Analytische Chemie
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- 8.1 Sensorik, mess- und prüftechnische Verfahren (12) (entfernen)
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Nach einer kurzen Einführung in die Schallemissionsprüfung und -Analyse wird ein Versuchsstand vorgestellt, der im Rahmen des Themenfeldprojektes Seal Waste Safe installiert wurde. Schwerpunkt des Vortrages sind die Schallemissionsmessungen an einem 150 l Demonstrator aus Salzbeton und einem weiteren aus alkali-aktiviertem Material. Neben der konventionellen Schallemissionsanaylse mit Aktivitäts- und Intensitätsparametern der Zeitsignale, werden auch frequenzbasierte Parameter zur Analyse andiskutiert.
Gas distribution mapping is important to have an accurate understanding of gas concentration levels in hazardous environments. A major problem is that in-situ gas sensors are only able to measure concentrations at their specific location. The gas distribution in-between the sampling locations must therefore be modeled. In this research, we interpret the task of spatial interpolation between sparsely distributed sensors as a task of enhancing an image's resolution, namely super-resolution. Because autoencoders are proven to perform well for this super-resolution task, we trained a convolutional encoder-decoder neural network to map the gas distribution over a spatially sparse sensor network. Due to the difficulty to collect real-world gas distribution data and missing ground truth, we used synthetic data generated with a gas distribution simulator for training and evaluation of the model. Our results show that the neural network was able to learn the behavior of gas plumes and outperforms simpler interpolation techniques.
Remote gas sensors mounted on mobile robots enable the mapping of gas distributions in large or poorly accessible areas. A challenging task however, is the generation of three-dimensional distribution maps from these spatially sparse gas measurements. To obtain high-quality reconstructions, the choice of optimal measuring poses is of great importance. Remote gas sensors, that are commonly used in Robot Assisted Gas Tomography (RAGT), require reflecting surfaces within the sensor’s range, limiting the possible sensing geometries, regardless of whether the robots are ground-based or airborne. By combining ground and aerial robots into a heterogeneous swarm whose agents are equipped with reflectors and remote gas sensors, remote inter-robot gas measurements become available, taking RAGT to the next dimension – releasing those constraints. In this paper, we demonstrate the feasibility of drone-to-drone measurements under realistic conditions and highlight the resulting opportunities.
Simulating a Gas Source Localization Algorithm with Gas Dispersion Produced by Recorded Outdoor Wind
(2022)
This presentation reports the use of the first gas dispersion simulator capable of generating large wind fluctuation, to test a modification to a gas source localization algorithm and study how the change affects it. Gas source localization at an outdoor environment is a challenging task mainly due to the complexity of the spreading gas caused by the unpredictable nature of constantly changing wind. Therefore, a novel use of outdoor wind in developing a gas source localization system by simulation will be shared. To consider the characteristic of sudden but large and unpredictable changes in wind direction, recorded outdoor wind was used to simulate a realistic outdoor gas dispersion which has been done for the first time. With the use of this simulator, a modification to a mobile robot-based gas source localization algorithm was evaluated. Multiple simulations of the modified and the original algorithm were done to study the effect of the tested modification. The results showed that a small difference in the algorithm can greatly impact the results. From this study, we consider the use of simulation consisting of the necessary traits to evaluate outdoor gas source localization, has the potential to accelerate the development of a reliable localization system.
To better understand the dynamics in hazardous environments, gas distribution mapping aims to map the gas concentration levels
of a specified area precisely. Sampling is typically carried out in a spatially sparse manner, either with a mobile robot or a sensor network and concentration values between known data points have to be interpolated. In this paper, we investigate sequential deep learning models that are able to map the gas distribution based on a multiple time step input from a sensor network. We propose a novel hybrid convolutional LSTM - transpose convolutional structure that we train with synthetic gas distribution data. Our results show that learning the spatial and temporal correlation of gas plume patterns outperforms a non-sequential neural network model.
Ein Umlaufkühler ist im Betrieb explodiert. Splitter des zerborstenen Gehäuses aus Kunststoff wurden mit dem Kühlwasser in die Umgebung geschleudert, am Betriebsort entstand Personenschaden. Bei Funktionsprüfungen am beschädigten Gerät traten unerwartet - aber reproduzierbar - Knalleffekte bei Berührung der Außenoberfläche der Kupfer-Kühlschlange auf. Ein möglicher Mechanismus konnte im Labor durch Synthese von Kupferazid auf Kupferproben und Auslösung vergleichbarer Knalleffekte nachgestellt werden. Damit ist die Plausibilität des beschriebenen Schadensereignisses mit diesem oder einem ähnlich reagierenden Stoff belegt. Ein eindeutiger Nachweis darüber, dass bei dem aufgetretenen Schadensfall dieselbe chemische Reaktion stattgefunden hat, war nicht möglich, da die Belag-Überreste aus dem explodierten Kühlgerät für eine Analyse nicht mehr in ausreichender Menge verfügbar gewesen sind.
Occupational health is an important topic, especially in industry, where workers are exposed to airborne by-products (e.g., dust particles and gases). Therefore, continuous monitoring of the air quality in industrial environments is crucial to meet safety standards. For practical and economic reasons, high-quality, costly measurements are currently only carried out sparsely, both in time and space, i.e., measurement data are collected in single day campaigns at selected locations only.
The project “Robot-assisted Environmental Monitoring for Air Quality Assessment in Industrial Scenarios” (RASEM) addresses this issue by bringing together the benefits of both – low- and high-cost – measuring technologies enabling cost-efficient long-term air quality monitoring in realtime: A stationary network of low-cost sensors that is augmented by mobile units carrying high-quality sensors. By mapping the distribution of gases and particles in industrial environments with the proposed RASEM system, measures can be identified to improve on-site working conditions much faster than using traditional methods.
In this presentation, we detail the technical aspects of RASEM and introduce the mobile platforms used.