Analytische Chemie
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Eingeladener Vortrag
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The vulnerability of low quality concrete to changing weather conditions is well known. The constant exposure to temperature changes, biological activity, and humidity ends up in damage to buildings and structures which contain this material. It is therefore necessary to take preventive measures to control the extent of the damage done by weathering and possible penetration of adverse chemicals into structures which need public safety.
The Federal Institute for Materials Research and Testing (BAM), in cooperation with the small enterprise LinTech GmbH, is working on a project to monitor humidity changes in concrete by analyzing the changes in signal strength (RSSI) from Bluetooth Low Energy sensors. In this paper, we show results which demonstrate the influence of changing water content in concrete on the received RSSI. We observed that as water content in concrete decreases, the received RSSI improves. However, the damping effect is not linearly proportional to water content, rather exponentially proportional. This suggests that changes in the received signal strength are more easily observed when water content in concrete is higher. Finally, we reconstructed a RSSI distribution map using computed tomography.
A 400 m² soil test field with gas injection system was built up, which enables an experimental validation of linear gas sensors for specific applications and gases in an application-relevant scale. Several injection and soil watering experiments with carbon dioxide (CO2) at different days with varying boundary conditions were performed indicating the potential of the method for, e.g., rapid leakage detection with respect to Carbon Capture and Storage (CCS) issues.
This work presents first results from repeti-tive CO2 injection experiments performed on a recently built-up 400 m² soil test field with gas injection system. The test field contains 48 membrane-based linear gas sensors that were installed in several depths of the test field. Sensors for measuring meteorological parameters (e.g., wind / rain) and the parameters soil temperature, soil moisture, and groundwater level were installed additionally. A more de-tailed description of the test field setup can be found in. A short description of the mem-brane-based linear gas sensors’ functional prin-ciple can be found in.
Untergrundspeicher für Roh- und Abfallstoffe gewinnen zunehmend an Bedeutung. Verwendet werden sie vor allem für Stoffe wie Erdgas, Wasserstoff, Erdöl und neuerdings auch für Kohlen-stoffdioxid (CO2). Diese Stoffe werden meist unter Druck in Kavernen- oder Porenspeichern einge-lagert. Die Speicher dienen einerseits zum Ausgleich von Ungleichgewichten zwischen Ange-bot/Förderung und Nachfrage/Verbrauch und zur Erhöhung der Versorgungssicherheit. Anderer-seits bestehen Konzepte Abfallstoffe oder Gefahrstoffe für die Umwelt (aktuell CO2-Speicherung) dort für lange Zeiträume einzulagern. Mit den Einlagerungsstoffen verbunden ist ein signifikantes Gefahrenpotential für Mensch und Umwelt, falls es trotz aller Sicherheitsmaßnahmen zu einem unkontrollierten Austritt dieser Stoffe kommen sollte. Daher kommt dem Monitoring derartiger Untergrundspeicher und den darüber befindlichen Bodenstrukturen eine extrem hohe Bedeutung zu.
Ein hochaktuelles Beispiel, das die Überwachung entsprechender Bodenflächen fordert, ist die unterirdische CO2-Speicherung im Rahmen der CO2-Abscheidung und -Speicherung (Carbon Dioxide Capture and Storage, CCS). CCS gilt als wichtige Brückentechnologie der Energiewirtschaft und wird weltweit vorangetrieben, während die Sicherheit von Bevölkerung und Biosphäre noch kont-rovers diskutiert wird. Auch die EU setzt auf CCS und gibt in der EU-Richtlinie 2009/31 als Ziel-setzung bis 2015 vor, 15 Pilotanlagen zu bauen und in Betrieb zu nehmen. Als Bedingung für die Genehmigung der CO2-Speicherung ist explizit die Überwachung der Speicheranlagen durch Monito-ring vorgeschrieben, wobei die technisch besten Lösungen zum Einsatz kommen sollen.
Das zu entwickelnde Messsystem adressiert neben den o.g. Anwendungsfeldern weitere, bei denen insbesondere die Emission von Gasen ein Risiko für Mensch und Umwelt darstellt oder wirt-schaftlichen Schaden verursachen kann. Hierzu zählen die Überwachung von Abfalldeponien, Ge-fahrgutlagerstätten, kontaminierten Altlastengebieten, Moor-, Torf-, Kohleflözen (präventive Branderkennung) und geodynamisch aktiven Regionen. Auch moderne Fördertechnologien, wie das Hot-Dry-Rock-Verfahren (HDR) zur Energiegewinnung durch Einpressen von überkritischem CO2 in den Erdkörper, das die Beweglichkeit eines Gases mit der Dichte einer Flüssigkeit kombiniert und Wärmeaustausch im Erdinneren bewirkt, bergen das Risiko unkontrollierter Gasemissionen und bedürfen der umfassenden Überwachung.
KonSens (Kommunizierende Sensorsysteme für die Bauteil- und Umweltüberwachung) - Projektergebnisse
(2019)
Im Projekt KonSens werden für die Anwendungsbeispiele bauteilintegrierte Sensorik für Betonkomponenten und mobile Multigassensorik Sensorsysteme in Form von Funktionsmustern entwickelt, validiert und angewendet. Schwerpunkte liegen einerseits in der Detektion und Bewertung von Korrosionsprozessen in Beton und andererseits in der Detektion und Quantifizierung sehr geringer Konzentrationen toxischer Gase in der Luft. Dabei ist die Adaption der sensorischen Methoden aus dem Labor in reale Messumgebungen inklusive geeigneter Kommunikationstechnik ein wichtiger Aspekt.
