Concentration Of Different Gases Research Articles

Variation Law of Hybrid Explosion Characteristic Parameters of Gas and Coal Dust Coupled with Multiple Factors.

This study aims at extensively investigating the explosion characteristics of a hybrid mixture of gas and coal dust. Accordingly, the standard 20 L spherical explosion system was applied to measure parameters such as the lower explosion limit, maximum explosion pressure, and index of the hybrid mixture of different concentrations of gas and coal dust. Moreover, different coal dust particle sizes and components were measured. With regard to coal dust with different particle sizes and components, the obtained results revealed that, while the addition of gas significantly reduced the lower explosion limit, the maximum explosion pressure and index were increased; that is to say, the presence of gas will increase the explosion risk of coal dust. However, under conditions in which the particle size of the coal dust was large or the volatile content was low, the addition of gas was found to lead to a higher decrease of the lower explosion limit; this is, while the maximum explosion pressure and explosion index were increased. Consequently, gas can be argued to have a greater influence on the explosion risk of coal dust with a large particle size or low volatile content. Furthermore, regardless of the particle size or the volatile content of coal dust, the maximum explosion pressure and explosion index of the hybrid mixture were observed to be higher than that of the pure coal dust but lower than that of the pure gas. That is to say, the explosion intensity of the gas/coal dust composite system is higher than that of pure coal dust but less than that of pure gas. The research results can provide theoretical basis for coal mine explosion disaster prevention and control and have important significance.

Enhancing the efficiency of gas sensing on perovskite BaTiO3 nanoparticles using dynamic shock wave flow environment

The effect of nonlinear dynamic shock waves on perovskite BaTiO3 nanoparticles (NPs) is investigated using a table top pressure-driven shock tube (Reddy Tube) for ammonia gas sensing applications with the Mach number of 2.2. In general, shock waves are a nonlinear high-pressure phenomenon characterized by high dynamic pressure and temperature. Raman and powder X-ray diffraction (XRD) methods were utilized to evaluate the molecular and structural characteristics of the BaTiO3 (BTO) NPs under the influence of dynamic shock waves. The surface absorption was assessed using UV- diffuse reflectance spectroscopy (UV-DRS). XRD data shows that BaTiO3 NPs crystallized in tetragonal crystal structure with P4mm space group and stable throughout the shocks, without undergoing any phase change. The average particle size of the control BaTiO3 was 31 nm, while those of shocked BaTiO3 were 62 nm (100 shocks) and 55 nm (200 shocks), demonstrating the significant change in morphology and showing that particles have irregular spherical morphology and are agglomerated by shock wave loaded conditions. Moreover, BaTiO3 nanoparticles were shocked after being tested with various concentrations of different gases at a temperature of 200°C. Under shock loaded conditions, the heightened sensing response for ammonia gases was between 5 and 20 ppm; also, sensing properties such as sensitivity, response, and recovery time indicated that BaTiO3 nanoparticles are a good potential ammonia gas sensing material for real-time sensor applications with enhanced characteristics.

Ultrasensitive HCHO sensor based on Cr-doped cerium oxide nanoparticles with full recoverability and decisive anti-humidity ability

Notably, the Cr-doped sensor sense the 100-ppb formaldehyde which is below the 740-ppb permissible exposure limit (fixed by WHO). A highly crystalline puny-size nanoparticles are obtained by single air-annealing process (400 °C). The size-controlled undoped and Cr-doped cerium nanoparticles produce the oxygen vacancies, helping to expend more active sites to gas molecules. Higher-surface area of doped cerium supports formaldehyde sensing proficiently. The Cr-doped cerium sensor operates at noticeable temperature of 50 and 80 °C for formaldehyde gas where, the sensor responses to ∼2 s and recovered around 3 s (5 ppm), suggesting appreciable signs to formaldehyde detection. The sensor shows the LOD of 0.1 ppm (100 ppb) and unveil the good stability to anti-humidity test. The growing oxygen-vacancies demonstrate the sensor signal increase and also dopant enriches the oxygen-vacancies. The stability of the sensor surface has been identified by ladder-growth of current signal to different concentrations of gas. Current signal to formaldehyde increase implies the enhancement of conductivity. Highly-selective response to formaldehyde attributes to the particle size, oxygen vacancies and bandgap of Cr-doped cerium sensor material. At last, the reproducibility inspection unveils 99.06 % results. Such a high reproducibility confirms the long-term chemical stability and mechanical robustness of sensor, opening a route to developing industrially viable sensor technology.

Ocimum sanctum Leaf Extract-Assisted Green Synthesis of Pd-Doped CuO Nanoparticles for Highly Sensitive and Selective NO2 Gas Sensors.

In view of facile, cost-effective, and environmentally friendly synthetic methods, palladium-doped copper oxide (Pd-CuO) nanoparticles have been synthesized from Ocimum sanctum (commonly known as "Tulsi") phytoextract for gas-sensing applications. The structural, morphological, and compositional properties of Pd-doped CuO nanoparticles were studied using various techniques such as XRD, FESEM, XPS, and EDX. The characterization results confirmed the doping of Pd on CuO nanoparticles, and Pd-CuO nanostructures appear as nanoflakes in FESEM analysis. The gas-sensing response of Pd (1.12 wt %)-CuO nanoflake-based sensor was measured at 5-100 ppm concentration of different gases, NO2, H2S, NH3, and H2, at 125 °C. Gas-sensing tests reveal that the sensitivity of the sensor were 81.7 and 38.9% for 100 and 5 ppm concentrations of NO2, respectively, which was significantly greater than that of pure CuO. The response and recovery times of the sensor were 72 and 98 s for 100 ppm of NO2 gas, while they were 90 and 50 s for 5 ppm NO2. The calculated limit of detection (LOD) value of the sensor is 0.8235. This appealing LOD is suitable for real-time gas detection. The gas sensor was found to exhibit excellent selectivity toward NO2 gas and repeatability and stability in humid (80%) conditions. The Pd doping in CuO nanostructures plays a significant role in escalating the sensitivity and selectivity of CuO-based NO2 gas sensor suitable to work at low operating temperatures.

Gas sensing in industry. A case study: Train hangar

The detection and measurement of gas levels have become very important in both domestic and industrial fields. This is a result of the realization that certain gases are toxic and may have a harmful or even lethal effect on humans (and/or the environment), depending on exposure time or concentration. For this reason, gas sensors play a crucial role, especially in industries, where they help to prevent risks by detecting and measuring the presence of harmful gases in the workers’ environment. There is a wide variety of sensors and techniques used to detect gases and determine their characteristics. The case study presented in this paper analyzes the presence of gases in a train hangar. Using a range of low-cost sensors, it is possible to measure the concentration of different gases over time and identify the moments where these concentrations are higher. In this study, it has been determined that these periods are usually related to moments when maintenance works are taking place in the hangar and their relationship with the measurements of a high-accuracy CO2 sensor. Detecting and predicting when these events take place allows to alert employees in case gas levels are considered dangerous for human health, or to take different actions to counteract this threat. An analysis of how to improve the energetic efficiency of the hangar is also carried out, estimating the losses caused by an inefficient ventilation system. Finally, some ideas are given to help improve the well-being of the workers, and also reduce energy costs.

Development of an IoT-Enabled Air Pollution Monitoring and Air Purifier System

Air Pollution is one of the severe hazards which affect the health quality of all living beings and the environment itself, especially in urban areas. It creates significant health issues in many countries and harms human health if its concentration exceeds tolerable levels. Monitoring these pollutants and their concentrations is a crucial preventative measure to alert the public to the air quality in the area. The primary objective of this paper is to design a smart air pollution prototype to purify the air with the help of a PM sensor, gas sensors, analog to digital converter (ADC), Raspberry Pi 3 B+ microcontroller, and multiple filters (Step-by-step filtering of particles from sizes 200 to 0.3 µm will be done by different filters). The gas sensors, MQ2 and MQ135, measure the presence/concentration of the LPG, smoke, CO, CO2, and NH4 before and after the filtering process. The PMSA003 sensor detects Particulate Matter (PM) in the range of 0.3 microns to 10 microns. ADC and Raspberry Pi 3 B+ convert the outputs of gas sensors and PM sensors into concentrations of particles. These results are transmitted to the Thingspeak Cloud platform through the WiFi Module. The sensor outputs can be viewed in any system/mobile in the Thingspeak Cloud platform. The concentration of different gases and the measured size concentration of various particles are plotted before and after the filtration, and the results are compared. It is found that the designed filter effectively filters all the particles above 0.3 µm. The performance and efficiency test of both air quality monitoring and air purifier system is done.

