Gas Sensor Network Research Articles

Application of Fiber-Optical Gas Sensor Network in Gas Detection Based on Digital Signal Processing Algorithm

Application of Fiber-Optical Gas Sensor Network in Gas Detection Based on Digital Signal Processing Algorithm

Development of an energy-harvesting toxic and combustible gas sensor for oil and gas industries

Wireless gas leakage detection systems are necessary in the oil and gas industry (OGI). This is because they are more flexible in terms of installation and maintenance. However, wireless gas sensors have an average life span of 2 years. This is because of the huge energy consumption of the gas sensors. In this paper, a wireless gas sensor network (WGSN) combining adaptive sleep cycle and energy harvesting is developed. The system is able to reach a life span of 5.5 years with the help of solar panels.

MLP Neural Network Based Gas Classification System on Zynq SoC

Systems based on wireless gas sensor networks offer a powerful tool to observe and analyze data in complex environments over long monitoring periods. Since the reliability of sensors is very important in those systems, gas classification is a critical process within the gas safety precautions. A gas classification system has to react fast in order to take essential actions in the case of fault detection. This paper proposes a low latency real-time gas classification service system, which uses a multi-layer perceptron (MLP) artificial neural network to detect and classify the gas sensor data. An accurate MLP is developed to work with the data set obtained from an array of tin oxide (SnO2) gas sensor, based on convex micro hotplates. The overall system acquires the gas sensor data through radio-frequency identification (RFID), and processes the sensor data with the proposed MLP classifier implemented on a system on chip (SoC) platform from Xilinx. Hardware implementation of the classifier is optimized to achieve very low latency for real-time application. The proposed architecture has been implemented on a ZYNQ SoC using fixed-point format and the achieved results have shown that an accuracy of 97.4% has been obtained.

스마트폰 기반 산소 결핍 모니터링 장치

O2-deficiency related accidents occur every year and the most effective way to prevent them is to measure O2 concentration in air with a properly-calibrated O2 monitoring device before entering low-O2 areas. An electro-chemical sensor, Texas Instrument gas platform, and iPhone are used to construct a smartphone-based O2 monitoring device. The smartphone based O2 measuring approach offers advantages of small size, accessibility, internet-connectivity, and programmability in comparison to conventional O2 measuring devices. Multiple gas sensors can be conveniently interfaced to single smartphone, allowing for creating a network of gas sensors distributed across workplaces and remote monitoring via existing mobile communication network. To check proper function of the O2 monitoring device the sensor was exposed to shallow and deep human breaths. The readings decreased immediately after being exposed to exhalation and recovered during inhalation to a calibrated level of 20.9%. When readings decreased below a preset warning value of 19.5%, a low O2 warning was successfully activated on the smartphone.

English

Analysing the stability of the building is needed in measurement process for all buildings in the cities. Earthquake damage & structural behavior of the building can be monitoring using low power sensor network. This paper describes an applications of wireless sensor network in monitoring and controlling of energy in residential and commercial buildings. In addition to that we are developing fire sensor, gas sensor network to detect and to alert the people who were inside the buildings, alert message to nearby hospitals, ambulances, police stations at the time of emergency cases like earthquake, firing, gas leakage occurring in the building. Accelerometer, vibration sensors are used to monitor and detect the damages occurred in the building. Communication is established between transmitter and receiver base station is through GSM protocol. In this project the receiving base station which consists of mobile phone. Inside the mobile phone the GSM SIM300 is inserted.

Wireless Gas Sensor Network for the Spatially Resolved Measurement of Hazardous Gases in Case of a Disaster

A sensor network with sensor nodes for the spatially resolved measurement of hazardous gases and the reconstruction of the gas distribution in case of a disaster is presented. After the distribution of the sensor nodes in a disaster environment, e.g. with unmanned ground vehicles or unmanned aircraft systems, the sensor nodes work autonomously. To establish a pollution map, showing for example hazardous gas distribution, three main tasks were carried out: the detection of toxic gases with gas sensors, the low-cost indoor localization of the sensor nodes and sensor data fusion and reconstruction of the distributed physical phenomenon. The aim is a complete decentralization where measurements and data evaluation take place in the network. We use ultrasonic time of flight measurements for indoor localization and a two-step method for the reconstruction of the gas distribution in a room.

