Objective: Volcanic eruptions are a serious threat to the environment. In order to assess more accurately the state of a volcanic zone, spatially distributed measurements are required. Methodology: An electronic nose (eNose), a quadcopter drone with gas, temperature, and humidity sensors was developed. The drone was assembled with 3D printed parts and tested for properties like structural rigidity. The eNose samples gases, manages a sensor array, acquires data, extracts features, and classifies them with suitable classification algorithms. Results: The eNose drone system provides a versatile technology for autonomous monitoring of diverse environments. A logarithmic calibration curve was observed for the CO sensor. Conclusions: The implementation of a eNose drone system and its application to the detection and study of gases in volcanic areas would be innovative in Argentina. The system can access remote dangerous areas and is versatile. Different gas sensors like H2S or SO2 can be added.
Introduction Volcanic eruptions are a serious threat to the environment. The volcanic ash can contaminate water, vegetation, livestock and people. The Andes or Andean Mountains are the longest continental mountain range in the world, with a length of 8500 km. and presents different volcanoes [1]. In our work group the gas emissions of the Peteroa volcano are analyzed. In order to assess more accurately the state of a volcanic zone, spatially distributed measurements are required. Consequently, it is necessary to have an unmanned aerial vehicle to obtain several measurement points. An Electronic Nose (eNose) and a quadcopter drone equipped with gas sensors for CO2, temperature and humidity were developed [2][3]. The Dron eNose system provides a versatile technology for autonomous monitoring of diverse environments. The use of this device minimizes the risks of exposure to dangerous compounds for people and allows exploring inaccessible areas. The advantage of this system, over drones sold in the market (provided with proprietary software), lies in the possibility of adding accessories, which gives to it more adaptability. In this work, the complete structure of the quadcopter was manufactured using a 3D printer. To obtain a robust frame, parts were printed with filaments composed of different materials. Motors and propellers were selected according to the drone load capacity, the sensors, the stability control and the teletransmission system. The system is very versatile and different gas sensors can be added such as for detect SO2 and H2S. The flight can be controlled manually by remote control, or autonomously by a programmed flight plan. Method The implemented methodology was aimed at: 1) developing a drone (mobile robot) with an eNose system carrying capacity; 2) the testing of analytical capabilities of the assembled system by conducting studies in semi-field conditions (small spaces under controlled conditions). Once these objectives have been met, an area or region of interest may be explored to sample pollutants and to obtain their distribution map. Results in different project stages contribute to the development of new technologies relevant to environmental chemistry Drone structure parts were manufactured using a 3D printer. Drone arms rigidity tests were performed. The weights of each arm must be constant, the tubular arms oscillate less than the rectangular ones, so the former was chosen. This improved the structural rigidity of the drone. The electronic controller had to compensate for vibrations, therefore, it was essential to have more rigidity. The selected design allows to be printed without support material, and can accommodate a 3 cells 6000 mAh or 4 cells 4200 mAh LiPo battery. In addition, it is possible to put a camera and / or sensors in their compartments. Four Readytosky model 2212 of 920kv motors and suitable propellers were selected. A Pixhawk PX4 Autopilot PIX 2.4.8 32-bit flight controller was chosen because of its benefits: it has a real-time operating system (RTOS), multiple communication interfaces, a backup power system, manual and automatic flight modes and the ability to record flight data on an SD card. The chosen kit also includes a GPS module and telemetry. The connection with the drone is done through a Radiolink AT9S 2.4G 9CH transmitter. The gas detection system (eNose) to be mounted on the drone consist of a plate containing the gas sensors, the electronics for sensors measurements and an Arduino Nano 3.0 microcontroller. It processes the obtained signals. Data is analyzed on a PC with user friendly pattern recognition software which has a GUI that allows to pick between the following algorithms: PCA, Fisher and linear classifiers [4][5]. The sensor array consists of 6 Microsens MOS type gas sensors. Part of this arrangement consisted of three MSGS-3000i sensors. The MSGS-3000i Gas Sensor detects carbon monoxide, hydrocarbons, ethanol and volatile organic compounds emissions. The remaining part of the arrangement consisted of three MSGS-3007i sensors. The MSGS-3007i detects NO2 in 0.05 to 5 ppm range. Due to the integrated sockets that the eNose possess, sensors could be easily changed to detect different compounds. The assembled drone is shown in Fig. 1. Results and Conclusions The implementation of a drone eNose system would be one of the first in Argentina. It has the advantage of being a flexible system. This project was carried out with the objective of studying the negative environmental impacts arisen from volcanic eruptions. The technological innovations of this project could be applied to other fields of monitoring. For example, control of high risk of toxicity areas due to the emission of gaseous pollutants, inaccessible for humans. References [1]. T. Veblen, K. R. Young, & A. R. Orme, ed. The Physical Geography of South America. Oxford University Press.(2007) p. 12. ISBN 978-0-19-531341-3. [2] J. Vorobioff, E. Videla, N. Boggio, O.D. Salomón, A. Lamagna, C.A. Rinaldi. Sensors and Actuators B: Chemical, 257 (2018), pág. 200-206, ISSN 0925-4005 [3] P. Neumann, S. Asadi, J. H. Schiller, A. J. Lilienthal, and M. Bartholmai, IEEE Robotics and Automation Magazine, 19 (2012), pág. 50-61. [4] A.R. Webb, K.D.Copsey, Statistical Pattern Recognition (2011), Ed. 3, Wiley [5] S. M. Scott, D. James, Z. Ali, Data analysis for electronic nose systems, Microchem. (2007) Acta 156, pp. 183–207. Fig. 1. Assembled dron Figure 1
La tesis acerca de la existencia de “las dos culturas” entre quienes gozan de una ecuación formal, propuesta en la segunda mitad del siglo XX por C. P. Snow (1959; 1964), tiene su precedente filosófico en los siglos XVI y XVII en la obra de Francis Bacon. Los problemas abordados por ambos autores muestran la relevancia y vigencia de los Estudios sobre Ciencia, Tecnología y Sociedad (CTS) para el siglo XXI.
