A method for real-time estimation of gas leakage flow from leakage source based on point detection data. (August 2022)

• Record Type: Journal Article
• Authors: Peng, Lan; Huang, Xianjia; Chen, Jianghua; Yang, Ping; Xing, Chaoliang; Zhao, Chunyang
• Is Part Of: Journal of loss prevention in the process industries. Volume 78(2022)
• Publication Date: 2022-08
• Subjects: Chemical gas leakage -- Support vector regression -- Leakage flow -- Source tracking -- Inverse problem; Chemical industries -- Safety measures -- Periodicals; 660.2804
• DOI: 10.1016/j.jlp.2022.104822
• Languages: English
• ISSNs: 0950-4230

Abstract

Abstract: Chemical leakage accidents may lead to significant hazards such as fire, explosions and the release of toxic gases. And the estimation of real-time accident hazards is of critical importance for emergency response. In this study, a method was proposed to predict the gas flow in real-time based on the simulated database. Based on datasets generated by numerical computations, a machine-learning algorithm was trained and used to then be validated by data from real-world scenarios. Compared with the full-scale experimental data, for the scenarios in which the relative error of the simulation was within the range of 0%–25%, the relative error of the developed model prediction was 9.8%. When the error of the simulation was within the range of 0%–25%, the machine learning model trained by simulated data can predict the gas leakage in real world with high accuracy. Highlights: A method was proposed to predict real-time gas flow based on the simulated database. Prediction error of the developed model using machine learning can reached 9.8%. Accuracy of the simulated database determine the reliability of the developed model.