Impact of Storage Controlled Atmosphere on the Apple Phenolic Acids, Flavonoids, and Anthocyanins and Antioxidant Activity In Vitro.

Apples are seasonal fruits, and it is important to prepare them adequately for storage and ensure proper storage conditions. In this study, we used ten different apple cultivars: ‘Alva’, ‘Auksis’, ‘Connell Red’, ‘Cortland’, ‘Ligol’, ‘Lodel’, ‘Noris’, ‘Rubin’, ‘Sampion’, and ‘Spartan’. We studied the qualitative and quantitative composition of phenolic compounds in the apple and apple extracts antioxidants activity before placing them in the controlled atmosphere chambers and again at the end of the experiment, eight months later. Different concentrations of O2, CO2, and N2, constant temperature, relative humidity, and removal of endogenous ethylene were continually maintained. HPLC analysis showed that the highest amount of 2265.7 ± 152.5 µg/g of chlorogenic acid was found in apple samples of the ‘Auksis’ cultivar stored under variant IV conditions. Different concentrations of gas in the controlled atmosphere chambers caused changes in antioxidant activity in whole apple and apple peel extracts. In our study, we found that the antioxidant activity of apple extracts varied between samples of different apple cultivars and depended on the composition of the controlled atmosphere. Determining the optimal storage conditions is beneficial to providing the consumers with apples that have a known and minimally altered chemical composition of phenolic compounds and the strongest antioxidant activity, which determine the use of apples in the healthy food chain.

Development of a Sensor Array Based on Pt, Pd, Ag and Au Nanocluster Decorated SnO2 for Precision Agriculture

Introduction Nowadays, precision farming is a key topic. Because of the steady increase in the world's population, which is expected to reach 9.6 billion by 2050, it is vital to increase the productivity of agricultural land while reducing waste of water, fertilizers, pesticides and pesticide. To achieve these goals, precision agriculture aims to provide farmers with a wealth of information to optimize field management, by matching farming practices more closely to crop needs. This information is obtained exploiting satellite and weather data, and wireless sensor arrays, which combined with the use of GPS, Internet of Things (IoT) and machine learning allow the farmer to operate with both a control and predictive approach [1].For the best use of precision agriculture, it is therefore essential to collect as much data as possible on the crop status. On the one hand, various technologies have been developed or improved to collect information directly in the field, in particular to measure the pH, the nitrogen compound concentrations and the humidity amount in the soil [2,3]. On the other hand, there is not yet a well-structured system for monitoring crop gas emissions, which together with the control of soil parameters, can lead to a comprehensive evaluation of the effective health status and growth of the crop [4]. In this work, a sensor array composed of four different sensing materials, i.e. SnO2 decorated with Ag, Pd, Pt and Au nanoclusters, were developed and investigated to selective detect five different gases commonly present or emitted by crops. Material and Method SnO2 nanoparticles were synthesized by means of sol-gel technique, by dissolving Sn(II) ethylhexanoate in a hydroalcoholic solution of water and 2-propanol. Afterwards, it was adopted a simple impregnation method to add the metal nanoclusters. For instance, to obtain the decoration with Au nanoclusters, SnO2 nanoparticles were kept stirred in a water solution together with AuBr3, at room temperature. The nanopowder was then calcined at 650°C for 2 hours.The four sensing materials were then screen printed onto alumina substrates, which were equipped with a Pt heater and Au interdigitated electrodes.The gas sensors were then tested with five different gases commonly present or emitted by crops: ethylene, isoprene, CO, methanol and ammonia. They were analyzed five different concentrations of ethylene, methanol and CO and three concentrations of isoprene and ammonia, respectively. The sensing responses were collected by thermo-activating the sensing materials at four different temperatures: 200, 250, 300 and 350°C.The response values from the sensors, combined in a 3-dimensional point for each gas concentration, were processed through a Support Vector Machine (SVM) with a linear kernel to improve sensors selectivity. A first set was used to train the system, and a second one was used to optimize and test its performance. Results and Conclusions The Scanning Electron Microscope (SEM) analysis highlighted that the average size of the synthesized SnO2 nanoparticles was about 30-40 nm (Figure 1), while the X-ray Diffraction (XRD) highlighted a single crystalline phase (cassiterite). The SEM images showed that the metal nanoclusters were growth over the SnO2 nanoparticles surface, without modifying the SnO2 morphology, average size and crystalline structure (XRD) of SnO2. The weight concentration of metals in decorated samples was lower than 1% (Figure 2).The gas sensing characterization was performed in a sealed aluminum gas chamber, by exploiting certified cylinders of target gases and mass flow controllers. The Figure 3 shows the Principle Component Analysis (PCA) for the SnO2/Pt gas sensors vs. tested gases. As it can be observed, the PCA clearly discriminates the different concentrations of gases. The Figure 4 a) and b) show the results obtained in the test set of gas sensing responses with SnO2/Pt gas sensor, after the training of the SVM with linear kernels fit, carried out to build the classification model. The position of each point of the test set was compared with the trained model and thus classified. Figure 4a) highlights that there was a perfect SVM classification (100%) of the different concentration of the tested gases (21 points) for the SnO2/Pt, with a relative low error in the estimated concentrations compared to the real ones (Figure 4b). The same data analysis was then performed for the other sensing materials: Ag, Au and Pd/SnO2. Finally, the SVM classification was also carried out by combining all the gas sensing responses of the four different gas sensors, by achieving a perfect classification of the gases analyzed together with a very low error in the estimated concentrations, compared with the concentrations injected in the gas chamber.

