Hochaufgelöste Simulation von meteorologischen Feldern in urbanen Räumen mit dem Weather Research and Forecasting (WRF) Modell

<p><span>Städte und Kommunen sind für mehr als 70% </span><span>der globalen, fossilen CO2-Emissionen</span><span> verantwortlich, sodass hier ein enormes Mitigationspotential besteht. Informationen über (inner-)städtische CO2-Emissionen stehen allerdings oft nicht </span><span>in hoher zeitlicher und räumlicher Auflösung</span><span> zur Verfügung und sind </span><span>meist</span><span> mit großen Unsicherheiten behaftet. Diese Umstände erschweren eine zielgerichtete und effiziente Mitigation im urbanen Raum. </span><span>Städtische Messnetzwerke können als unabhängige Informationsquelle einen Beitrag leisten, um CO2-Emissionen in Städten zu quantifizieren und Mitigation zu verifizieren</span><span>. </span><span>Verschiedene denkbare Beobachtungsstrategien sollten</span><span> im Vorfeld abgewägt werden, um urbane Emissionen bestmöglich, d.h. mit der erforderlichen Genauigkeit und </span><span>Kosteneffizienz</span><span> zu quantifizieren. So können Messnetzwerke die Basis für zielgerichtete und kosteneffiziente Mitigation legen.</span></p><p><span>Im Rahmen des Verbundvorhabens „Integrated Greenhouse Gas Monitoring System for Germany“ (ITMS) werden wir verschiedene Beobachtungsstrategien für urbane Räume entwerfen und mit Hilfe von Modellsimulation evaluieren und abwägen. Notwendige Voraussetzung für </span><span>die Evaluation der Strategien</span><span> ist eine akkurate Repräsentation des atmosphärischen Transports im Modell.</span></p><p><span>Diese Studie zeigt</span><span> erste Ergebnisse der hochauflösenden (1kmx1km) meteorologischen Simulationen für den Rhein-Neckar-Raum mit dem WRF Modell. </span><span>Die in WRF simulierten meteorologischen Größen werden für verschiedene Modellkonfigurationen mit </span><span>re-analysierten Daten des European Centre for Medium-Range Weather Forecasts (ECMWF) und ausgewählten Messstationen verglichen. Damit evaluieren wir </span><span>den Einfluss unterschiedlicher Nudging-Strategien, Parametrisierungen physikalischer Prozesse und urbaner Interaktionen</span><span> auf </span><span>die Modellperformance</span> <span>von</span><span> Lufttemperatur, Windrichtung, Windgeschwindigkeit und Grenzschichthöhe. Durch diese Analysen gewährleisten wir, dass die Simulation der Beobachtungsstrategien auf robuste</span><span>m</span><span> und realistische</span><span>m</span><span> atmosphärischen Transport basieren und schlussendlich repräsentative Empfehlungen für den Aufbau von Messnetzwerken liefern können. </span></p>

Hardware Reliability Analysis of a Coal Mine Gas Monitoring System Based on Fuzzy-FTA

The hardware reliability of a gas monitoring system was investigated using the fuzzy fault tree analysis method. A fault tree was developed considering the hardware failure of the gas monitoring system as a top event. Two minimum path sets were achieved through qualitative analysis using the ascending method. The concept of fuzzy number of the fuzzy set theory was applied to describe the probability of basic event occurrence in the fault tree, and the fuzzy failure probabilities of the middle and top events were calculated using fuzzy AND and OR operators. The results show that the proposed fuzzy fault tree is an effective method of reliability analysis for gas monitoring systems. Results of calculations using this method are more reasonable than those obtained with the conventional fault tree method.

Decision-Making Optimization of Mine Gas Monitoring Based on Gauss Process Regression and Interval Number Correlation Analysis

For the subjective limitation of gas sensor calibration in coal mines, a decision-making method for gas sensor calibration under monitoring failure was studied based on the Gauss process regression (GPR) and the correlation analysis of interval numbers. Based on the correlation characteristics of gas monitoring data of each monitoring point in the work face area in coal mine, the initial confidence interval of gas concentration in monitoring failure period was obtained by GPR, and then the confidence interval was further optimized by the correlation analysis of interval numbers. According to the correlation characteristics of monitoring data of each monitoring point, its similarity of dynamic variation tendency was measured by using Euclidean distance of interval numbers, and the optimal confidence interval was determined by calculating the correlation degree of interval numbers. The case study shows that making full use of the effective monitoring information of multiple monitoring points ensures the reliability of the initial confidence interval; the dynamic adjustment of model parameters in correlation analysis of interval number avoids the subjectivity defect of similar methods and further obtains the consistency between interval numbers’ reliability and correlation degree, which can ensure the effectiveness of the application of this method.

Prediction of Dispersion Behavior of Typical Exhaust Pollutants From Mine Hydraulic Support Transporters Based on Numerical Simulation

To analyze the impact of exhaust emissions from mine hydraulic support transporters on the roadway environment. In this paper, the dispersion distribution of diesel exhaust pollutant during the functioning of a hydraulic support transporters were all-round simulated by Dynamic Mesh of Computational Fluid Dynamics. More specifically, the dispersion and distribution of the main exhaust pollutants CO, HC, and NOx emitted by vehicles under the influence of the roadway wind flow were simulated with computational fluid dynamics (CFD) and the dispersion of exhaust pollutants from hydraulic support transporters during multiple driving phases in an alleyway (from hauling in material, unloading at idle speed, to driving off with no load) was predicted. The simulation results show that the exhaust pollutants emitted during the movement of hydraulic support transporters can pollute the roadway environment and negatively affect gas monitoring devices in the roadway. Therefore, coal mining enterprises should optimize the ventilation design scheme to improve the roadway environment: they should increase the ventilation volume to dilute the emitted pollutants; in addition, the locations of underground gas monitoring devices should be adjusted to avoid interference from exhaust pollutants emitted by vehicles. This paper provides a theoretical basis for the preliminary investigation of the dispersion and transportation characteristics of exhaust pollutants emitted by vehicles in roadways, the research in this paper is of guiding significance to reduce the inhalation of the diesel exhaust pollutants of the miners and reduce the probability of suffering from occupational diseases.

IoT Based Sewage Gas Monitoring System with Overflow Detection

The sewage system is an important component of urban infrastructure. Most of the cities adopted efficient sewage systems to maintain the cleanliness of the cities. If the sewage maintenance is not proper, infectious disease may spread. Current sewage monitoring system is not efficient, as it is difficult to find out the poisonous gas level of sewage water. In the existing system, water level and temperature sensors are used to detect the overflow and the inside temperature of the sewage plant. An “IoT BASED SEWAGE GAS MONITORING SYSTEM WITH OVERFLOW DETECTION” is proposed to overcome the above mentioned limitations of existing systems. Internet of Things (IoT) is a network of physical objects that use sensors to capture data and exchange information over the internet which can be adopted to detect and monitor the exaction location of sewage system and the amount of poisonous gases present in the sewage plant.One of the advantage of this project is to prevent of sewage water get mixed with drinking water ,so that the environmental health issues can be solved. The advanced MQ5 sensor is used instead of MQ4 sensor as it detects only methane gas.MQ5 sensor helps to detect the poisonous and flammable gases such as carbon monoxide, methane & LPG. Electrochemical oxygen sensor is provided to detect oxygen level. Then the final measurement will be sent through the WiFi module to the database . The GSM and GPS module is used to send the emergency message and current location to a specific smartphone . In future the project can be upgraded with the aid of modern technology like artificial intelligence.