.5 MHz NMR spectrometer together with an acquisition unit, a programmable logic controller for automated triggering, flow con-trol, as well as data communication.
First results of an aromatic coupling reaction in lab scale showed a general feasibil-ity according to the signal information in the acquired NMR spectra even though with a considerable overlap. Due to the comparatively low field strength of the NMR spectrometer multivariate methods had to be considered for the prediction of con-centration profiles based on spectral data. Typically, for industrial application of those methods, e.g. Partial Least Squares Regression (PLS-R) as well as Indirect Hard Modeling, large amount of calibration data is demanded, which must be ac-quired in time consuming lab-scale experiments and offline analytics. When it comes to changes in raw materials (e.g., varying functional groups, additional stabi-lizing agents) calibration experiments and data evaluation models are developed again. Here we present an approach of automated data analysis tools for low field NMR spectra with minimal calibration effort. The algorithms are based on Indirect Hard Modeling, whereby each component in each mixture spectra can be rep-resented by several flexible peak functions (pure component models). This means, that only pure component NMR spectra are needed to generate a first evaluation model. The flexibility of peak functions in the spectral model can be adjusted via constraints of peak parameters. The area of any pure component model can either be converted to concentrations based on a one-point calibration on raw material concentration or even neat solvent signals. In several cases it has been shown, the IHM works almost independently of the matrix of the real samples. Such a calibration can be repeated daily in the beginning of each process run with minimal time effort. Moreover, additional pure components can be added to the model or even substitut-ed while keeping the previously adjusted peak function constraints. The proposed method exhibited good agreement of resulting concentration data from low field NMR spectra, when compared to an online high field NMR spectrometer as refer-ence instrument.
Smart functions of sensors simplify their use and enable plug-and-play, even though they are more complex. This is particularly important for, self-diagnostics, self-calibration and self-configuration/parameterization. Intelligent field devices, digital field networks, Internet Protocol (IP)-enabled connectivity and web services, historians, and advanced data analysis software are providing the basis for the future project “Industrie 4.0” and Industrial Internet of Things (IIoT).
Important smart features include connectivity and communication ability according to a unified protocol (OPC-UA currently most widely discussed), maintenance and operating functions, traceability and compliance, virtual description to support a continuous engineering, and well as interaction capabilities between sensors. This is a prerequisite for the realization of Cyber Physical Systems (CPS) within these future automation concepts for the process industry. Therefore, smart process sensors enable new business models for users, device manufacturers, and service providers.
The departure from current automation to smart sensor has already begun. Further development is based on the actual situation over several steps. Possible perspectives will be via additional communication channels to mobile devices, bidirectional communication, integration of the cloud and virtualization. The integration of virtual runtime environments can provide a more flexible topology for process control environments.
The talk summarizes the currently discussed requirements to process sensors 4.0 and introduces an online NMR sensor as an example, which was developed in the EU project CONSENS.