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Abstract

This study is focused on the image analysis of motionless hydraulic mixing process, for which pressure changes were the driving force. To improve the understanding of hydraulic mixing, mixing efficiency was assessed with dye introduction, which resulted in certain challenges. In order to overcome them, the framework and methodology consisting of three main steps were proposed and applied to an experimental case study. The experiments were recorded using a camera and then processed according to the proposed framework and methodology. The main outputs from the methodology which were based only on the recorded movie were liquid heights and colour changes during the process time. In addition, considerable attention has also been given to issues related to other colour systems and the hydrodynamic description of the process.
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Authors and Affiliations

Aleksandra Golczak
1
Waldemar Szaferski
1
ORCID: ORCID
Szymon Woziwodzki
1
Piotr T. Mitkowski
1
ORCID: ORCID

  1. Poznan University of Technology, Department of Chemical Engineering and Equipment, Berdychowo 4, 60-965 Poznan, Poland
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Abstract

The article describes the development of a model problem for electrocoagulation treatment of industrial wastewater taking into account changes in voltage and current. The study included computer simulation of the change in the concentration of iron at the output of the electrocoagulator at variable current levels. The laboratory-scale plant was developed for the photocolorimetric analysis of the iron-containing coagulant. It consisted of a flowing opaque cell through which water is pumped with a constant flow and also the block for processing and storage of information. Such structure allows to reduce human participation in the measurement process and to ensure the continuity of measurement without any need for sampling of the tested material, as well as to reduce the measurement cost. During the processing of results, graphical dependences were determined between RGB-components of water colour and the corresponding concentration of total iron and Fe3+ in water.
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Bibliography

AL-BARAKAT H.S., MATLOUB F.K., AJJAM S.K., AL-HATTAB T.A. 2020. Modeling and simulation of wastewater electrocoagulation reactor. The First International Conference of Pure and Engineering Sciences (ICPES2020). Karbala, Iraq, 26– 27.02.2020. IOP Conference. Ser. Materials Science and Engineering. Vol. 871, 012002 p. 1–16. DOI 10.1088/1757-899X/ 871/1/012002.
ANNEM S. 2017. Determination of iron content in water. Capstone Project. MSc Thesis. Governors State University OPUS Open Portal to University Scholarship pp. 18.
ASSÉMIAN A.S., KOUASSI E.K. 2018. Removal of a persistent dye in aqueous solutions by electrocoagulation process: Modeling and optimization through response surface methodology. Water Air and Soil Pollution. Vol. 229(6), 184. DOI 10.1007/s11270-018-3813-2.
BARROS J.A.V.A., MOREIRA F., SANTOS G., WISNIEWSKI C., LUCCAS P.O. 2016. Digital image analysis for the colorimetric determination of aluminum, total iron, nitrite and soluble phosphorus in waters. Analytical Letters. Vol. 50(2) p. 414–430. DOI 10.1080/00032719.2016.1182542.
BOMBA A., KLYMIUK YU., PRYSIAZHNIUK I., PRYSIAZHNIUK O., SAFONYK A. 2016. Mathematical modeling of wastewater treatment from multicomponent pollution by using microporous particles. AIP Conference Proceedings. Vol. 1773, 040003 p. 1–11. DOI 10.1063/1.4964966.
BOMBA A., SAFONYK A. 2013. Mathematical modeling of aerobic wastewater treatment in porous medium. Zeszyty Naukowe WSInf. Vol. 12. Nr 1 p. 21–29.
FIRDAUSA M., ALWIB W., TRINOVELDIB F., RAHAYUC I., RAHMIDARD L., WARSITOA K. 2014. Determination of chromium and iron using digital image-based colorimetry. Procedia Environmental Sciences. Vol. 20 p. 298–304. DOI 10.1016/j.proenv.2014.03.037.
FORERO G., HERNÁNDEZ-LARA R., ROJAS O. 2020. Development of an electrocoagulation equipment for wall paint wastewater treat-ment. Ingeniería y Competitividad. Vol. 22(2) p. 1–10. DOI 10.25100/iyc.v22i2.9474.
GOVINDAN K., ARUMUGAM A., KALPANA M., RANGARAJANB М., SHANKARE P., JANG A. 2019. Electrocoagulants characteristics and applica-tion of electrocoagulation for micropollutant removal and transformation mechanism. ACS Applied Materials & Interfaces. Vol. 12(1) p. 1775–1788. DOI 10.1021/acsami.9b16559.
KAUR R., AMIT A. 2018. Treatment of waste water through electrocoagulation. Pollution Research. Vol. 37(2) p. 394–403. KHANDEGAR V., ACHARYA S., JAIN A.K. 2018. Data on treatment of sewage wastewater by electrocoagulation using punched aluminum electrode and characterization of generated sludge. Data in Brief. Vol. 18 p. 1229–1238. DOI 10.1016/j.dib.2018.04.020.
KOYUNCU S., ARIMAN S. 2020. Domestic wastewater treatment by real- scale electrocoagulation process. Water Science and Technology. Vol. 81(4) p. 656–667. DOI 10.2166/wst.2020.128.
LUKA G. S., NOWAK E., KAWCHUK J., HOORFAR M., NAJJARAN H. 2017. Portable device for the detection of colorimetric assays. Royal Society Open Science. Vol. 4(11), 171025 p. 1–13. DOI 10.1098/rsos.171025.
MASAWAT P., HARFIELD A., SRIHIRUN N., NAMWONG A. 2016. Green determination of total iron in water by digital image colorimetry. Analytical Letters. Vol. 50(1) p. 173–185. DOI 10.1080/00032719.2016.1174869.
PAVÓN T., MUNGUIA G., MOKHTAR A., ROMERO H., HUACUZ J. 2018. Photovoltaic energy-assisted electrocoagulation of a synthetic textile effluent. International Journal of Photoenergy. Vol. 3 p. 1–9. DOI 10.1155/2018/7978901.
PERREN W., WOJTASIK A., CAI Q. 2018. Removal of microbeads from wastewater using electrocoagulation. American Chemical Society Omega. Vol. 3 p. 3357–3364. DOI 10.1021/acsomega.7b02037.
POSAVČIĆ H., HALKIJEVIĆ I., VUKOVIĆ Ž. 2019. Application of electro-coagulation for water conditioning. Environmental Engineering – Inženjerstvo Okoliša. Vol. 6. No. 2 р. 59–70. DOI 10.37023/ee.6.2.3.
RAHMAN A.N., KUMAR N.K.M.F., GILAN U.J., JIHED E.E., PHILIP A., LINUS A.A., SHAHINAN NEN D., ISMAIL V. 2020. Kinetic study & statistical modelling of Sarawak Peat Water Electrocoagulation System using copper and aluminium electrodes. Journal of Applied Science & Process Engineering. Vol. 7(1) p. 439–456. DOI 10.33736/jaspe.2195.2020.
SAMIR A., CHELLIAPAN S., ZURIATI Z., AJEEL M., ALABA P. 2016. A review of electrocoagulation technology for the treatment of textile wastewater. Reviews in Chemical Engineering. Vol. 33 p. 263– 292.
SHANTARIN V.D., ZAVYALOV V.V. 2003. Optimization of processes of electrocoagulation treatment of drinking water. Nauchnye i Tekhnicheskiye Aspekty Okhrany Okruzhayushchey Sredy. No. 5 p. 62–85.
YASRI N., ARUMUGAM A., KALPANA M., SHU T., FULADPANJEH B., OLDENBURG T., TRIFKOVIC M., MAYER B., ROBERTS P.L.E. 2017. Electrocoagulation for the treatment of produced water [online]. University of Calgary. [Access 10.01.2021]. Available at: https://albertainnovates.ca/wp-content/uploads/2019/07/145-Nael-Yasri. pdf
YASRI N., HU J., KIBRIA MD. G., ROBERTS P. L. E. 2020. Electrocoagulation separation processes. Multidisciplinary advances in efficient separation processes. Chapter 6. ACS Symposium Series. Vol. 1348 р. 167–203. DOI 10.1021/bk-2020-1348.ch006.
YAVUZ Y., ÖGÜTVEREN Ü. B. 2018. Treatment of industrial estate wastewater by the application of electrocoagulation process using iron electrodes. Journal of Environmental Management. Vol. 207 p. 151–158. DOI 10.1016/j.jenvman.2017.11.034.
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Authors and Affiliations

