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Air pollution within industrial scenarios is a major risk for workers, which is why detailed knowledge about the dispersion of dusts and gases is necessary. This paper introduces a system combining stationary low-cost and high-quality sensors, carried by ground robots and unmanned aerial vehicles. Based on these dense sampling capabilities, detailed distribution maps of dusts and gases will be created. This system enables various research opportunities, especially on the fields of distribution mapping and sensor planning. Standard approaches for distribution mapping can be enhanced with knowledge about the environment’s characteristics, while the effectiveness of new approaches, utilizing neural networks, can be further investigated. The influence of different sensor network setups on the predictive quality of distribution algorithms will be researched and metrics for the quantification of a sensor network’s quality will be investigated.
Wireless sensor networks provide occupational health experts with valuable information about the distribution of air pollutants in an environment. However, especially low-cost sensors may produce faulty measurements or fail completely. Consequently, not only spatial coverage but also redundancy should be a design criterion for the deployment of a sensor network. For a sensor network deployed in a steel factory, we analyze the correlations between sensors and build machine learning forecasting models, to investigate how well the sensor network can compensate for the outage of sensors. While our results show promising prediction quality of the models, they also indicate the presence of spatially very limited events. We, therefore, conclude that initial measurements with, e.g., mobile units, could help to identify important locations to design redundant sensor networks.
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.
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.
Commercial, off-the shelf propane cylinders are subjected to high safety regulations. Furthermore, those cylinders are equipped with safety devices like pressure relief valves (PRV). Despite these regulations and safety measures, a failure of the container is possible if exposed to an intense fire. The result of this is severe hazard for users, rescue forces and infrastructure. Within the framework of a destructive test series, 15 identical propane cylinders, without pressure relief devices, were exposed to an intensive fire in horizontal position until failure. Each cylinder was filled with a mass of m =11 kg of liquid propane. Three different fire sources were used (wood, petrol, propane). The experiments revealed the failure of all cylinders in a time period t < 155 s. The failure lead to a fragmentation into several major parts with throwing distances of up to l =262 m. In all trials, the temperature of the cylinder wall (top, side, bottom), of the liquid phase inside and of the surrounding fire (top, side, bottom) was recorded. In addition, the inner cylinder pressure and the induced overpressure of the blast wave after the failure were recorded. Overpressures of up to p=0.27 bar were recorded close to the cylinder (l =5 m). All tests were documented by video from several positions (general view, close-up, high-speed 5000 fps). This test series creates the basis for further experimental studies in the field of alternative fuels for vehicles. The aim of this test series is to assess and analyse the consequences of the failure of gas vessels (for LPG, CNG, CGH2) in the aftermath of severe incidents.
In case of a vehicle fire, an installed LPG (liquefied petroleum gas) tank with a malfunctioning safety device poses severe hazards. To investigate the consequences in case of tank failure, we conducted 16 tests with toroidal shaped LPG vehicle tanks. Three tanks were used for a Hydraulic Burst Test under standard conditions. Another three tanks were equipped with a statutory safety device and were subjected to a gasoline pool fire. The safety device prevented tank failure, as intended. To generate a statistically valid dataset on tank failure, ten tanks without safety devices were exposed to a gasoline pool fire. Five tanks were filled to a level of 20 %; the re-maining five were filled to a level of 100 %. In order to gain information on the heating process, three tem-perature readings at the tank surface, and three nearby flame temperatures were recorded. At distances of l = (7; 9; 11) m to the tank, the overpressure of the shock wave induced by the tank failure and the unsteady tem-peratures were measured. All ten tanks failed within a time of t < 5 min in a BLEVE (boiling liquid expanding vapor explosion). Seven of these resulted directly in a catastrophic failure. The other three resulted in partial failure followed by catastrophic failure. A near field overpressure at a distance of l = 7 m of up to p = 0.27 bar was measured. All ten tests showed massive fragmentation of the tank mantle. In total, 50 fragments were found. These 50 fragments make-up 88.6 % of the original tank mass. Each fragment was georeferenced and weighed. Fragment throwing distances of l > 250 m occurred. For the tanks with a fill level of 20 %, the average number of fragments was twice as high as it was for the tanks that were filled completely.
Compressed natural gas (CNG) is a widely used automotive fuel in a variety of countries. In case of a vehicle fire where the safety device also malfunctions, a failure of the CNG automotive cylinder could occur. Such a cylinder failure is associated with severe hazards for the surrounding environment. Firstly, a comprehensive analysis is given below, summarizing various accidents involving CNG automotive cylinders and their consequences. In an extensive experimental program, 21 CNG automotive cylinders with no safety device were tested. Of the 21, burst tests were carried out on 5 Type III and 5 Type IV cylinders. Furthermore, fire tests with 8 Type III and 3 Type IV cylinders were conducted. Apart from cylinder pressure, inner temperature and cylinder mantle temperature, the periphery consequences, such as nearfield blast pressure and fragmentation are documented. The maximum measured overpressure due to a Type III cylinder failure was p = 0.41 bar. Each traceable fragment was georeferenced. All-in-all, fragment throw distances of d > 300 m could be observed. As one key result, it can be stated that the tested Type IV CNG cylinders showed less critical failure behavior then the Type III cylinders under fire impingement.
Concept for investigating mechanical and thermal impacts on distributed subsurface gas monitoring
(2016)
A multifunctional sensor in line shape was developed and introduced in previous work for measuring of gas concentrations, temperature change, and strain. A current field study focuses on a spatially distributed monitoring of subsurface CO 2 gas storage sites in near real time.
Mechanical impacts, e.g., caused by construction work, denudation, and seismic activity, can affect the integrity of underground gas storage sites. Thermal or moisture impacts, e.g., caused by weather conditions, can influence the gas Distribution behavior. In this paper, we briefly describe the setup of a CO 2 injection soil test field. This setup contains actuating elements for the investigation of mechanical and thermal impacts on distributed subsurface gas monitoring. A concept is given for evaluating these impacts and first experimental results are presented.
A multifunctional sensor in line shape was developed and introduced in previous work for measuring of gas concentrations, temperature change, and strain. A current field study focuses on a spatially distributed monitoring of subsurface CO2 gas storage sites in near real time.
Mechanical impacts, e.g., caused by construction work, denudation, and seismic activity, can affect the integrity of underground gas storage sites. Thermal or moisture impacts, e.g., caused by weather conditions, can influence the gas distribution behavior. In this paper, we briefly describe the setup of a CO2 injection soil test field. This setup contains actuating elements for the investigation of mechanical and thermal impacts on distributed subsurface gas monitoring. A concept is given for evaluating these impacts and first experimental results are presented.
A 400 m² soil test field with gas injection system was built up for the purpose of large-scale validation, optimization, and characterization of a novel comprehensive monitoring method for underground gas storage areas. The method combines gas sensing technology with linear form factor for in-situ monitoring of gases in soil with the mapping capabilities of Computed Tomography (CT) to reconstruct time-series of gas distribution maps based on samples of orthogonally-aligned linear gas sensors. Several injection experiments with carbon dioxide (CO2) at different days with varying boundary conditions indicates the potential of the method for, e.g., rapid leakage detection with respect to Carbon Capture and Storage (CCS) issues.