Prediction of gas explosion pressures: A machine learning algorithm based on KPCA and an optimized LSSVM |
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| Institution: | 1. Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming, Yunnan, 650500, China;2. Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming, Yunnan, 650500, China;3. Kunming Prospecting Design Institute of China Nonferrous Metals Industry Co., Ltd, Kunming, 650051, Yunnan, China;1. Beijing Institute of Technology, School of Mechatronical Engineering, Beijing, 100081, China;2. Zhejiang Sci-tech University, Graduate School, Hangzhou, Zhejiang, 310018, China;3. Nanjing University of Science and Technology, School of Chemical Engineering, Nanjing, Jiangsu, 210094, China;4. Army Engineering University of PLA, Field Engineering Institute, Nanjing, Jiangsu, 210007, China;5. Beijing Key Laboratory of Metro Fire and Passenger Transportation Safety, China Academy of Safety Science and Technology, Beijing, 100012, China;1. Guangdong Engineering Center for Structure Safety and Health Monitoring, Department of Civil and Environmental Engineering, Shantou University, Shantou, Guangdong Province, China;2. MOE Key Laboratory of Intelligent Manufacturing Technology, College of Engineering, Shantou University, Shantou, Guangdong Province, China;3. China Petroleum Pipeline Engineering Co.,Ltd., Langfang, Hebei Province, China;4. Department of Electronic and Information Engineering, Shantou University, Shantou, Guangdong Province, China;1. Guangzhou Institute of Chemistry, Chinese Academy of Sciences, Guangzhou, 510650, People''s Republic of China;2. Guangdong Provincial Key Laboratory of Organic Polymer Materials for Electronics, Guangzhou, 510650, People''s Republic of China;3. CAS Engineering Laboratory for Special Fine Chemicals, Guangzhou, 510650, People''s Republic of China;4. CASH GCC Shaoguan Research Institute of Advanced Materials, Nanxiong, 512400, People''s Republic of China;5. University of the Chinese Academy of Sciences, Beijing, 100049, People''s Republic of China;6. Incubator of Nanxiong CAS Co., Ltd., Nanxiong, 512400, People''s Republic of China;7. Management Committee of Shaoguan NanXiong Hi-tech Industry Development Zone, Nanxiong, 512400, People''s Republic of China;1. Institute of Safety Science and Engineering, South China University of Technology, Guangzhou, 510640, China;2. Department of Chemical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia;3. Institute of Ocean and Earth Sciences, C308, Institute for Advanced Studies Building, University of Malaya, 50603, Kuala Lumpur, Malaysia;4. Centre for Dryland Agriculture, Bayero University, P.M.B. 3011, Kano, Nigeria;1. Department of Safety Engineering, College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing, 100029, China;2. University Gustave Eiffel, UPEC, CNRS, Laboratory Multi Scale Modeling and Simulation, (MSME/UMR 8208), 5 bd Descartes, 77454, Marne-la-Vallee, France;3. Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing, 211816, Jiangsu, China;4. Process Safety and Disaster Prevention Laboratory, National Yunlin University of Science and Technology, Douliou, Yunlin, 64002, Taiwan, ROC |
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| Abstract: | A gas explosion, as a common accident in public life and industry, poses a great threat to the safety of life and property. The determination and prediction of gas explosion pressures are greatly important for safety issues and emergency rescue after an accident occurs. Compared with traditional empirical and numerical models, machine learning models are definitely a superior approach. However, the application of machine learning in gas explosion pressure prediction has not reached its full potential. In this study, a hybrid gas explosion pressure prediction model based on kernel principal component analysis (KPCA), a least square support vector machine (LSSVM), and a gray wolf optimization (GWO) algorithm is proposed. A dataset consisting of 12 influencing factors of gas explosion pressures and 317 groups of data is constructed for developing and evaluating the KPCA-GWO-LSSVM model. The results show that the correlations among the 12 influencing factors are eliminated and dimensioned down by the KPCA method, and 5 composite indicators are obtained. The proposed KPCA-GWO-LSSVM hybrid model performs well in predicting gas explosion pressures, with coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) values of 0.928, 26.234, and 12.494, respectively, for the training set; and 0.826, 25.951, and 13.964, respectively, for the test set. The proposed model outperforms the LSSVM, GWO-LSSVM, KPCA-LSSVM, beetle antennae search improved BP neural network (BAS-BPNN) models and reported empirical models. In addition, the sensitivity of influencing factors to the model is evaluated based on the constructed database, and the geometric parameters X1 and X2 of the confined structure are the most critical variables for gas explosion pressure prediction. The findings of this study can help expand the application of machine learning in gas explosion prediction and can truly benefit the treatment of gas explosion accidents. |
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| Keywords: | Gas explosion pressures Kernel principal component analysis (KPCA) Least square support vector machine (LSSVM) Gray wolf optimizer (GWO) algorithm Sensitivity analysis |
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