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http://dx.doi.org/10.9713/kcer.2020.58.2.248

A Study on Predictive Models based on the Machine Learning for Evaluating the Extent of Hazardous Zone of Explosive Gases  

Jung, Yong Jae (Department of Safety Engineering, Pukyong National University)
Lee, Chang Jun (Department of Safety Engineering, Pukyong National University)
Publication Information
Korean Chemical Engineering Research / v.58, no.2, 2020 , pp. 248-256 More about this Journal
Abstract
In this study, predictive models based on machine learning for evaluating the extent of hazardous zone of explosive gases are developed. They are able to provide important guidelines for installing the explosion proof apparatus. 1,200 research data sets including 12 combustible gases and their extents of hazardous zone are generated to train predictive models. The extent of hazardous zone is set to an output variable and 12 variables affecting an output are set as input variables. Multiple linear regression, principal component regression, and artificial neural network are employed to train predictive models. Mean absolute percentage errors of multiple linear regression, principal component regression, and artificial neural network are 44.2%, 49.3%, and 5.7% and root mean square errors are 1.389m, 1.602m, and 0.203 m respectively. Therefore, it can be concluded that the artificial neural network shows the best performance. This model can be easily used to evaluate the extent of hazardous zone for explosive gases.
Keywords
Extent of hazardous zone of explosive gases; Multiple linear regression; Principal component regression; artificial neural network;
Citations & Related Records
Times Cited By KSCI : 17  (Citation Analysis)
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