An Approach on the Evaluation of LNG Tank Container Transportation Safety

  • Feiyu Meng
  • Li Ma
  • Xuefeng Wang
Keywords: LNG Tank Container, Index System, RNN, Case Study, Safety

Abstract

As a clean energy source, liquefied natural gas (LNG) has been widely accepted all around the world. As a way to transport LNG, tank container transportation is becoming more and more popular. However, how to carry out safety management for the whole transportation process of tank container is a problem troubling the whole industry. Therefore, this paper proposes a model based on the Recurrent Neural Networks(RNN) to evaluate the safety performance. First, find the factors affecting the safety of LNG transport by sea and construct an index system. Next, design a questionnaire and get scores from supporting experts. Then, this paper utilize the trained RNN to judge the safety statue of LNG tank transportation. Through the comparison of training results and the final score got from experts, the result shows that the MAE is negligible and prove the effectiveness of the RNN. Finally, a case study was conducted. From the analysis of the training results, it is known that enterprise safety management plays an important role in transportation safety and a better safety management system will greatly reduce the probability of accidents and improve the transportation safety.

References

[1] China Energy Network. (2019). The China energy storage alliance is a non-profit industry association dedicated to promoting energy storage technology in China. Available at: www.Cnenergynews.cn.
[2] Zeng J, Min W, & Liu Y, et al. (2012). Characteristics and prevention of road transport liquefied natural gas (LNG) accidents. Advanced Forum on Transportation of China, 2012, 221-225.
[3] Peng, J. (2009). Test and analysis of LNG tank container waterway transportation. Second International Conference on Transportation Engineering, 50(5), 1844-1848.
[4] Yudhbir Lalit. (1999). Maritime risk and transportation model for the transport of crude oil and petroleum products. University of Miami, 2587-2590.
[5] Chlomoudis C I, Kostagiolas P A, & Lampridis C D. (2011). Quality and safety systems for the port industry: Empirical evidence for the main Greek ports. European Transport Research Review, 3(2), 85-93.
[6] Dong Q ,Qian D L, & Li C H, et al. (2013). Research on safety evaluation indexes of the road transportation enterprise of dangerous goods. Applied Mechanics and Materials, 409-410, 1330-1334.
[7] Sergeichev I V, Ushakov A E, & Safonov AA, et al. (2015). Design of the composite tank container for multi-modal transportation of chemically aggressive fluids and petrochemicals. CAMX Conference, 1-10.
[8] Edward Lisowski & Wojciech Czyzycki. (2011). Transport and storage of LNG in container tanks. Journal of KONES Powertrain and Transport, 18(3), 193-201.
[9] Goldarag Y J, Mohammadzadeh A, & Ardakani A S. (2016). Fire risk assessment using neural network and logistic regression. Journal of the Indian Society of Remote Sensing, 44(6), 1-10.
[10] Peng Y & Ying S. (2010). The risk assessment of E-commerce based on BP neural network. International Conference on E-business & E-government, IEEE Computer Society, 2587-2590.
[11] Qiao L S H & Lin Z H. (2013). The evaluation study of supply chain financial risk based on the BP neural network. Applied Mechanics and Materials, 401-403, 2306-2309.
[12] Qin Yao, Li yong, & Wang Shimin. (2018). Research on improved RNN urban traffic congestion prediction model. Electronic World, 06, 45-46.
[13] Hu Xin. (2017). Network security situation prediction method based on RNN. Modern Computer (Professional Edition), 6, 14-16.
[14] Song B Y, Moon D S, & Lee D H. (2009). A study on development of safety index for evaluating railway safety. Korea Railway Society Proceedings, 12(4), 443-449.
[15] Qing-Song Z. (2009). Quantitative risk assessment approach in LNG tank shipping container in port water area. Natural Gas Industry, 7(01)114-116.
[16] Nanduri A & Sherry L. (2016). Anomaly detection in aircraft data using Recurrent Neural Networks (RNN). Integrated Communications Navigation & Surveillance, 5C2-1-5C2-8.
[17] Zheng Fengming & Li Shufang. (2017). Anomaly detection in smart grid based on encoder-decoder framework with recurrent neural network. The Journal of China Universities of Posts and Telecommunications, 24(06), 67-73.
[18] Du K L & Swamy M.N.S. (2014). Recurrent neural networks. London: Springer.
Published
2019-10-31
How to Cite
Feiyu Meng, Li Ma, & Xuefeng Wang. (2019). An Approach on the Evaluation of LNG Tank Container Transportation Safety. International Journal of Engineering and Management Research, 9(5), 44-53. Retrieved from http://www.ijemr.net/ojs/index.php/ojs/article/view/35