Microelectronic Transducer of Gas Concentration based on MOSFET with an Active Inductive Element

2019 ◽  
Vol 1 (4) ◽  
pp. 239-243
Author(s):  
Alexandr OSADCHUK
Keyword(s):  
Gas Concentration ◽  
Inductive Element

ANALISIS PENGUKURAN KONSENTRASI KARBONMONOKSIDA (CO) PADA BREATHING ZONE PETUGAS PARKIR BASEMENT MALL KOTA BANDUNG

2019 ◽  
Author(s):  
Gede H Cahyana
Keyword(s):  
Air Pollution ◽  
Indoor Air ◽  
Indoor Air Pollution ◽  
Sampling Method ◽  
Breathing Zone ◽  
Gas Concentration ◽  
Average Value ◽  
Co Concentration ◽  
Personal Dosimeter ◽  
Co Gas

Indoor air pollution in closed room is one of the air pollution that gives serious threats to human health. One of them come from vehicle gas emissions in closed parking area. This research identifies and analyses CO concentration measured in Mall X parking man’s breathing zone with closed parking area and in Mall Y semi-opened parking area. CO measurement carried out by passive sampling method using Personal Dosimeter Tubes. Measurement result of CO gas concentration to parking man’s breathing zone in Mall X was 25 – 81,25 ppm with average value in 50 ± 26,15 ppm. Meanwhile CO gas concentration in Mall Y gave result 3,13 – 12,5 ppm with average value in 7,88 ± 4,36 ppm. Correlation value between CO concentration and its intake in Mall X area was 0,9983, meanwhile correlation value between CO concentration and its intake in Mall Y area was 0,9903. It was concluded that CO gas concentration measured in parking man’s breathing zone influenced the differences of CO intake value in significance value.

scholarly journals Intelligent Mobile Wireless Network for Toxic Gas Cloud Monitoring and Tracking

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3625
Author(s):  
Mateusz Krzysztoń ◽  
Ewa Niewiadomska-Szynkiewicz
Keyword(s):  
Data Exchange ◽  
Mobility Model ◽  
Model Parameters ◽  
Toxic Substances ◽  
Cloud Detection ◽  
Gas Dispersion ◽  
Gas Concentration ◽  
Cloud Monitoring ◽  
Optimal Values ◽  
Gas Cloud

Intelligent wireless networks that comprise self-organizing autonomous vehicles equipped with punctual sensors and radio modules support many hostile and harsh environment monitoring systems. This work’s contribution shows the benefits of applying such networks to estimate clouds’ boundaries created by hazardous toxic substances heavier than air when accidentally released into the atmosphere. The paper addresses issues concerning sensing networks’ design, focussing on a computing scheme for online motion trajectory calculation and data exchange. A three-stage approach that incorporates three algorithms for sensing devices’ displacement calculation in a collaborative network according to the current task, namely exploration and gas cloud detection, boundary detection and estimation, and tracking the evolving cloud, is presented. A network connectivity-maintaining virtual force mobility model is used to calculate subsequent sensor positions, and multi-hop communication is used for data exchange. The main focus is on the efficient tracking of the cloud boundary. The proposed sensing scheme is sensitive to crucial mobility model parameters. The paper presents five procedures for calculating the optimal values of these parameters. In contrast to widely used techniques, the presented approach to gas cloud monitoring does not calculate sensors’ displacements based on exact values of gas concentration and concentration gradients. The sensor readings are reduced to two values: the gas concentration below or greater than the safe value. The utility and efficiency of the presented method were justified through extensive simulations, giving encouraging results. The test cases were carried out on several scenarios with regular and irregular shapes of clouds generated using a widely used box model that describes the heavy gas dispersion in the atmospheric air. The simulation results demonstrate that using only a rough measurement indicating that the threshold concentration value was exceeded can detect and efficiently track a gas cloud boundary. This makes the sensing system less sensitive to the quality of the gas concentration measurement. Thus, it can be easily used to detect real phenomena. Significant results are recommendations on selecting procedures for computing mobility model parameters while tracking clouds with different shapes and determining optimal values of these parameters in convex and nonconvex cloud boundaries.

scholarly journals Hydrogen gas concentration measurement in small area using raman lidar measurement technnology

2018 ◽  
Vol 176 ◽  
pp. 01019 ◽  
Author(s):  
Sachiyo Sugimoto ◽  
Ippei Asahi ◽  
Tatuso Shiina
Keyword(s):  
Small Area ◽  
Gas Flow ◽  
Hydrogen Gas ◽  
Measurement Technology ◽  
Lidar Measurement ◽  
Measurement Point ◽  
Raman Lidar ◽  
Gas Concentration ◽  
Scattering Light ◽  
Temporal And Spatial

When change of hydrogen(H2) gas concentration in a certain point is measured, non-contact measurement technology with high temporal and spatial resolution is necessary. In this study, H2 concentration in the small area of <1cm2 under the gas flow was measured by using a Raman lidar. Raman scattering light at the measurement point of 750mm ahead was detected by the Raman lidar. As a result, it was proved that the H2 concentration of more than 100ppm could be successfully measured.

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