Plume Detection System Based Internet of Things

  • I Nyoman Susila Astraning Utama Universitas Bumigora
  • Sirojul Hadi Universitas Bumigora https://orcid.org/0000-0001-6387-7369
  • I Putu Hariyadi Universitas Bumigora
Keywords: Plume detection, gas leak, internet of things, LPG gas, Alcohol evaporated gas

Abstract

Security is one of the important aspects in a system or environment. Residential, office, tourist and industrial areas are places that are prone to fires because they contain flammable objects. Slow handling when a gas leak occurs can trigger a fire. The solution that can be used to minimize the occurrence of fires is to build tools that work to monitor the condition of the room or environment that is prone to leakage of gas or other flammable liquids. The design and manufacture of a system to detect LPG and alcohol gas leaks can be useful for providing information in the event of a gas or alcohol leak so that it can be handled quickly and minimize fire damage. This system combines an plume detection system with an internet of things system so that it can provide information when a gas or flammable liquid leak occurs. The gas leak information is sent as a notification to the telegram from the operator. The design and manufacture of this system uses the Waterfall methodology with the following stages: analyzing (covering the need for system creation), system design (including designing electronic circuits and web monitoring interfaces), implementing system design and testing the system as a whole. The result of this research is that an electronic detection system has been successfully built that can distinguish gases and can provide information via telegram and web if gas is detected in the sensor environment. In the LPG gas leak test, the results show that the characteristics of LPG gas, namely the sensor output voltage, have an average of 4.17 volts with an average Part Per Million (PPM) of 8340 and the characteristics of alcohol gas, namely the sensor output voltage, have an average of 0, 13 volts with an average Part Per Million (PPM) of 254.

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Published
2022-03-02
How to Cite
[1]
I. N. Utama, S. Hadi, and I. P. Hariyadi, “Plume Detection System Based Internet of Things”, International Journal of Engineering and Computer Science Applications (IJECSA), vol. 1, no. 1, pp. 19-26, Mar. 2022.
Section
Articles

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