SIMULATION AND ANALYSIS OF DGA ANALYSIS FOR POWER TRANSFORMER USING ADVANCED CONTROL METHODS

In any power system or grid network transformer device use for power from one circuit to another circuit without changing the frequency with high efficiency levels. Due to number of usages Transformer protection is very important now a days for electric supply which is fault free, efficiency and to increase the transformer life cycle. This paper is a brief discussion about the concentration of different gases like CO, CO2, H2, C2H6, C2H4, C2H2 and CH4 related faults which is known as DGA analysis with the help of various classical techniques gives different conditions for the same sample unit. This paper presents MATLAB simulation of ANN and Machine learning based high accuracy design techniques for DGA analysis and the results are compared with the classical techniques like Key Gas Method, IEC Ratio method, Duval triangle Method and Rogers Ratio Method, but in this paper we have done only Duval triangle Method for Comparison using Matlab. The Simulation result of the proposed methods shows that overall DGA analysis using Machine learning algorithm is better than conventional Duval triangle method Performance.

SIMULATION AND ANALYSIS OF DGA ANALYSIS FOR POWER TRANSFORMER USING ADVANCED CONTROL METHODS

In any power system or grid network transformer device use for power from one circuit to another circuit without changing the frequency with high efficiency levels. Due to number of usages Transformer protection is very important now a days for electric supply which is fault free, efficiency and to increase the transformer life cycle. This paper is a brief discussion about the concentration of different gases like CO, CO2, H2, C2H6, C2H4, C2H2 and CH4 related faults which is known as DGA analysis with the help of various classical techniques gives different conditions for the same sample unit. This paper presents MATLAB simulation of ANN and Machine learning based high accuracy design techniques for DGA analysis and the results are compared with the classical techniques like Key Gas Method, IEC Ratio method, Duval triangle Method and Rogers Ratio Method, but in this paper we have done only Duval triangle Method for Comparison using Matlab. The Simulation result of the proposed methods shows that overall DGA analysis using Machine learning algorithm is better than conventional Duval triangle method Performance.