In the KonSens Project, sensor systems are developed, validated, and operated in form of functional models for the application areas Structure Integrated Sensors and Mobile Multi-gas Sensors. Key aspects are the detection and evaluation of corrosion processes in reinforced concrete structures as well as the detection and quantification of very low concentrations of toxic gases in air. The adaption of sensor principles from the lab into real-life application including appropriate communication techniques is a major task.
In recent years, Structural Health Monitoring have gained in importance, since growing age of buildings and infrastructure as well as increasing load requirements demand for reliable surveillance methods. In this regard, the project follows two strategies: First, the development and implementation of completely embedded sensor systems consisting of RFID-tag and in situ sensors, and their further application potential (e.g. for precast concrete elements, roadways, wind power plants, and maritime structures). Secondly, the development of a long-term stable, miniaturized, fiber optic sensor for a ratiometric and referenced measurement of the pH-value in concrete based on fluorescence detection as an indicator for carbonation and corrosion.
Environmental pollution through emission of toxic gases becomes an increasing problem not only in agriculture (e.g. biogas plants) and industry but also in urban areas. This leads to increasing demand to monitor environmental emissions as well as ambient air and industrial air components in many scenarios and in even lower concentrations than nowadays. The selectivity of luminescence-based sensors is enabled by the combination of the sensing dye and the material, which is used as accumulation medium for concentration of the analyte. This principle allows for developing gas sensors with high selectivity and sensitivity of defined substances. Additional benefits, particularly of fluorescence-based sensors, are their capability for miniaturization and potential multiplex mode. Objective is the development and implementation of sensors based on fluorescence detection for defined toxic gases (ammonia, hydrogen sulfide, ozone, and benzene) with sensitivity in the low ppm or even ppb range. Additionally, the integration of such sensors in mobile sensor devices is addressed.
Im Projekt KonSens werden für die Anwendungsbei-
spiele bauteilintegrierte Sensorik für Betonkomponen-
ten und mobile Multigassensorik Sensorsysteme in
Form von Funktionsmustern entwickelt, validiert und
angewendet. Schwerpunkte liegen einerseits in der
Detektion und Bewertung von Korrosionsprozessen in
Beton und andererseits in der Detektion und Quantifi-
zierung sehr geringer Konzentrationen toxischer Gase
in der Luft. Dabei ist die Adaption der sensorischen
Methoden aus dem Labor in reale Messumgebungen
inklusive geeigneter Kommunikationstechnik ein
wichtiger Aspekt.
Innovation is the catalyst for the technology of the future. It is important to develop new and better technologies that can continuously monitor the environmental impact, e.g., for air Quality control or emission detection. In the recently at BAM developed Universal Pump Sensor Control (UPSC3) module, different components and sensors are fused. The combination of the individual components makes the UPSC3 module an excellent monitoring and reference system for the development and characterization of gas specific sensors. Measurements over long periods are possible, for mixed gas loads or for certain gas measurements. The System is part of a mobile sensor network of several sensor units, which can also be used as standalone systems.
For mobile robots that operate in complex, uncontrolled environments, estimating air flow models can be of great importance. Aerial robots use air flow models to plan optimal navigation paths and to avoid turbulence-ridden areas. Search and rescue platforms use air flow models to infer the location of gas leaks. Environmental monitoring robots enrich pollution distribution maps by integrating the information conveyed by an air flow model. In this paper, we present an air flow modelling algorithm that uses wind data collected at a sparse number of locations to estimate joint probability distributions over wind speed and direction at given query locations. The algorithm uses a novel extrapolation approach that models the air flow as a linear combination of laminar and turbulent components. We evaluated the prediction capabilities of our algorithm with data collected with an aerial robot during several exploration runs. The results show that our algorithm has a high degree of stability with respect to parameter selection while outperforming conventional extrapolation approaches. In addition, we applied our proposed approach in an industrial application, where the characterization of a ventilation system is supported by a ground mobile robot. We compared multiple air flow maps recorded over several months by estimating stability maps using the Kullback-Leibler divergence between the distributions. The results show that, despite local differences, similar air flow patterns prevail over time. Moreover, we corroborated the validity of our results with knowledge from human experts.
For mobile robots that operate in complex, uncontrolled environments, estimating air flow models can be of great importance. Aerial robots use air flow models to plan optimal navigation paths and to avoid turbulence-ridden areas. Search and rescue platforms use air flow models to infer the location of gas leaks. Environmental monitoring robots enrich pollution distribution maps by integrating the information conveyed by an air flow model. In this paper, we present an air flow modelling algorithm that uses wind data collected at a sparse number of locations to estimate joint probability distributions over wind speed and direction at given query locations. The algorithm uses a novel extrapolation approach that models the air flow as a linear combination of laminar and turbulent components. We evaluated the prediction capabilities of our algorithm with data collected with an aerial robot during several exploration runs. The results show that our algorithm has a high degree of stability with respect to parameter selection while outperforming conventional extrapolation approaches. In addition, we applied our proposed approach in an industrial application, where the characterization of a ventilation system is supported by a ground mobile robot. We compared multiple air flow maps recorded over several months by estimating stability maps using the Kullback-Leibler divergence between the distributions. The results show that, despite local differences, similar air flow patterns prevail over time. Moreover, we corroborated the validity of our results with knowledge from human experts.