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Prediction of the Maximum Temperature of Sulfur-Containing Oil using Gaussian Process Regression for Hazards Prevention

Volume 14, Number 12, December 2018, pp. 2951-2959
DOI: 10.23940/ijpe.18.12.p5.29512959

Chenhui Rena, Yuxuan Yangb, Xue Donga, and Haiping Donga

aSchool of Mechatronical Engineering, Beijing Institute of Technology, Beijing, 100081, China
bNo.12 Middle School of Beijing, Beijing, 100071, China

(Submitted on September 8, 2018; Revised on October 15, 2018; Accepted on November 21, 2018)

Abstract:

An oxidation self-heating process of sulfurized rust usually results in a fire or an explosion in crude oil tanks due to the oil’s maximum temperature (Tmax) exceeding the critical temperature at which the fire and explosion happens. Some previous studies have shown that Tmax is determined by the five main factors including water content, mass of sulfurized rust, operating temperature, air flow rate, and oxygen concentration in the safety valve. In this paper, based on a collected dataset about the five factors and Tmax, the Gaussian process regression (GPR) method is adopted to build a nonlinear model describing the relationship between Tmax and the five factors, and the new model is then used to predict Tmax of other similar processes by inputting the data corresponding to the five factors. The results show that the GPR model can reach the prediction accuracy and the prediction result by the GPR model is more accurate than that by the model of Support Vector Machine (SVM). This indicates that the GPR method can be applied to predict Tmax of the oxidation self-heating process of sulfurized rust. The prediction of Tmax using the GPR model is of great significance to industrial risk control and accident prevention of sulfur-containing oil in production and transportation.

 

