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Experimental study of cured dust layer structure parameters based on semantic segmentation

  • Separation Technology, Thermodynamics
  • Published:
Korean Journal of Chemical Engineering Aims and scope Submit manuscript

Abstract

The structural properties of the dust layer, including its thickness, porosity, and particle size distribution, play a critical role in ensuring the high precision and long-term stability of filter elements. However, observing these properties is challenging due to the weak adherence and cohesiveness of the layer. To address this issue, atomization thermosetting glue was used to achieve pre-curing, and the entire dust layer was cured with epoxy resin. After the sample was frozen and fractured using liquid nitrogen, the boundaries of the dust particles became plainly visible. Traditional binarization techniques were insufficient in identifying the edges of the dust particles since the grayscale values of particles and their environment partially overlap. As a result, a deep learning model based on the DeeplabV3+ network architecture was used to identify particles in the dust layer and achieved an accuracy of 90.99%. The research reveals that pulse-jet cleaning can double the thickness of the local dust layer on adjacent filter elements. Additionally, the surface morphology of the filter element significantly impacts the shape and thickness of the dust layer, causing it to change dramatically. Uneven thickness of the dust layer can result in a higher number of dust particles passing through the filter element membrane.

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References

  1. J. C. Lin, T. Hsiao, S. Hsiau, D. Chen, Y. Chen, S. Huang, C. Chen and M. Chang, Sep. Purif. Technol., 198, 146 (2018).

    Article  CAS  Google Scholar 

  2. S. Berbner and T. Pilz, Powder Technol., 86, 103 (1996).

    Article  CAS  Google Scholar 

  3. D. Koch, J. Seville and R. Clift, Powder Technol., 86, 21 (1996).

    Article  CAS  Google Scholar 

  4. R. Boudhan, A. Joubert, S. Durécu, K. Gueraoui and L. Le Coq, J. Aerosol Sci., 130, 1 (2019).

    Article  CAS  Google Scholar 

  5. L. F. Liu, Korean J. Chem. Eng., 38, 2122 (2021).

    Article  CAS  Google Scholar 

  6. M. Lupion, B. Navarrete, B. Alonso-Farinas and M. Rodriguez-Galan, Fuel, 108, 24 (2013).

    Article  CAS  Google Scholar 

  7. K. Furumoto, T. Fukasawa, T. Ishigami, M. Irwan Fatkhur Rozy, H.-P. Kuo, A.-N. Huang and K. Fukui, Adv. Powder Technol., 33, 103602 (2022).