Research on Gas Emission Distribution in Coalface Based on Trend Surface Polynomial Analysis

In this paper, gas intensity data collected from coal mine with wireless gas sensor network has been analyzed for the understanding of gas distribution at mining work place. The concentration parameters acquired at different position were dealt with polynomial equation surface fitting based on trend surface analysis, and we built the trend face analysis model with calculation results. Diagram of gas distribution trend and the isocline chart of gas concentration were achieved with the model. The diagram and chart present the distribution of gas concentration in different direction at coal working place and the gas accumulation at top corner from the ventilate eddy. Then, we discussed the effects of airflow and cutting coal to gas distribution. The research results are significant for monitoring and controlling of gas to avoid explosion accident the coal mining.

Optimal swarm formation for odor plume finding.

This paper presents an analytical approach to the problem of odor plume finding by a network of swarm robotic gas sensors, and finds an optimal configuration for them, given a set of assumptions. Considering cross-wind movement for the swarm, we found that the best spatial formation of robots in finding odor plumes is diagonal line configuration with equal distance between each pair of neighboring robots. We show that the distance between neighboring pairs in the line topology depends mainly on the wind speed and the environmental conditions, whereas, the number of robots and the swarm's crosswind movement distance do not show significant impact on optimal configurations. These solutions were analyzed and verified by simulations and experimentally validated in a reduced scale realistic environment using a set of mobile robots.

The Random Sampling Method for Gas Sensing Signal Based on Compressed Sensing

This paper presents a random sampling method for gas signal,which is based on the principle called compressed sensing. This method is suitable for gas sensor network, and it is conducive to the realization of low power, low cost of the gas sensor network. According to the characteristics of the output signal of gas sensor, we design the random sampling method which acquire the useful information in signal. Using compressed sensing recovery algorithm to reconstruct signal,we finally fulfill complete signal collection. The simulation results show that the method we proposed can achieve signal acquisition with a sampling frequency less than 1/5 of standard sampling frequency.

Optimal spatial formation of swarm robotic gas sensors in odor plume finding

Finding the best spatial formation of stationary gas sensors in detection of odor clues is the first step of searching for olfactory targets in a given space using a swarm of robots. Considering no movement for a network of gas sensors, this paper formulates the problem of odor plume detection and analytically finds the optimal spatial configuration of the sensors for plume detection, given a set of assumptions. This solution was analyzed and verified by simulations and finally experimentally validated in a reduced scale realistic environment using a set of Roomba-based mobile robots.

Safe Delivery of Sensed Data in Wireless Sensor Networks for Gas Leak Detection: a Boiler Facility Scenario

Abstract In this work we share our experience in the deployment of a Wireless Gas Sensor Network (WGSN) in an operational boiler facility. Our setup is based on a state-of-the-art WGSN platform which ensures reliable gas detection and long–term operation of the network. We first describe the deployment of the network and then evaluate its wireless links using Received Signal Strenght Indicator (RSSI) and Link Quality Indicator (LQI) metrics.

Development of wireless sensor network for combustible gas monitoring

This paper describes the development and the characterization of a wireless gas sensor network (WGSN) for the detection of combustible or explosive gases. The WGSN consists of a sensor node, a relay node, a network coordinator, and a wireless actuator. The sensor node attains early gas detection using an on board 2D semiconductor sensor. Because the sensor consumes a substantial amount of power, which negatively affects the node lifetime, we employ a pulse heating profile to achieve significant energy savings. The relay node receives and forwards traffic from sensor nodes towards the network coordinator and vice versa. When an emergency is detected, the network coordinator alarms an operator through the GSM/GPRS or Ethernet network, and may autonomously control the source of gas emission through the wireless actuator. Our experimental results demonstrate how to determine the optimal temperature of the sensor's sensitive layer for methane detection, show the response time of the sensor to various gases, and evaluate the power consumption of the sensor node. The demonstrated WGSN could be used for a wide range of gas monitoring applications.