References: 31

                    1. S. P. Zhao, J. C. Jiang, and J. Zheng, “Thermal Analysis on the Kinetics of Thermal Decomposition of Sulfurized Rust,” Journal of Chongqing University, Vol. 34, No. 1, pp. 140-144, 2011
                    2. S. Zhao, C. Wang, and P. Li, “The Influence of Sulfurization of Rust in Oil Tanks,” Energy Sources Part A Recovery Utilization & Environmental Effects, Vol. 29, No. 12, pp. 1111-1119, 2007
                    3. Z. Dou, J. Jiang, and Z. Wang, “Kinetic Analysis for Spontaneous Combustion of Sulfurized Rust in Oil Tanks,” Journal of Loss Prevention in the Process Industries, Vol. 32, pp. 387-392, 2014
                    4. Y. Zhang, J. Jiang, and L. Huang, “Oxidation Experiment of Sulfurized Rusts in Crude Oil Tank,” Journal of Nanjing Tech University, Vol. 2, No. 7, pp. 39-45, 2017
                    5. S. Zhao and J. C. Jiang, “Study on Spontaneous Combustion Mechanism of Oil Containing Sulfur based on Thermal Analysis,” Oil & Gas Storage & Transportation, Vol. 28, No. 10, pp. 45-48, 2009
                    6. J. Gao, X. Man, and J. Shen, “Synthesis of Pyrophoric Active Ferrous Sulfide with Oxidation Behavior under Hypoxic Conditions,” Vacuum, Vol. 143, pp. 386-394, 2017
                    7. R. I. Hughes and T. D. B. Morgan, “The Generation of Pyrophoric Material in the Cargo Tanks of Crude Oil Carriers,” Trans. Inst. Mar. Eng., Vol. 88, pp. 153-161, 1976
                    8. P. Li and Y. C. Zhai, “Study on Dynamic Ignition Temperature Curve of Oil Storage Tank Induced by Ferrous Sulfide,” China Safety Science Journal, Vol. 14, No. 3, pp. 44-48, 2004
                    9. Z. Dou, J. C. Jiang, and S. P. Zhao, “Analysis on Oxidation Process of Sulfurized Rust in Oil Tank,” Journal of Thermal Analysis and Calorimetry, Vol. 128, No. 1, pp. 125-134, 2017
                    10. Z. Dou, A. Mebarki, L. Ni, J. C. Cai, and M. G. Zhang, “SVM Application in Hazard Assessment: Self-Heating for Sulfurized Rust,” Journal of Loss Prevention in the Process Industries, Vol. 39, pp. 112-120, 2016
                    11. G. Landucci, G. Lovicu, and F. Barontini, “Hazards and Safety Issues Associated to the Residual Solid Content in Crude Edible Oil Processing,” Chemical Engineering Transactions, Vol. 36, pp. 151-156, 2014
                    12. G. Landucci, B. Nucci, and L. Pelagagge, “Hazard Assessment of Edible Oil Refining: Formation of flammable Mixtures in Storage Tanks,” Journal of Food Engineering, Vol. 105, No. 1, pp. 105-111, 2011
                    13. G. Landucci, L. Pelagagge, and C.Nicolell, “Analysis of Maintenance and Storage Operations in Edible Oil Plants: Formation of Flammable Mixtures,” in Proceedings of International Conference on Safety and Environment in the Process, pp. 33-38, 2012
                    14. X. Li, Y. J. Shang, Z. L. Chen, Y. Niu, and M. Yang, “Study of Spontaneous Combustion Mechanism and Heat Stability of Sulfide Minerals Powder based on Thermal Analysis,” Powder Technology, Vol. 309, pp. 68-73, 2017
                    15. F. Q. Yang and W. U. Chao, “Mechanism of Mechanical Activation for Spontaneous Combustion of Sulfide Minerals,” Transactions of Nonferrous Metals Society of China, Vol. 23, No. 1, pp. 276-282, 2013
                    16. P. Li, “Study on Corrosion and Oxidation Combustion Tendency of Oil Tank Containing Sulfur Oil,” Northeastern University, Shenyang, Liaoning, China, 2005
                    17. R. Walker, A. D. Steele, and T. D. B Morgan, “Pyrophoric Oxidation of Iron Sulphide,” Surface & Coatings Technology, Vol. 34, No. 2, pp. 163-175, 1988
                    18. C. Wu, Z. Li, and F. Yang, “Risk Forecast of Spontaneous Combustion of Sulfide Ore Dump in a Stope and Controlling Approaches of the Fire,” Archives of Mining Sciences, Vol. 53, No. 4, pp. 565-579, 2008
                    19. P. Li, S. Wang, and Z. Zhang, “Study on the Effect of Water on the Formation and Pyrophoricity of Ferrous Sulfide,” Petroleum Science & Technology, Vol. 29, No. 18, pp. 1922-1931, 2011
                    20. Y. Liu, Z. Zhang and N. Bhandari, “New Approach to Study Iron Sulfide Precipitation Kinetics, Solubility, and Phase Transformation,” Industrial & Engineering Chemistry Research, Vol. 56, No. 31, pp. 9016-9027, 2017
                    21. O. Samuelsson, A. Björk, and J. Zambrano, “Gaussian Process Regression for Monitoring and Fault Detection of Wastewater Treatment Processes,” Water Science & Technology a Journal of the International Association on Water Pollution Research, Vol. 75, No. 12, pp. 1-12, 2017
                    22. Y. Liu, Y. Pan, D. Huang, and Q. Wang, “Fault Prognosis of Filamentous Sludge Bulking Using an Enhanced Multi-Output Gaussian Processes Regression,” Control Engineering Practice, Vol. 62, pp. 46-54, 2017
                    23. G. O. Sahinoglu, M. Pajovic, and Z. Sahinoglu, “Battery State-of-Charge Estimation based on Regular/Recurrent Gaussian Process Regression,” IEEE Transactions on Industrial Electronics, Vol. 65, No. 5, pp. 4311-4321, 2018
                    24. H. Sheng, J. Xiao, and Y. Cheng, “Short-Term Solar Power Forecasting based on Weighted Gaussian Process Regression,” IEEE Transactions on Industrial Electronics, Vol. 65, No. 1, pp. 300-308, 2018
                    25. B. Sun, H. Yao, and T. Liu, “Short-term Wind Speed Forecasting based on Gaussian Process Regression Model,” Proceedings of the Csee, Vol. 32, No. 29, pp. 104-109, 2012
                    26. S. Raschka and V. Mirjalili, “Python Machine Learning,” Packt Publishing Ltd., 2017
                    27. A. Chekroud, “Why Validation Matters: A Demonstration Predicting Antipsychotic Response Using 5 Rcts,” Schizophrenia Bulletin, Vol. 44, pp. 707-715, 2018
                    28. P. Gramatica, “Principles of QSAR Models Validation: Internal and External,” Molecular Informatics, Vol. 26, No. 5, pp. 694-701, 2010
                    29. M. Raissi, P. Perdikaris, and G. E. Karniadakis, “Machine Learning of Linear Differential Equations Using Gaussian Processes,” Journal of Computational Physics, Vol. 348, pp. 683-693, 2017
                    30. C. E. Rasmussen and C. K. I. Williams, “Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning),” The MIT Press, 2005
                    31. A. Géron, “Hands-on Machine Learning with Scikit-Learn and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems,” O’Reilly Media, Inc., 2017

                                       

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