    Article  Google Scholar 

  8. J. H. Kim, Y. C. Kim and J. H. Choi, Korean J. Chem. Eng., 33, 726 (2016).

    Article  CAS  Google Scholar 

  9. A. C. B. Neiva and L. Goldstein, Chem. Eng. Process., 42, 495 (2003).

    Article  CAS  Google Scholar 

  10. J. H. Choi, S. J. Ha, Y. C. Bak and Y. O. Park, Korean J. Chem. Eng., 19, 1085 (2002).

    Article  CAS  Google Scholar 

  11. D. Q. Jiang, W. D. Zhang, J. T. Liu, W. Geng and Z. Q. Ren, Korean J. Chem. Eng., 25, 744 (2008).

    Article  CAS  Google Scholar 

  12. D. P. i. M. M. D. M. Kanaoka, T. Kishima and M. Furuuchi, High Temperature Gas Cleaning (1996).

  13. C. R. N. Silva, V. S. Negrini, M. L. Aguiar and J. R. Coury, Powder Technol., 101, 165 (1999).

    Article  CAS  Google Scholar 

  14. W. Höflinger, C. Stöcklmayer and A. Hackl, Filtr. Sep., 31, 807 (1994).

    Article  Google Scholar 

  15. M. L. Aguiar and J. R. Coury, Ind. Eng. Chem. Res., 35, 3673 (1996).

    Article  CAS  Google Scholar 

  16. J. H. Kim, Y. Liang, K. M. Sakong, J. H. Choi and Y. C. Bak, Powder Technol., 181, 67 (2008).

    Article  CAS  Google Scholar 

  17. M. Hata, M. Furuuchi, C. Kanaoka, R. Kurose and H. Makino, Adv. Powder Technol., 14, 719 (2003).

    Article  CAS  Google Scholar 

  18. A. Y. Al-Otoom, Y. Ninomiya, B. Moghtaderi and T. F. Wall, Energy Fuels, 17, 316 (2003).

    Article  CAS  Google Scholar 

  19. N. Döring, J. Meyer and G. Kasper, Chem. Eng. Sci., 64, 2483 (2009).

    Article  Google Scholar 

  20. I. Tomasetta, D. Barletta and M. Poletto, Adv. Powder Technol., 24, 609 (2013).

    Article  CAS  Google Scholar 

  21. L. G. Tong, X. D. Chen, Y. P. Zhang, S. W. Yin, C. P. Liu, L. Wang and Y. L. Ding, Powder Technol., 354, 760 (2019).

    Article  CAS  Google Scholar 

  22. C. Kanaoka, M. Hata and H. Makino, Powder Technol., 118, 107 (2001).

    Article  CAS  Google Scholar 

  23. H. Chi, Z. Ji and B. Du, J. China Univ. Pet., Ed. Nat. Sci., 35 (2011).

  24. P. C. Zhao and Y. G. Li, Colloid Surf. A-Physicochem. Eng. Asp., 585 (2020).

  25. S. Suresh and S. Lal, Appl. Soft Comput., 55, 503 (2017).

    Article  Google Scholar 

  26. B. Li, W. Zhang, Y. Xue, R. Kong, W. Zhu, Y. Yu and Y. Chen, Int. J. Rock Mech. Min. Sci., 158, 105195 (2022).

    Article  Google Scholar 

  27. L. Deng and D. Yu, Found. Trends Signal Process., 7, 197 (2014).

    Article  Google Scholar 

  28. E. Shelhamer, J. Long and T. Darrell, IEEE Trans. Pattern Anal. Mach. Intell., 39, 640 (2017).

    Article  PubMed  Google Scholar 

  29. J. Stallkamp, M. Schlipsing, J. Salmen and C. Igel, Neural Networks, 32, 323 (2012).

    Article  CAS  PubMed  Google Scholar 

  30. Y. Niu, P. Mostaghimi, M. Shabaninejad, P. Swietojanski and R. T. Armstrong, Water Resour. Res., 56 (2020).

  31. S. Karimpouli and P. Tahmasebi, Comput. Geosci., 126, 142 (2019).

    Article  Google Scholar 

  32. P. I. Guntoro, G. Tiu, Y. Ghorbani, C. Lund and J. Rosenkranz, Miner. Eng., 142, 105882 (2019).

    Article  CAS  Google Scholar 

  33. T. I. Anderson, B. Vega and A. R. Kovscek, Comput. Geosci., 145, 104593 (2020).

    Article  Google Scholar 

  34. M. P. Filippo, O. da Fonseca Martins Gomes, G. A. O. P. da Costa and G. L. A. Mota, Miner. Eng., 170, 107007 (2021).

    Article  CAS  Google Scholar 

  35. Y. Wang, X. Bai, L. Wu, Y. Zhang and S. Qu, Fuel, 308, 121844 (2022).

    Article  CAS  Google Scholar 

  36. T. A. Sipkens and S. N. Rogak, J. Aerosol Sci., 152, 105699 (2021).

    Article  CAS  Google Scholar 

  37. H. Chi, Z. Ji and D. Sun, J. China Univ. Pet., Ed. Nat. Sci., 23, 72 (2010).

    CAS  Google Scholar 

  38. X. Tian and D. Hugh, The Leading Edge, 37, 435 (2018).

    Article  Google Scholar 

  39. L.-C. Chen, G. Papandreou, F. Schroff and H. Adam, Rethinking atrous convolution for semantic image segmentation, arXiv (2017).

  40. E. Schmidt and F. Löffler, Powder Technol., 60, 173 (1990).

    Article  CAS  Google Scholar 

  41. W.-M. Lu and K.-J. Hwang, AIChE J., 41, 143 (1995).

    Article  Google Scholar 

  42. J. H. Choi, S. J. Ha and H. J. Jang, Powder Technol., 140, 106 (2004).

    Article  CAS  Google Scholar 

  43. K. J. Jeon and Y. W. Jung, Powder Technol., 141, 1 (2004).

    Article  CAS  Google Scholar 

  44. Z. Ji, S. Peng, H. Chen and M. Shi, CIESC J., 35 (2003).

  45. S. Peng, Z. Ji and H. Chen, J. Combust. Sci. Technol., 498 (2002).

  46. M. Saleem, G. Krammer and M. S. Tahir, Powder Technol., 228, 100 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. M. Koch and G. Krammer, Powder Technol., 292, 149 (2016).

    Article  CAS  Google Scholar 

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Acknowledgements

The National Key Research and Development Program of China (2021YFB3801304) and the National Natural Science Foundation of China (No. 51904315) supported this work. The authors are also grateful to Prof. Haixia Li at Henan Polytechnic University for her suggestions and discussions.

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Authors and Affiliations

  1. Beijing Key Laboratory of Process Fluid Filtration and Separation, College of Mechanical and Transportation Engineering, China University of Petroleum, Beijing, China

    Bin Li, Zhongli Ji, Junfeng Mu, Yulin Ren & Zhen Liu

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  1. Bin Li
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  2. Zhongli Ji
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  3. Junfeng Mu
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  4. Yulin Ren
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  5. Zhen Liu
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Correspondence to Zhongli Ji.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Li, B., Ji, Z., Mu, J. et al. Experimental study of cured dust layer structure parameters based on semantic segmentation. Korean J. Chem. Eng. 40, 2271–2281 (2023). https://doi.org/10.1007/s11814-023-1414-2

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  • DOI: https://doi.org/10.1007/s11814-023-1414-2

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