Analytische Chemie
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A 400 m2 soil test field with gas injection system was built up, which enables an experimental validation of linear membrane-based gas sensors – a sensor technology which was developed based on the selective permeation of gases through membranes. Several soil watering and injection experiments with carbon dioxide at different days with varying boundary conditions were performed showing that the sensor behaves mostly insensitive to the environmental conditions investigated, i.e., barometric pressure fluctuations, soil temperature, air temperature and humidity, sun duration, and wind speed. Furthermore, depending on water infiltration, a gas phase displacement could be observed in-situ based on the changed measurement signal. The results of the validation experiments highlight the potential of the method for rapid leak detection and localization qualifying the sensor particularly for safety applications, e.g., in underground gas storage areas.
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.
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.
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.
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.
Ammonia and its reaction products can cause considerable damage of human health and ecosystems, increasing the necessity for reliable and reversible sensors to monitor traces of gaseous ammonia in ambient air directly on-site or in the field. Although various types of gas sensors are available, fluorescence sensors have gained importance due to advantages such as high sensitivity and facile miniaturization.
Here, we present the development of a sensor material for the detection of gaseous ammonia in the lower ppm to ppb range by incorporation of a fluorescent dye, which shows reversible fluorescence modulations as a function of analyte concentration, into a polymer matrix to ensure the accumulation of ammonia. A gas standard generator producing standard gas mixtures, which comply with the metrological traceability in the desired environmentally relevant measurement range, was used to calibrate the optical sensor system. To integrate the sensor material into a mobile device, a prototype of a hand-held instrument was developed, enabling straightforward data acquisition over a long period.
- Auswirkungen des unfallbedingten Versagens von Gasbehältern für alternative Kraftstoffe - Kurzvorstellung des Projekts „CoFi-ABV“
- UAV-REGAS Plattform (Unmanned Aerial Vehicle for Remote Gas Sensing): Kombination von Open-Path-Gassensorik mit einem 3-Achs-Gimbalsystem
- Ergebnisse erster experimenteller Untersuchungen
- Möglichkeiten und Herausforderungen der Lokalisierung von Gasquellen und Erstellung von Gasverteilungskarten mittels UAV
- Ausblick für die Weiterentwicklung in Zukunft
Geogenic gases such as CH4 or CO2 from natural sources, gases (CCS-CO2, H2, Natural
gas, City gas …) from a geological repository, or a leaking gas pipeline can present serious
risks in industrial and urban areas where the density of infrastructural elements increases as
well as above and below ground. To extend the lead time for risk treatment in such critical
regions, reliable detection of gases within the shallow subsurface is required to observe
critical gas accumulations before degassing into the atmosphere.
A near real-time monitoring approach is introduced to determine the volumetric expansion of
such a gas escaping from a leak in the subsurface. Considering the pressure relaxation with
the ambient air pressure, the approach enables the forecasting of the final size of a
pressurized gas body in terms of characteristic lengths. According to theoretical basics, such
a characteristic length, which allows to perform a gas (safety) measurement based on a
purely geometrical measure, behaves independently of subsurface properties, i.e., it enables
a reliable quantification of the escaping gas, irrespective of its heterogeneous flow path
distribution. A field test for a 10 l/min pinhole leakage of CO2 injected in an unsaturated Chernozemic soil (agricultural test field Bad Lauchstädt)that was equipped with linear gas sensors demonstrates the lateral-vertical volumetric gas expansion along the environment of these gas sensors, and confirms the applicability of the new characteristic length approach.