A Novel Gas Recognition and Concentration Detection Algorithm for Artificial Olfaction

A novel gas recognition and concentration detection algorithm consisting of a dynamic wavelet convolutional neural network (DWCNN) and a many-to-many long short-term memory-recurrent neural network (LSTM-RNN), respectively, is proposed as a replacement for the traditional data processing algorithm in artificial olfaction. The proposed DWCNN gas recognition algorithm does not require gas signal preprocessing, and it directly converts the raw time-domain gas signal data to 64 * 64 2-D gray images as the input layer of a convolutional neural network (CNN). The experiments show that the recognition accuracy of CO, H2, and the gas mixture of CO and H2 is nearly 100%, and the many-to-many LSTM-RNN algorithm requires only a few labeled data from the steady-state values of the gas sensor array signals. In addition, comparisons with other neural network multilayer perceptrons (MLPs), gated recurrent unit (GRU) algorithms, and conventional algorithms, such as the Bayesian ridge, support vector machines (SVMs), decision tree, k-nearest neighbor (KNN), random forest, AdaBoost, gradient-boosting decision tree (GBDT), and bagging, revealed that the algorithm can obtain a higher concentration detection accuracy, which was evaluated using two different kernel functions for the kernel principal component analysis (KPCA) dimensionality reduction: polynomial and rbf. The experimental results demonstrated that the proposed many-to-many LSTM gas concentration detection model outperformed the abovementioned algorithms and can more accurately estimate the concentration of different gases while using less labeled data.

Demand forecast and allocation system of coalbed methane of different grades in mining area

ABSTRACT Coal mine drainage gas, also known as Coal-Bed Methane (CBM), is a type of clean energy. Different concentrations or grades of gas are extracted and used in the mining area, and the supply and demand of different concentrations of gas is complicated. To achieve optimal resource allocation and minimal environmental pollution, the utilization of different concentrations of CBM and the time characteristics of utilization are explored, and a method for forecasting the demand for different concentrations of gas is proposed based on the time series forecasting algorithm. With the model fitting parameters combined, the prediction models are evaluated by comparison of the predicted results with the actual data. The predicted results show that the simple periodic model can reasonably predict the demand for high-grade CBM in the mining area, while the Additive Holt-Winters (AHW) model can forecast the demand for low-grade CBM properly. And the fitting parameters of the prediction models indicate that the information is sufficiently extracted and the prediction results are satisfactory. Finally, the resource allocation system for different grades of CBM is proposed through the minimum element method and the analytic hierarchy process. The proposed allocation system can provide a reference for developing a better CBM allocation system in the mining area to reduce the waste of CBM.

Electronic Nose Testing for Confined Space Application Utilizes Principal Component Analysis and Support Vector Machine

A confined space has a limited space for entry and exit but it is large enough for workers to enter and perform work inside. It is not designed for continuous occupancy because it can contribute atmospheric hazards accidents that threaten the worker safety and industry progress. In this work, we reported the testing an instrument to assist workers for atmosphere testing during pre-entry. An electronic nose (e-nose) using specific sensor arrays is the integration between hardware and software that able to sense different concentrations of gases in an air sample using pattern recognition techniques. The instrument utilizes multivariate statistical analysis which is Principal Component Analysis (PCA) for discriminate the different concentrations of gases and the Support Vector Machine (SVM) to classify the acquired data from the air sample. The instrument was successfully tested using diesel, gasoline, petrol and thinner. The results show that the instrument able to discriminate an air sample using PCA with total variation for 99.94%, while the classifier success rate for SVM indicates at 98.21% for train performance and 95.83% for test performance. This will contribute significantly to acquiring a new and alternative method of using the instrument for monitoring the atmospheric hazards in confined space to ensure the safety of workers during work progress in a confined space.

