O
Orclever
Back to Journal
Research Article Open AccessOrclever Native

Imcube: A static particle size analyzer for all shape types via advanced vision tools and integrated machinery

Ozgun Deliismail1✉,
Basak Tuncer2,
Alperen Guler3
1SOCAR Turkey R&D and Innovation
2SOCAR Turkey R&D and Innovation
3SOCAR Turkey R&D and Innovation
Published:December 31, 2023
DOI: 10.56038/ejrnd.v3i4.340
Vol. 3, No. 4

Abstract

An integrated machinery to perform the analysis of particle size distribution through image processing formulations is presented. The product is comprehensive and flexible to many different industrial needs with a tailored hardware integration and design, which employs a set of sophisticated algorithms for the computational efficiency and accuracy. Compared to the traditional methods, the architecture is superior and provides significant impact as irregular and noncircular particles from a wide dimension spectrum can be analysed instantly, eliminating the need for significant manual effort with conventional trays with low accuracy. The reports are obtained through the built-in screen, mounted on the device, at customer specified detail level in addition to state-of-the-art presentations benefiting common statistics. The prediction performance, which is validated through industrial data, can further be developed for smaller particles, as a higher resolution camera implementation is necessary, with a heuristic algorithm to estimate the maximum likelihood of particle sizes when they overlap on the measurement tray.

Keywords
Image Processingcatalystparticle sizeparticle size distribution

References

  1. 1.S. Gan, Y. Liang, D. R. Baer, M. R. Sievers, G. S. Herman, and C. H. F. Peden, “Effect of Platinum Nanocluster Size and Titania Surface Structure upon CO Surface Chemistry on Platinum-Supported TiO 2 (110),” J Phys Chem B, vol. 105, no. 12, pp. 2412–2416, Mar. 2001, doi: 10.1021/jp003125z.DOI
  2. 2.M. Englisch, A. Jentys, and J. A. Lercher, “Structure Sensitivity of the Hydrogenation of Crotonaldehyde over Pt/SiO2and Pt/TiO2,” J Catal, vol. 166, no. 1, pp. 25–35, Feb. 1997, doi: 10.1006/jcat.1997.1494.DOI
  3. 3.W. T. Ralston, G. Melaet, T. Saephan, and G. A. Somorjai, “Evidence of Structure Sensitivity in the Fischer–Tropsch Reaction on Model Cobalt Nanoparticles by Time‐Resolved Chemical Transient Kinetics,” Angewandte Chemie International Edition, vol. 56, no. 26, pp. 7415–7419, Jun. 2017, doi: 10.1002/anie.201701186.DOI
  4. 4.J.-X. Liu, P. Wang, W. Xu, and E. J. M. Hensen, “Particle Size and Crystal Phase Effects in Fischer-Tropsch Catalysts,” Engineering, vol. 3, no. 4, pp. 467–476, Aug. 2017, doi: 10.1016/J.ENG.2017.04.012.DOI
  5. 5.N. Fischer, E. van Steen, and M. Claeys, “Structure sensitivity of the Fischer–Tropsch activity and selectivity on alumina supported cobalt catalysts,” J Catal, vol. 299, pp. 67–80, Mar. 2013, doi: 10.1016/j.jcat.2012.11.013.DOI
  6. 6.A. E. Yarulin, R. M. Crespo-Quesada, E. V Egorova, and L. L. Kiwi-Minsker, “Structure sensitivity of selective acetylene hydrogenation over the catalysts with shape-controlled palladium nanoparticles,” Kinetics and Catalysis, vol. 53, no. 2, pp. 253–261, 2012, doi: 10.1134/S0023158412020152.DOI
  7. 7.P. A. Rautanen, M. S. Lylykangas, J. R. Aittamaa, and A. O. I. Krause, “Liquid Phase Hydrogenation of Naphthalene on Ni/Al2O3,” in Studies in Surface Science and Catalysis, vol. 133, G. F. Froment and K. C. Waugh, Eds., Elsevier, 2001, pp. 309–316. doi: https://doi.org/10.1016/S0167-2991(01)81977-7.DOI
  8. 8.L. M. Kustov and A. N. Kalenchuk, “The Role of the Metal in the Catalytic Reactions of Hydrogenation–Dehydrogenation of Polycyclic Hydrocarbons for Hydrogen Storage,” Metals (Basel), vol. 12, no. 12, p. 2002, Nov. 2022, doi: 10.3390/met12122002.DOI
  9. 9.P. A. Rautanen, M. S. Lylykangas, J. R. Aittamaa, and A. O. I. Krause, “Liquid-Phase Hydrogenation of Naphthalene and Tetralin on Ni/Al2O3: Kinetic Modeling,” Ind Eng Chem Res, vol. 41, no. 24, pp. 5966–5975, Nov. 2002, doi: 10.1021/ie020395q.DOI
  10. 10.E. L. Hall, J. J. Hwang, and F. A. Sadjadi, “Computer Image Processing and Recognition” H. L. Kasdan, Ed., May 1980, pp. 2–10. doi: 10.1117/12.958573.DOI
  11. 11.M. Feixas, A.Bardera, J.Rigau, and Q.Xy,”Information Theory Tools for Image Processing”,2022, Springer International Publishing
  12. 12.M.A., Sid-Ahmed, “Image_Processing: Theory, Algorithms, and Architectures”, 1995, McGraw-Hill
  13. 13.G. Borgefors, “Distance transformations in digital images,” Comput Vis Graph Image Process, vol. 34, no. 3, pp. 344–371, Jun. 1986, doi: 10.1016/S0734-189X(86)80047-0.DOI
  14. 14.S. van der Walt et al., “scikit-image: image processing in Python,” PeerJ, vol. 2, p. e453, Jun. 2014, doi: 10.7717/peerj.453.DOI
  15. 15.C. Grigorescu and N. Petkov, “Distance sets for shape filters and shape recognition,” IEEE Transactions on Image Processing, vol. 12, no. 10, pp. 1274–1286, Oct. 2003, doi: 10.1109/TIP.2003.816010.DOI
  16. 16.F. Veillon, “Study and comparison of certain shape measures,” Signal Processing, vol. 11, no. 1, pp. 81–91, Jul. 1986, doi: 10.1016/0165-1684(86)90096-4.DOI
  17. 17.J. K. Udupa et al., “A framework for evaluating image segmentation algorithms,” Computerized Medical Imaging and Graphics, vol. 30, no. 2, pp. 75–87, Mar. 2006, doi: 10.1016/j.compmedimag.2005.12.001.DOI
  18. 18.A. Kornilov and I. Safonov, “An Overview of Watershed Algorithm Implementations in Open Source Libraries,” J Imaging, vol. 4, no. 10, p. 123, Oct. 2018, doi: 10.3390/jimaging4100123.DOI
Download PDF
Cite This Article
Deliismail, O., Tuncer, B., Guler, A. (2023). Imcube: A static particle size analyzer for all shape types via advanced vision tools and integrated machinery. *The European Journal of Research and Development*, 3(4), 414-423. https://doi.org/10.56038/ejrnd.v3i4.340

Bibliographic Info

JournalThe European Journal of Research and Development
Volume3
Issue4
Pages414–423
PublishedDecember 31, 2023
eISSN2822-2296