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Automatic batch sedimentation test based on histogram back-projection image analysis

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
A new method for measurement of sludge blanket height (SBH) based on image analysis is presented. The proposed method uses a histogram back-projection algorithm to distinguish between the settling sludge and supernatant and can be used with sludge possessing different coloring characteristics both in the sludge color and the color of supernatant produced. Individual pixels in the acquired image are compared with a histogram of a representative sludge region. Therefore, the proposed method relies neither on the assumed shape of light intensity profile nor on the dominant sludge or supernatant color. Batch sedimentation tests are presented for different initial sludge concentrations and different background colors to simulate different sludge characteristics. Parameters of a settling velocity function are estimated based on the obtained results. Additionally, an algorithm is proposed that enables the zone settling velocity (ZSV) to be estimated before the batch sedimentation test is completed.
Rocznik
Strony
art. no. e136187
Opis fizyczny
Bibliogr. 24 poz., rys., tab.
Twórcy
  • Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland
  • Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland
  • MM Automation, ul. E. Bojanowskiego 27a, 40-772 Katowice, Poland
Bibliografia
  • [1] M. Henze, P. Harremoës, J.C. Jansen, and E. Arvin, Wastewater treatment, Springer-Verlag, Berlin, 1995.
  • [2] M. Metzger, “Mathematical model of sequentially controlled activated sludge processes”, Arch. of Cont. Sci. 9(3‒4), 111‒133 (1999).
  • [3] J.Ph. Chancelier, M. Cohen de Lara, C. Joannis, and F. Pacard, “New insights in dynamic modeling of a secondary settler – I. Flux theory and steady-states analysis”, Wat. Res. 31(8), 1847‒1856 (1997).
  • [4] H. Gao and M.K. Stenstrom, “Generalizing the effects of the baffling structures on the buoyancy-induced turbulence in secondary settling tanks with eleven different geometries using CFD models”, Chem. Eng. Res. Design. 143, 215‒225 (2019).
  • [5] M. T. Shah et al., “A novel settling tank for produced water treatment: CFD simulations and PIV experiments”, J. Petro. Sci. Eng. 182, 106352 (2019).
  • [6] E. Asensi, E. Alemany, P. Duque-Sarango, and D. Aguado, “Assessment and modelling of the effect of precipitated ferric chloride addition on the activated sludge settling properties”, Chem. Eng. Res. Design. 150, 14‒25 (2019).
  • [7] X. Kang, Z. Xia, J. Wang, and W. Yang, „A novel approach to model the batch sedimentation and estimate the settling velocity, solid volume fraction, and floc size of kaolinite in concentrated solution”, Colloid Surf. A 579, 123647 (2019).
  • [8] J. Wiora, A. Kozyra, and A. Wiora, “Towards automation of measurement process of surface water parameters by remote-controlled catamaran”, Bull. Pol. Ac.: Tech. 65(3), 351‒359 (2017).
  • [9] R. Aguilar-López and I. Neria-González, “Controlling continuous bioreactor via nonlinear feedback: modelling an dsimulations approach”, Bull. Pol. Ac.: Tech. 64(1), 235‒241 (2016).
  • [10] G.A. Ekama, et al., “Secondary settling tanks: theory, modeling, design and operation. of sludge sedimentation parameters”, IAWQ Scientific and Technical Report No. 6, IAWQ, London. 1997.
  • [11] P.A. Vesilind, “Theoretical considerations: Design of prototype thickeners from batch settling tests”, Wat. Sew. Wks. 115(7), 302‒307 (1968).
  • [12] I. Takács, G.G. Patry, and D. Nolasco, “A dynamic model of the clarification-thickening process”, Wat. Res. 25(10), 1263‒1271 (1991).
  • [13] P. Grassia, S.P. Usher, and P.J. Scales, “Closed-form solution for batch settling height from model settling flux functions”, Chem. Engng. Sci. 66(5), 964‒972 (2011).
  • [14] A. Vanderhasselt and P.A. Vanrolleghem, “Estimation of sludge sedimentation parameters from single batch settling curve”, Wat. Res. 34(2), 395‒406 (2000).
  • [15] P. Vanrolleghem, et al., “On-line quantification of settling properties with in-sensor-experiments in an automated settlometer”, Wat. Sci. Tech. 33(1), 37‒51 (1996).
  • [16] X. Lu, et al. “Automatic monitoring and quantitative characterization of sedimentation dynamics for non-homogenous systems based on image profile analysis”, Powder Technol. 281, 49‒56 (2015).
  • [17] W. Suchecki, “Investigation of the sedimentation process using flow visualization methods”, Chem. And Proc. Eng. 40(2), 223‒233 (2019).
  • [18] Y.J. Kim, S.J. Choi, H. Bae, and C.W. Kim, “Sludge settleability detection using automated SV30 measurement and its application to a field WWTP”, Wat. Sci. Technol. 64(8), 1743‒1749 (2011).
  • [19] N. Derlon, Ch. Thürlimann, D. Dürrenmatt, and K. Villez, “Batch settling curve registration via image data modelling”, Wat. Res. 114, 327‒337 (2017).
  • [20] Z-H. Li, D. Han, C-J. Yang, T-Y Zhang, and H-Q. Yu, “Probing operational conditions of mixing and oxygen deficiency using HSV color space”, J. Environ. Manage. 232, 985‒992 (2019).
  • [21] Y. Xu, L. Zheng, R. Liu, and X. Dai, “Decyphering color for comprehensive utilization of sludge”, Resour. Conserv. Recycl. 153, 104579 (2020).
  • [22] S.M. Pizer, et al., ”Adaptive histogram equalization and its variations”, Comp. Vis. Grap. Img. Proc. 39(3), 355‒368 (1987).
  • [23] M.J. Swain and D.H. Ballard, “Color indexing”, Int. J. Comput. Vis. 7, 11‒32 (1991).
  • [24] W. Niblack, An introduction to digital image processing, 1st English ed., Prentice Hall, 1986.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-5e49a69c-724d-4a53-97ff-353610855a10
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