Development of a Pt, Pd, Ag and Au Nanocluster Decorated SnO2 Sensor Array for Precision Agriculture

Introduction Nowadays, precision farming is a key topic. Because of the steady increase in the world's population, which is expected to reach 9.6 billion by 2050, it is vital to increase the productivity of agricultural land while reducing waste of water, fertilizers, pesticides and pesticide. To achieve these goals, precision agriculture aims to provide farmers with a wealth of information to optimize field management, by matching farming practices more closely to crop needs. This information is obtained exploiting satellite and weather data, and wireless sensor arrays, which combined with the use of GPS, Internet of Things (IoT) and machine learning allow the farmer to operate with both a control and predictive approach [1]. For the best use of precision agriculture, it is therefore essential to collect as much data as possible on the crop status. On the one hand, various technologies have been developed or improved to collect information directly in the field, in particular to measure the pH, the nitrogen compound concentrations and the humidity amount in the soil [2,3]. On the other hand, there is not yet a well-structured system for monitoring crop gas emissions, which together with the control of soil parameters, can lead to a comprehensive evaluation of the effective health status and growth of the crop [4]. In this work, a sensor array composed of four different sensing materials, i.e. SnO2 decorated with Ag, Pd, Pt and Au nanoclusters, were developed and investigated to selective detect five different gases commonly present or emitted by crops. Material and Method SnO2 nanoparticles were synthesized by means of sol-gel technique, by dissolving Sn(II) ethylhexanoate in a hydroalcoholic solution of water and 2-propanol. Afterwards, it was adopted a simple impregnation method to add the metal nanoclusters. For instance, to obtain the decoration with Au nanoclusters, SnO2 nanoparticles were kept stirred in a water solution together with AuBr3, at room temperature. The nanopowder was then calcined at 650°C for 2 hours.The four sensing materials were then screen printed onto alumina substrates, which were equipped with a Pt heater and Au interdigitated electrodes.The gas sensors were then tested with five different gases commonly present or emitted by crops: ethylene, isoprene, CO, methanol and ammonia. They were analyzed five different concentrations of ethylene, methanol and CO and three concentrations of isoprene and ammonia, respectively. The sensing responses were collected by thermo-activating the sensing materials at four different temperatures: 200, 250, 300 and 350°C.The response values from the sensors, combined in a 3-dimensional point for each gas concentration, were processed through a Support Vector Machine (SVM) with a linear kernel to improve sensors selectivity. A first set was used to train the system, and a second one was used to optimize and test its performance. Results and Conclusions The Scanning Electron Microscope (SEM) analysis highlighted that the average size of the synthesized SnO2 nanoparticles was about 30-40 nm (Figure 1), while the X-ray Diffraction (XRD) highlighted a single crystalline phase (cassiterite). The SEM images showed that the metal nanoclusters were growth over the SnO2 nanoparticles surface, without modifying the SnO2 morphology, average size and crystalline structure (XRD) of SnO2. The weight concentration of metals in decorated samples was lower than 1% (Figure 2).The gas sensing characterization was performed in a sealed aluminum gas chamber, by exploiting certified cylinders of target gases and mass flow controllers. The Figure 3 shows the Principle Component Analysis (PCA) for the SnO2/Pt gas sensors vs. tested gases. As it can be observed, the PCA clearly discriminates the different concentrations of gases. The Figure 4 a) and b) show the results obtained in the test set of gas sensing responses with SnO2/Pt gas sensor, after the training of the SVM with linear kernels fit, carried out to build the classification model. The position of each point of the test set was compared with the trained model and thus classified. Figure 4a) highlights that there was a perfect SVM classification (100%) of the different concentration of the tested gases (21 points) for the SnO2/Pt, with a relative low error in the estimated concentrations compared to the real ones (Figure 4b). The same data analysis was then performed for the other sensing materials: Ag, Au and Pd/SnO2. Finally, the SVM classification was also carried out by combining all the gas sensing responses of the four different gas sensors, by achieving a perfect classification of the gases analyzed together with a very low error in the estimated concentrations, compared with the concentrations injected in the gas chamber.

