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The SUSAN-project aimed at developing a sustainable and safe strategy for nutrient recovery from sewage sludges using thermal treatment. Mono-incineration of the sludges completely destructs the organic pollutants in a first step. The incineration residues are ashes with a high phosphorus content that still contain heavy metal compounds above the limits for agricultural use. Furthermore, phosphorus in the ashes exhibits low bioavailability - a disadvantage in farming. Therefore, in a second thermochemical step heavy metals are removed and phosphorus transferred into mineral phases available for plants. The principle of the SUSAN-project is the separation of the small heavy metals fraction from the ashes to receive a phosphorus rich product suited as raw material for fertilisers that also contains SiO2, CaO, Al2O3, Fe2O3, MgO and K2O.
The thermochemical process is based on evaporation of volatile heavy metal chlorides at 850 - 1000 °C. The sewage sludge ashes are mixed with chlorine donors such as MgCl2 or KCl, grained and treated in a thermal aggregate e.g. a rotary furnace. At the same time a transfer of non soluble P-compounds into Mineral phases available for plants takes place.
Besides technological development of the thermochemical process, the products fertilisation performance, the product design, the market for the products and the sustainability of the whole process chain are emphasised in the SUSAN-project.
In dem Projekt soll die vollständige Wiederverwertbarkeit der Schlacke und ihrer Bestandteile demonstriert werden. Dazu gehören die vollständige Rückgewinnung der metallischen Phase, die in die Edelstahlherstellung zurückgeführt werden kann, sowie die Aufwertung der mineralischen Fraktion, die ihre uneingeschränkte Verwendung als Zementzumahlstoff oder mineralischer Baustoff erlaubt. Bei geeigneter Prozessführung des elektrischen Lichtbogenofens können durch „reduzierendes Schmelzen“ die chromhaltigen Wertstoffe zusammen mit anderen Schwermetallen zu 97% in einer Metalllegierung angereichert und separiert werden. Diese kann als Rohstoff in der Metallurgie eingesetzt werden. Der Chromgehalt der mineralischen Komponte der Schlacke konnte von ca. 3% auf unter 0,1% verringert werden. Die mineralische Fraktion lässt sich durch entsprechende Zuschläge während des Schmelzbetriebes und eine anschließende Granulation der schmelzflüssigen Phase zu einem Material aufwerten, das in seiner Zusammensetzung und seinen Eigenschaften typischen Zementkomponenten entspricht und als Rohstoff in der Zementproduktion eingesetzt werden kann. Dafür müssen allerdings noch die rechtlichen Voraussetzungen geschaffen werden, da der Rohstoffeinsatz gerade im Zement strengen Auflagen unterliegt und die Zemente bestimmte Normen erfüllen müssen.
Durch die in RECARC aufgezeigte Möglichkeit der Wiederverwertung können somit Stoffkreisläufe geschlossen werden, die ansonsten zu einem Verlust wertvoller Rohstoffe führen. Vor allem die steigenden Rohstoffkosten, aber auch die immer stärker limitierten CO2-Emissionen zeigen die hohe Relevanz im Umgang mit metallurgischen Reststoffen. Eine Überführung des in RECARC demonstrierten Verfahrens in die Industrie könnte einen wichtigen Beitrag zur nachhaltigen Schonung der natürlichen Ressourcen und der Umwelt leisten.
Learned block iterative shrinkage thresholding algorithm for photothermal super resolution imaging
(2020)
Block-sparse regularization is already well-known in active thermal imaging and is used for multiple measurement based inverse problems. The main bottleneck of this method is the choice of regularization parameters which differs for each experiment. To avoid time-consuming manually selected regularization parameters, we propose a learned block-sparse optimization approach using an iterative algorithm unfolded into a deep neural network. More precisely, we show the benefits of using a learned block iterative shrinkage thresholding algorithm that is able to learn the choice of regularization parameters. In addition, this algorithm enables the determination of a suitable weight matrix to solve the underlying inverse problem. Therefore, in this paper we present the algorithm and compare it with state of the art block iterative shrinkage thresholding using synthetically generated test data and experimental test data from active thermography for defect reconstruction. Our results show that the use of the learned block-sparse optimization approach provides smaller normalized mean square errors for a small fixed number of iterations than without learning. Thus, this new approach allows to improve the convergence speed and only needs a few iterations to generate accurate defect reconstruction in photothermal super resolution imaging.
This paper presents deep unfolding neural networks to handle inverse problems in photothermal radiometry enabling super resolution (SR) imaging. Photothermal imaging is a well-known technique in active thermography for nondestructive inspection of defects in materials such as metals or composites. A grand challenge of active thermography is to overcome the spatial resolution limitation imposed by heat diffusion in order to accurately resolve each defect. The photothermal SR approach enables to extract high-frequency spatial components based on the deconvolution with the thermal point spread function. However, stable deconvolution can only be achieved by using the sparse structure of defect patterns, which often requires tedious, hand-crafted tuning of hyperparameters and results in computationally intensive algorithms. On this account, Photothermal-SR-Net is proposed in this paper, which performs deconvolution by deep unfolding considering the underlying physics. This enables to super resolve 2D thermal images for nondestructive testing with a substantially improved convergence rate. Since defects appear sparsely in materials, Photothermal-SR-Net applies trained blocksparsity thresholding to the acquired thermal images in each convolutional layer. The performance of the proposed approach is evaluated and discussed using various deep unfolding and thresholding approaches applied to 2D thermal images. Subsequently, studies are conducted on how to increase the reconstruction quality and the computational performance of Photothermal-SR-Net is evaluated.
