- Title
- Predicting Deflagration and Detonation in Detonation Tube
- Creator
- Namazi, Samira; Brankovic, Ljiljana; Moghtaderi, Behdad; Zanganeh, Jafar
- Relation
- Applications of Artificial Intelligence and Machine Learning . Proceeding of the Applications of Artificial Intelligence and Machine Learning, Vol. 925 (Greater Noida, India 16 September, 2022) p. 529-543
- Publisher Link
- http://dx.doi.org/10.1007/978-981-19-4831-2_43
- Publisher
- Springer
- Resource Type
- conference paper
- Date
- 2022
- Description
- In order to better understand conditions that lead to methane explosions in underground coal mines, we apply machine learning to data collected in an industrial scale research project carried out at the University of Newcastle, Australia, 2014–2018 (VAM Abatement Safety Project). We present a comparison of five different methods (Decision Tree, Random Forest, Naïve Bayes, AdaBoostM1, and SVM with SMO) to classify the maximum pressure and maximum flame velocity in order to predict detonation and inform the design of capture ducts. All methods are evaluated with a tenfold cross validation technique. We found that tree-based classification methods provide the most accurate prediction of dangerous pressure and supersonic velocity.
- Subject
- classification; coal mines; data mining; decision tree; flame velocity; machine learning
- Identifier
- http://hdl.handle.net/1959.13/1490096
- Identifier
- uon:52839
- Identifier
- ISBN:9789811948305
- Language
- eng
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