Detection of Hazardous Gas Mixture in the Smart Kitchen Using an Electronic Nose Under Interference Gas Environment

Introduction For a long time, due to various factors such as kitchen gas, house decoration and living habits, concentrations of toxic and harmful gases in the kitchen are likely to be much higher than national standards in a short period of time. High concentrations of toxic and harmful gases can cause extremely adverse effects on the human body, which can lead to physical discomfort and even serious accidents such as fires and explosions.In the past few years, some researches focused on the development of a more specific and intelligent gas detection system, including the detection of harmful gases in industrial environments [1], dairy industry [2] and living environment [3]. However, there are few studies on graded, low-cost, wide-range, cheap, multifunctional and simultaneous gas detection systems for mixed harmful gases in kitchen so far. E-nose System The potential toxic and harmful gases in the kitchen are mainly carbon monoxide and methane. However, formaldehyde is also present in the kitchen and acted as interference gas in this study. Therefore, a corresponding detection system was developed to evaluate the concentrations of the mixed harmful gases using a MOS gas sensor array including MP-9, MP-4, MP503, TGS821, TGS816, TGS2602. Nearly half of collected samples contained formaldehyde. Besides, a SHT20 sensor is used to monitor humidity and temperature. This system (Fig.(A)) consists of three parts: the gas blender module, the detection module and the concentration identification module.The gas blender mixes different gases and provides required mixture for detection. All mixed gas samples are diluted by the standard air in order to make the MOS sensors work properly. The standard air is directly discharged into the gas chamber for cleaning. After that, the detection module collects the response of the sensor array to the mixed gas under the control of a microcontroller unit. Here, we adopted a differential amplification and low-pass filter circuit to process the initial sensor voltage signal. Simultaneously, the signals are sent to the software on the computer for further analyzing. Method According to China‘s national indoor air quality standard, combustible gas detection standard and the detection range of MOS sensors, the concentration levels of target gases were set as Fig.(B).In this study, the actual concentrations of different gases in the mixed gas were calculated by the gas blender. The flow rate was set to 1000 mL/min constantly. The experiment process consists of several parts. First, the sensor array was preheated for more than 30 minutes in advance and cleaned in standard air for nearly 30 minutes until the voltage of each sensor was below the corresponding threshold. Next, gas blender injected the mixed gas sample into the gas chamber at a constant flow rate for 5 minutes. In the meantime, the feature parameters of each sensor’s response were extracted and calculated by the MCU in real time. Finally, the sensor array was cleaned for 5 minutes. The extracted features including baseline, maximum positive slope, maximum, peak area, temperature and humidity were all considered in the LDA model. A total of 316 samples were collected. 183 of them contain formaldehyde ranging randomly from 1-40 ppm. In addition, 90% samples for training, 10% for testing. Results and Conclusions During model training, the LDA model can effectively distinguish different levels with or without formaldehyde for both target gases (Fig.(C), (D)). The testing accuracies of LDA model for carbon monoxide and methane without formaldehyde samples are 91.89% and 97.30%, respectively. When there is formaldehyde interference, the accuracies are 80.95% and 92.06%, respectively. Therefore, the interference of formaldehyde has a greater impact on the identification of carbon monoxide. Besides, it is worth noting that carbon monoxide is difficult to distinguish at lower concentration levels, while methane is difficult to distinguish at higher levels. This may be due to the actual detection concentration is close to the upper or lower detection limits of the sensor, causing the sensor to be insensitive at lower levels and too saturated at higher levels. But this study still proves that the system can well meet the requirements of the detection of harmful gases in the interference environment. Figure 1

Oxidation and Characterization of Low-Concentration Gas in a High-Temperature Reactor

To achieve the efficient utilization of low-concentration mine gas, reduce resource waste and alleviate environmental pollution, the high-temperature oxidation of low-concentration gas at a concentration range of 1.00% to 1.50%, which is directly discharged into the atmosphere during coal mine production, was carried out to recover heat for reuse. The gas oxidation equipment was improved for the heating process and the safety of low-concentration gas oxidation under a high-temperature environment was evaluated. The experimental results showed that the reactor could provide a 1000 °C high-temperature oxidation environment for gas oxidation after installing high-temperature resistant ceramics. The pressure variation curves of the reactor with air and different concentrations of gas were similar. Due to the thermal expansion, the air pressure slightly increased and then returned to normal pressure. In contrast, the low-concentration gas exhibited a stable pressure response in the high-temperature environment of 1000 °C. The outlet pressure was significantly greater than the inlet pressure, and the pressure difference between the inlet and outlet exhibited a trend to increase with the gas concentration. The minimum pressure difference was 4 kPa (air) and the maximum was 11 kPa (1.50% gas). The explosion limit varied with the temperature and the blend of oxidation products. The ratio of measured gas pressure to air pressure after oxidation was below the explosion criterion, indicating that the measured concentration of gas is still safe after the shift of the explosion limit, which provides a safe concentration range for the efficient use of low-concentration gas in the future.