Introduction Volcanic eruptions are a serious threat to the environment. The volcanic ash can contaminate water, vegetation, livestock and people. The Andes or Andean Mountains are the longest continental mountain range in the world, with a length of 8500 km. and presents different volcanoes [1]. In our work group the gas emissions of the Peteroa volcano are analyzed. In order to assess more accurately the state of a volcanic zone, spatially distributed measurements are required. Consequently, it is necessary to have an unmanned aerial vehicle to obtain several measurement points. An Electronic Nose (eNose) and a quadcopter drone equipped with gas sensors for CO2, temperature and humidity were developed [2][3]. The Dron eNose system provides a versatile technology for autonomous monitoring of diverse environments. The use of this device minimizes the risks of exposure to dangerous compounds for people and allows exploring inaccessible areas. The advantage of this system, over drones sold in the market (provided with proprietary software), lies in the possibility of adding accessories, which gives to it more adaptability. In this work, the complete structure of the quadcopter was manufactured using a 3D printer. To obtain a robust frame, parts were printed with filaments composed of different materials. Motors and propellers were selected according to the drone load capacity, the sensors, the stability control and the teletransmission system. The system is very versatile and different gas sensors can be added such as for detect SO2 and H2S. The flight can be controlled manually by remote control, or autonomously by a programmed flight plan. Method The implemented methodology was aimed at: 1) developing a drone (mobile robot) with an eNose system carrying capacity; 2) the testing of analytical capabilities of the assembled system by conducting studies in semi-field conditions (small spaces under controlled conditions). Once these objectives have been met, an area or region of interest may be explored to sample pollutants and to obtain their distribution map. Results in different project stages contribute to the development of new technologies relevant to environmental chemistry Drone structure parts were manufactured using a 3D printer. Drone arms rigidity tests were performed. The weights of each arm must be constant, the tubular arms oscillate less than the rectangular ones, so the former was chosen. This improved the structural rigidity of the drone. The electronic controller had to compensate for vibrations, therefore, it was essential to have more rigidity. The selected design allows to be printed without support material, and can accommodate a 3 cells 6000 mAh or 4 cells 4200 mAh LiPo battery. In addition, it is possible to put a camera and / or sensors in their compartments. Four Readytosky model 2212 of 920kv motors and suitable propellers were selected. A Pixhawk PX4 Autopilot PIX 2.4.8 32-bit flight controller was chosen because of its benefits: it has a real-time operating system (RTOS), multiple communication interfaces, a backup power system, manual and automatic flight modes and the ability to record flight data on an SD card. The chosen kit also includes a GPS module and telemetry. The connection with the drone is done through a Radiolink AT9S 2.4G 9CH transmitter. The gas detection system (eNose) to be mounted on the drone consist of a plate containing the gas sensors, the electronics for sensors measurements and an Arduino Nano 3.0 microcontroller. It processes the obtained signals. Data is analyzed on a PC with user friendly pattern recognition software which has a GUI that allows to pick between the following algorithms: PCA, Fisher and linear classifiers [4][5]. The sensor array consists of 6 Microsens MOS type gas sensors. Part of this arrangement consisted of three MSGS-3000i sensors. The MSGS-3000i Gas Sensor detects carbon monoxide, hydrocarbons, ethanol and volatile organic compounds emissions. The remaining part of the arrangement consisted of three MSGS-3007i sensors. The MSGS-3007i detects NO2 in 0.05 to 5 ppm range. Due to the integrated sockets that the eNose possess, sensors could be easily changed to detect different compounds. The assembled drone is shown in Fig. 1. Results and Conclusions The implementation of a drone eNose system would be one of the first in Argentina. It has the advantage of being a flexible system. This project was carried out with the objective of studying the negative environmental impacts arisen from volcanic eruptions. The technological innovations of this project could be applied to other fields of monitoring. For example, control of high risk of toxicity areas due to the emission of gaseous pollutants, inaccessible for humans. References [1]. T. Veblen, K. R. Young, & A. R. Orme, ed. The Physical Geography of South America. Oxford University Press.(2007) p. 12. ISBN 978-0-19-531341-3. [2] J. Vorobioff, E. Videla, N. Boggio, O.D. Salomón, A. Lamagna, C.A. Rinaldi. Sensors and Actuators B: Chemical, 257 (2018), pág. 200-206, ISSN 0925-4005 [3] P. Neumann, S. Asadi, J. H. Schiller, A. J. Lilienthal, and M. Bartholmai, IEEE Robotics and Automation Magazine, 19 (2012), pág. 50-61. [4] A.R. Webb, K.D.Copsey, Statistical Pattern Recognition (2011), Ed. 3, Wiley [5] S. M. Scott, D. James, Z. Ali, Data analysis for electronic nose systems, Microchem. (2007) Acta 156, pp. 183–207. Figure 1
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