Thereby, it was found that the computing time for creating high-resolution images could be significantly reduced without decreasing the reconstruction quality by using pixel binning as a preprocessing step.
Laser excited super resolution thermal imaging for nondestructive inspection of internal defects
(2020)
A photothermal super resolution technique is proposed for an improved inspection of internal defects. To evaluate the potential of the laser-based thermographic technique, an additively manufactured stainless steel specimen with closely spaced internal cavities is used. Four different experimental configurations in transmission, reflection, stepwise and continuous scanning are investigated. The applied image post-processing method is based on compressed sensing and makes use of the block sparsity from multiple measurement events. This concerted approach of experimental measurement strategy and numerical optimization enables the resolution of internal defects and outperforms conventional thermographic inspection techniques.
Efforts are rising in opening up science by making data more transparent and more easily available, including the data reduction and evaluation procedures and code. A strong foundation for this is the F.A.I.R. principle, building on Findability, Accessibility, Interoperability, and Reuse of digital assets, complemented by the letter T for trustworthyness of the data. Here, we have used data, which was made available by the Institute Laue-Langevin and can be identified using a DOI, to follow the F.A.I.R.+T. principle in extracting, evaluating and publishing triple axis data, recorded at IN3.
Über das Korrosionsverhalten nichtrostender Stähle bei atmosphärischer Beanspruchung liegen umfangreiche Erfahrungen aus jahrzehntelangen praktischen Anwendungen weltweit vor. Systematische Auslagerungsversuche unter spezifischen Umgebungsbedingungen ergänzen diese Erfahrungen. Die daraus gewonnenen Daten erlauben die beanspruchungsgerechte Werkstoffauswahl. Unter vielen atmosphärischen Bedingungen sind die nichtrostenden Stähle bei freier Bewitterung, auch unter langzeit- oder dauerfeuchten Bedingungen vollständig korrosionsbeständig. Die Auswahl der nichtrostenden Stähle muss den jeweiligen Einsatzbedingungen angepasst werden, um in jedem Fall sicherzustellen, dass der passive Zustand aufrechterhalten bzw. eine erneute Passivierung unter den Einsatzbedingungen möglich ist. Störungen der Passivschichtausbildung führen zu verminderter Korrosionsbeständigkeit mit den für nichtrostende Stähle typischen örtlichen Korrosionserscheinungen. Das Merkblatt gibt Hinweise zur Werkstoffauswahl, zur Oberflächenausführung, zur Konstruktion sowie zu Verarbeitung, Nachbehandlung und Reinigung.
With Direct Laser Writing (DLW) maturing in all aspects as a manufacturing technology a toolset for quality assurance must be developed. In this work we want to introduce a first of its kind test artifact. Test artifacts are standardized 3D models with specific geometric feature to evaluate the performance of writing parameters. Test artifacts are already common in other 3D additive manufacturing technologies e.g. Selective Laser Melting. The test artifact introduced in this work was developed in particular to accommodate 1) the high geometrical resolution of DLW structures and 2) the limited possibilities to examine the resulting structure. Geometric accuracy, surface adhesion as well as confocal raman spectroscopy results were considered when evaluating the design of the test artifact. We will explain the individual features and design considerations of our DLW test artifact. The difference between two slicers, Cura and 3DPoli, and the implications on measured feature sizes and the general shape is quantified. The measured geometries are used to derive a general design guide for a specific combination of photoresist, laser power and scanning speed and to analyse the geometric accuracy of a structure produced using these guidelines.
The impact of magnetism on chemical ordering in face-centered cubic CrCoNi medium entropy alloy is studied by a combination of ab initio simulations, machine learning potentials, and Monte Carlo simulations. Large magnetic energies are revealed for some mixed L12 /L10 type ordered configurations, which are rooted in strong nearest-neighbor magnetic exchange interactions and chemical bonding among the constituent elements. There is a delicate interplay between magnetism and stability of MoPt2 and L12 /L10 type of ordering which may explain opposing experimental and theoretical findings.
The processes of hydrogen reduction of silicon and germanium chlorides under the conditions of radio-frequency (40.68 MHz) counteracted arc discharge stabilized between two rod electrodes were investigated. The main gas-phase and solid products of plasma-chemical transformations were determined.
Thermodynamic analysis of SiCl4 + H2 and GeCl4 + H2 systems was carried out. It is shown that under the implemented experimental conditions, equilibrium components of the products are established. The detected spectra of chemical activity were studied, which gave reason to assume that the molecular mechanism of the hydrogen reduction process is the main one. The impurity composition of gas-phase and solid reaction products was investigated. The possibility of single-stage production of high-purity Si and Ge mainly in the form of compact ingots, as well as high-purity chlorosilanes and trichlorogermane, was shown.