Wireless sensor network based fire monitoring in underground coal mines: A fuzzy logic approach

Abstract Mine fire is one of the major hazards in underground coal mines. Continuous monitoring of underground coal mines for early detection of fires is very much important from the mines safety point of view. In recent years, wireless sensor network (WSN) has gained enormous attention for monitoring of underground coal mines. In this technique, the sensor nodes are deployed in coal mines for collecting various environmental data, such as temperature, relative humidity, concentrations of different gases, etc. and send these data to a base station (sink) directly connected to the ground monitoring centre. Since the monitoring data are uncertain and imprecise in nature, it is important to interpret and analyze the data for taking measures for preventing fire hazard. In this paper, we propose a WSN-based fire monitoring system for underground coal mines using fuzzy logic approach for enhancing the reliability in decision-making process to alleviate the mine fire hazard. Mamdani fuzzy inference system is used as fuzzy model for taking real-time decisions on monitoring data. The system is extensively simulated using Fuzzy Logic Toolbox in MATLAB. The simulation results revealed that the proposed system is more reliable and responsive to fire hazard as compared to the offline monitoring system used in underground coal mines.

Synthesis and properties of Pr3+-doped ZnO nanowires

Well-aligned Pr3+-doped ZnO nanowires (NWs) were obtained by chemical vapor deposition (CVD) method without catalysts on silicon substrates by vapor-solid process. The length and diameter of ZnO NWs were 1–2 μm and 50–100 nm, respectively. X-ray diffraction (XRD), scanning electron microscopy (SEM), atomic force microscopy (AFM), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS) have been used to characterize the obtained Pr3+-doped ZnO nanowires. The concentration of dopant in the synthesized nanowires was 0.7 at %, according to EDX measurements. XPS was also used to characterize the samples and to confirm their composition. Pr3+-doped ZnO sensors were fabricated using a focused ion beam (FIB) with Au/Ti electrodes. A nanosensor is invented from an individual zinc oxide nanowire (80 nm in diameter). Response characteristics of the single nanowire towards dinitrogen monoxide (N2O), hydrogen (H2), ammonia (NH3) were examined at room temperature at different concentrations of gases.

Experimental estimation of the effect of contact condensation of steam–gas mixture on VVER passive safety systems operation

Results of experimental study of the effects of contact condensation of steam–gas mixture on the operation of VVER passive safety systems and steam generator in emergency condensation mode are presented. Contact condensation takes place when subcooled liquid is supplied in the accumulator tank of VVER reactor auxiliary passive core flooding system (the HA-2 system) in the presence of accumulated non-condensable gases. Water supplied to hydro accumulators can be used for increasing the operating time of VVER steam generator in the emergency condensation mode and for ensuring core cooling during longer time. Low liquid outlet velocity (less than 1m/s) caused by the necessity to ensure safety systems operation in passive mode constitutes the distinguishing feature of the investigated processes.Experiments were conducted on the test facility with parameters typical for the primary cooling loop of the reactor facility 24h after the accident initiation for different concentrations of gas in the steam–gas mixture. Nitrogen and helium, which replaces hydrogen for the purposes of safety assurance, were used as the non-condensable gases. It was established according to the results of experiments that the increase of concentration of non-condensable gas within the volume of HA-2 hydro accumulator model up to 45% leads to the reduction of intensity of contact condensation of steam from the steam–gas mixture by ∼29% in the experiment with nitrogen and by ∼57% in the experiment with helium. The obtained experimental data can be used for numerical simulation of emergency processes in the VVER reactor facility during operation of passive safety systems taking into account the removal of steam–gas mixture from steam generator by supplying the subcooled liquid jet into the volume HA-2 accumulator tanks.

Shock Compression Hydride and Deuteride Vanadium Atoms with Different Concentrations of Gases

Shock Compression Hydride and Deuteride Vanadium Atoms with Different Concentrations of Gases