SVM application in hazard assessment: Self-heating for sulfurized rust. (January 2016)

• Record Type: Journal Article
• Authors: Dou, Z.; Mebarki, A.; Ni, L.; Jiang, J.C.; Cai, Z.L.; Zhang, M.G.; Zhao, S.P.; Zhang, W.X.; Pensee, V.
• Is Part Of: Journal of loss prevention in the process industries. Volume 39(2016:Jan.)
• Page Start: 112
• Page End: 120
• Publication Date: 2016-01
• Subjects: Industrial risks -- Support vector machine (SVM) -- Sulfurized rust -- Oxidation self-heating -- Critical temperature; Chemical industries -- Safety measures -- Periodicals; 660.2804
• DOI: 10.1016/j.jlp.2015.11.011
• Languages: English
• ISSNs: 0950-4230

Abstract

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 ( T max ) 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 ( T max ) 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. Highlights: Support vector machine (SVM) technique is used to predict the maximum temperature of self-heating process. Five governing parameters are selected. The maximal temperatures values calculated by SVM technique were in good accordance with the experimental results. The prediction of accidents due to the oxidative self-heating is also presented.