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Development of MSDS Map for Visual Safety Management of Hazardous and Chemical Materials

유해화학물질의 시각적 안전관리를 위한 MSDS 지도 개발

  • Shin, Myungwoo (Department of Safety Engineering, Pukyong National University) ;
  • Suh, Yongyoon (Department of Safety Engineering, Pukyong National University)
  • Received : 2019.02.26
  • Accepted : 2019.04.16
  • Published : 2019.04.30

Abstract

For preventing the accidents generated from the chemical materials, thus far, MSDS (Material Safety Data Sheet) data have been made to notify how to use and manage the hazardous and chemical materials in safety. However, it is difficult for users who handle these materials to understand the MSDS data because they are only listed based on the alphabetical order, not based on the specific factors such as similarity of characteristics. It is limited in representing the types of chemical materials with respect to their characteristics. Thus, in this study, a lots of MSDS data are visualized based on relationships of the characteristics among the chemical materials for supporting safety managers. For this, we used the textmining algorithm which extracts text keywords contained in documents and the Self-Organizing Map (SOM) algorithm which visually addresses textual data information. In the case of Occupational Safety and Health Administration (OSHA) in the United States, the guide texts contained in MSDS documents, which include use information such as reactivity and potential risks of materials, are gathered as the target data. First, using the textmining algorithm, the information of chemicals is extracted from these guide texts. Next, the MSDS map is developed using SOM in terms of similarity of text information of chemical materials. The MSDS map is helpful for effectively classifying chemical materials by mapping prohibited and hazardous substances on the developed the SOM map. As a result, using the MSDS map, it is easy for safety managers to detect prohibited and hazardous substances with respect to the Industrial Safety and Health Act standards.

Keywords

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Fig. 1. Research procedure.

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Fig. 2. SOM method.

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Fig. 3. Data collection: MSDS data list published by OSHA.

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Fig. 4. Result of document-term matrix.

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Fig. 5. MSDS map Clustering results.

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Fig. 6. Prohibited(top) and permitted hazardous(bottom) substances.

Table 1. Summary of hazardous and chemical materials in MSDS map

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References

  1. J. Yang, C. Lim and S. Park, "A Study on the Priority for the Hazard and Risk Evalution of Chemicals(HREC) according to the Industrial Safety and Health Act(ISHA)", Journal of Korean Society of Occupational and Environmental Hygiene, Vol. 22, No. 1, pp. 73-81, 2012.
  2. J. Park, S. Ham, S. Kim, K. Lee, K. Ha, D. Park and C. Yoon, "Study on the Chemical Management - 1. Chemical Characteristics and Occupational Exposure Limits under Occupational Safety and Health Act of Korea", Journal of Korean Society of Occupational and Environmental Hygiene, Vol. 25, No. 1, pp. 45-57, 2015. https://doi.org/10.15269/JKSOEH.2015.25.1.45
  3. B. Song, S. Choi, Y. Kang, G. Lyu, Y. Jo, H. Im, J. Kwon and K. Park, "Safety Management System for Emergency Handling of Environmental Toxic Gas Release", Korean Journal of Hazardous Materials, Vol. 2, No. 2, pp. 27-31, 2014.
  4. Ministry of Environment, Chemistry Safety Clearinghouse, https://csc.me.go.kr/
  5. C. Yoon, S. Ham, J. Park, S. Kim, S. Lee, K. Lee and D. Park, "Comparison between the Chemical Management Contents of Laws Pertaining to the Ministry of Environment and the Ministry of the Employment and Labor", Journal of Environmental Health Sciences, Vol. 40, No. 5, pp. 331-345, 2014.
  6. Y. Suh, "Data Analytics for Social Risk Forecasting and Assessment of New Technology", J. Korean Soc. Saf., Vol. 32, No. 3, pp. 83-89, 2017. https://doi.org/10.14346/JKOSOS.2017.32.3.83
  7. S. Kang and Y. Suh, "On the Development of Risk Factor Map for Accident Analysis using Textmining and Self-Organizing Map(SOM) Algorithms", J. Korean Soc. Saf., Vol.33, No. 6, pp. 77-84, 2018. https://doi.org/10.14346/JKOSOS.2018.33.6.77
  8. R. Moura, M. Beer, E. Patelli and J. Lewis, "Learning from Major Accidents: Graphical Representation and Analysis of Multi-attribute Events to Enhance Risk Communication", Safety Science, Vol. 99, pp. 58-70, 2017. https://doi.org/10.1016/j.ssci.2017.03.005
  9. T. Kohonen, "The Self-Organizing Map", Neurocomputing, Vol. 21, No. 1-3, pp. 1-6, 1998. https://doi.org/10.1016/S0925-2312(98)00030-7
  10. T. Kohonen, "Self-Organization and Associative Memory", Springer-Verlag, Berlin, Heidelberg, 2012.
  11. T. Kohonen, "Essentials of the Self-Organizing Map", Neural Networks, Vol. 37, pp. 52-65, 2013. https://doi.org/10.1016/j.neunet.2012.09.018
  12. J. Vesanto and E. Allhoniemi, "Clustering of the Self-Organizing Map", IEEE Transactions on Neural Network, Vol. 11, No. 3, pp. 586-600, 2000. https://doi.org/10.1109/72.846731
  13. F. Palamara, F. Piglione and N. Piccinini, "Self-Organizing Map and Clustering Algorithms for the Analysis of Occupational Accident Database", Safety Science, Vol. 49, pp. 1215-1230, 2011. https://doi.org/10.1016/j.ssci.2011.04.003