The detection of evaporating hazardous material released from moving sources using a gas sensor network

Sensor information resulting from distributed locations and/or a multitude of instruments and heterogeneous sensors can increase the reliability of safety and security applications. A gas-sensing platform was developed, communicating via a wireless sensor network based on IEEE 802.15.4 and/or Ethernet. Data from this network are aggregated via a central server feeding its information via TCP/IP into subsequent data fusion software. The usually limited spatio-temporal resolution of chemical sensors can be compensated by space-time sensor data fusion. Sensor nodes have been equipped with metal oxide gas sensors in order to identify hazardous materials [1]. A number of these nodes have then been placed alongside a corridor people had to pass to enter a restricted area. The data from the chemical sensors were fused with tracking data from laser range scanners and video systems. It has been shown that it was possible to allocate a chemical contamination of one individual within a group of moving people and discriminate between various fire accelerating fuels and solvents. This was successfully demonstrated outside the laboratory with a test corridor build in a tent during a military tech-demo in Eckernförde, Germany.

Detection of volatile organic compounds by using a single temperature-modulatedSnO2 gas sensor and artificial neural network

A dynamic measurement method is described for the rapid identification and determination of volatileorganic compounds (VOCs) in ambient air. For the qualitative recognition of VOCs, only a singleSnO2-based gas sensor operating in a rectangular temperature-modulation mode isrequired. The working temperature of the sensor was modulated between 250 and300 °C and its dynamic responses to different concentrations of propane-2-ol, acetyl acetone andethanol vapor were measured. The discrete wavelet transform (DWT) was used to extractimportant features from the sensor response. These features were then input to a (neural)pattern recognition algorithm. The species considered can be discriminated with a 100%success rate using a back propagation network and the concentrations of the organic vaporcan also be accurately predicted.

Gas sensor network for air-pollution monitoring

This paper describes the development of a gas sensor system to be used as a sensing node to form a dense real-time environmental monitoring network. Moreover, a new auto-calibration method is proposed to achieve the maintenance-free operation of the sensor network. The network connectivity can be used not only for data collection but also for the calibration and diagnosis of the sensors since the measured pollutant concentrations can be easily compared through the network with nearby sensors and governmental monitoring stations. Different pollutant concentrations are usually monitored at different sites. However, a case study on local NO 2 distribution has shown that there exists a special condition under which pollutant concentrations become low and uniform in a certain local area. The baseline of the gas sensor response can be adjusted in this special occasion using the pollutant concentration values reported from the neighboring environmental monitoring stations. The experimental result has shown that NO 2 concentration can be measured with sufficient accuracy by incorporating appropriate temperature and humidity compensation into calibration curves. Moreover, a case study on auto-calibration demonstrates its effectiveness in keeping the measurement accuracy of the sensor system in long-term operation.

Improvement of Measurement Accuracy in Environmental Monitoring System Based on Semiconductor Gas Sensor

There has been growing interest in developing gas sensor systems for environmental applications. The network of a large number of sensors deployed in the field is expected to enable monitoring of local air pollution problems in detail. Toward the development of a gas sensor network, this paper addresses the issue of accuracy in measuring NO2 concentration using a semiconductor gas sensor. Firstly, a sensor cell with a temperature control system is developed. Many gas sensors show variation in their response characteristics with the change in the ambient temperature. The experimental results on the sensor calibration show that the error in the measured NO2 concentration can be effectively reduced by stabilizing the temperature of the gas sensor cell. Secondly, a method for recalibrating the sensor system is discussed. It is shown by the field experiments that the sensor system can be recalibrated by placing the gas sensor system with a reference instrument and readjusting the calibration parameters to minimize the error in the measured concentrations. The proposed recalibration procedure will help to simplify the operation of a number of gas sensors deployed in the sensor network.

