Autors: Dash, A., Bandopadhay, S., Samal, S.R., Poulkov, V. K.
Title: AI-Enabled IoT Framework for Leakage Detection and Its Consequence Prediction during External Transportation of LPG
Keywords: artificial intelligence; computational fluid dynamics (CFD)

Abstract: An accident during the transport of liquefied petroleum gas (LPG) via a tanker vehicle leads to the leakage of a flammable substance, causing devastation. In such a situation, the appropriate action with the shortest possible delay can minimize subsequent losses. However, the decision-making mechanism remains unable to detect the occurrence of an accident and evaluate its extent within the critical time. This paper proposes an automatic framework for leakage detection and its consequence prediction during the external transportation of LPG using artificial intelligence (AI) and the internet of things (IoT). An AI model is developed to predict the probable consequences of the accident in terms of the diameter of risk contours. An IoT framework is proposed in which the developed AI model is deployed in the edge device to detect any leakage of gas during transportation, to predict its probable consequences, and to report it to the remotely located disaster management team.

References

    Issue

    Sensors, vol. 23, issue 1417, 2023, Switzerland, Multidisciplinary Digital Publishing Institute (MDPI), DOI 10.3390/s23146473

    Copyright Multidisciplinary Digital Publishing Institute (MDPI)

    Цитирания (Citation/s):
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    Вид: статия в списание, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus