SVM application in hazard assessment: Self-heating for sulfurized rust |
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| Institution: | 1. Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Urban Construction and Safety Engineering, Nanjing Tech University, Nanjing, 210009, China;2. University Paris-Est, Laboratoire Modelisation et Simulation Multi Echelle, MSME (UMR 8208 CNRS), 5 bd Descartes – Bat. Lavoisier, 77454 Marne-la-Vallee Cedex 2, France;1. College of Chemical Engineering, China University of Petroleum(East China), Qingdao, Shandong 266580, China;2. College of Mechanical and Electronic Engineering, China University of Petroleum(East China), Qingdao, Shandong 266580, China;3. State Grid Anhui Electric Power Research Institute, Hefei, Anhui, 230 022, China;1. School of Chemical Engineering, China University of Petroleum (East China), Qingdao 266580, China;2. Center for Offshore Engineering and Safety Technology, China University of Petroleum (East China), Qingdao 266580, China;1. College of Quality and Safety Engineering, China Jiliang University, Hangzhou 310018, China;2. China Coal Information Institute, Beijing 100029, China;3. Department of Mining and Nuclear Engineering, Missouri University of Science and Technology, Rolla, MO 65401, USA;4. WA School of Mines: Minerals, Energy and Chemical Engineering, Curtin University, Kalgoorlie, WA 6430, Australia;1. Center for Offshore Engineering and Safety Technology, China University of Petroleum (East China), Qingdao, 266580, China;2. College of Chemical Engineering, China University of Petroleum (East China), Qingdao, 266580, China;3. Centre for Risk, Integrity and Safety Engineering (C-RISE), Memorial University, St John''s, NL, A1B 3X5, Canada;1. Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing, 210009, China;2. University Paris-Est, Laboratoire Modelisation et Simulation Multi Echelle, MSME (UMR 8208 CNRS), 5 bd Descartes – Bat. Lavoisier, 77454 Marne-la-Vallee Cedex 2, France;1. State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei 230026, PR China;2. Department of Materials Science & Engineering, University of Science and Technology of China, Hefei 230026, PR China |
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| Abstract: | In order to assess the oxidation self-heating hazard of sulfurized rust, for particular ambient conditions in crude oil tanks, the support vector machine (SVM) technique is applied to predict the maximum temperature (Tmax) of oxidation self-heating process. Five governing parameters are selected, i.e. the water content, mass of sulfurized rust, operating temperature, air flow rate and oxygen concentration in the respiratory/safety valve. The efficiency and validity of the SVM predictions are investigated in the case of two sets of data: more than 85 experiments performed in academic lab (China) and almost 17 additional results collected from existing literature. Two main steps are also discussed: the training process (on selected subsets of data) and prediction process (for the remaining subsets of data). It can be concluded that for both datasets the maximum temperature (Tmax) values calculated by SVM technique were in good accordance with the experimental results, with relative errors smaller than 15% except for a few cases.The SVM technique seems therefore to be relevant and very helpful for complex implicit processes such as chemical reactions, as it is the case of the oxidation of sulfurized rust in oil tanks. Furthermore, such predictive methods can be continuously be improved through additional experiments feedback (larger databases) and can then be of crucial help for monitoring and early warning of hazardous reactions. |
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| Keywords: | Industrial risks Support vector machine (SVM) Sulfurized rust Oxidation self-heating Critical temperature |
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