Tin dioxide gas sensor as a tool for atmospheric pollution monitoring: Problems and possibilities for improvements

The monitoring of atmospheric pollution needs network of gas sensors in order to increase the number of measuring points. For this goal, tin dioxide sensors have been evaluated. Their potentials and limitations (selectivity and stability), are presented via on-site experiments of urban pollution control. The study shows that some global information indicating mainly the traffic pollution can be obtained. The general problem of long-term stability and selectivity of SnO2 sensors is discussed. Some specific solutions are proposed, for example, a chemical treatment in the case of instability due to SO2. The problem of the dual response to oxidising and reducing gases is discussed, especially in regard to CO/NO2 detection. To solve this problem, the use of active filters is proposed. For example, a thin film of rhodium deposited above the sensing material allows to filter NO2. The use of a platinum filter has also been applied to improve the selectivity for the reducing gases. The results point out the difficulty to control this type of device in thin film technology. On the contrary, with thick films obtained by screen-printing, the possibility to separate HC from VOC (CH4 from CO and C2H5OH) is demonstrated. Some preliminary results concerning the use of a MnO2 filter are also presented in order to control the ozone reaction. The possibilities offered by signal processing are finally discussed using a multi-variable approach with 90 days aged sensors. AC measurements are exploited to improve the selectivity for the oxidizing gases NO2 and O3. The results obtained with a model built with atmospheric air as carrier gas appear promising.

Soil efflux and total emission rates of magmatic CO 2 at the Horseshoe Lake tree kill, Mammoth Mountain, California, 1995–1999

We report the results of eight soil CO 2 efflux surveys by the closed circulation chamber method at the Horseshoe Lake tree kill (HLTK) — the largest tree kill on Mammoth Mountain. The surveys were undertaken from 1995 to 1999 to constrain total HLTK CO 2 emissions and to evaluate occasional efflux surveys as a surveillance tool for the tree kills. HLTK effluxes range from 1 to >10,000 g m −2 day −1 (grams CO 2 per square meter per day); they are not normally distributed. Station efflux rates can vary by 7–35% during the course of the 8- to 16-h surveys. Disturbance of the upper 2 cm of ground surface causes effluxes to almost double. Semivariograms of efflux spatial covariance fit exponential or spherical models; they lack nugget effects. Efflux contour maps and total CO 2 emission rates based on exponential, spherical, and linear kriging models of survey data are nearly identical; similar results are also obtained with triangulation models, suggesting that the kriging models are not seriously distorted by the lack of normal efflux distributions. In addition, model estimates of total CO 2 emission rates are relatively insensitive to the measurement precision of the efflux rates and to the efflux value used to separate magmatic from forest soil sources of CO 2. Surveys since 1997 indicate that, contrary to earlier speculations, a termination of elevated CO 2 emissions at the HLTK is unlikely anytime soon. The HLTK CO 2 efflux anomaly fluctuated greatly in size and intensity throughout the 1995–1999 surveys but maintained a N–S elongation, presumably reflecting fault control of CO 2 transport from depth. Total CO 2 emission rates also fluctuated greatly, ranging from 46 to 136 t day −1 (metric tons CO 2 per day) and averaging 93 t day −1. The large inter-survey variations are caused primarily by external (meteorological) processes operating on time scales of hours to days. The externally caused variations can mask significant changes occurring at depth; a striking example is the masking of a degassing event generated at depth and detected by a soil gas sensor network in September 1997 while an efflux survey was in progress. Thus, occasional efflux surveys are not an altogether effective surveillance tool for the HLTK, and making them effective by greatly increasing their frequency may not be practical.

Experimental demonstration of a fiber-optic gas sensor network addressed by FMCW

We report on the use of frequency-modulated continuous-wave and wavelength modulation spectroscopy techniques for addressing a multipoint gas sensor network. A three-sensor network of ladder topology is experimentally demonstrated for the detection of acetylene gas. A minimum detectable concentration of 270 ppm//spl radic/Hz is obtained with 25-mm gas cells under atmospheric pressure which corresponds to a minimum detectable absorbance of 3.40/spl times/10/sup -4/. The crosstalk between the sensors is below -22 dB.