Andrii Safonyk
1
ORCID: ORCID
Ivanna Hrytsiuk
1
ORCID: ORCID
Marko Klepach
1
ORCID: ORCID
Maksym Mishchanchuk
1
ORCID: ORCID
Andriy Khrystyuk
1
ORCID: ORCID

  1. National University of Water and Environmental Engineering, Institute of Automatics, Cybernetics and Computer Engineering, Soborna St, 11, Rivne, Rivnens’ka oblast, 33028, Ukraine
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Abstract

Bridge inspections are a vital part of bridge maintenance and the main information source for Bridge Management Systems is used in decision-making regarding repairs. Without a doubt, both can benefit from the implementation of the Building Information Modelling philosophy. To fully harness the BIM potential in this area, we have to develop tools that will provide inspection accurate information easily and fast. In this paper, we present an example of how such a tool can utilise tablets coupled with the latest generation RGB-D cameras for data acquisition; how these data can be processed to extract the defect surface area and create a 3D representation, and finally embed this information into the BIM model. Additionally, the study of depth sensor accuracy is presented along with surface area accuracy tests and an exemplary inspection of a bridge pillar column.
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Bibliography

  1.  J.S. Kong and D.M. Frangopol, “Life-Cycle Reliability-Based Maintenance Cost Optimization of Deteriorating Structures with Emphasis on Bridges”, J. Struct. Eng. 129(6), 818–828 (2003).
  2.  B.M. Phares, G.A. Washer, D.D. Rolander, B.A. Graybeal, and M. Moore, “Routine Highway Bridge Inspection Condition Documentation Accuracy and Reliability”, J. Bridg. Eng. 9(4), 403–413 (2004).
  3.  A. Costin, A. Adibfar, H. Hu, and S.S. Chen, “Building Information Modeling (BIM) for transportation infrastructure – Literature review, applications, challenges, and recommendations”, Autom. Constr. 94, 257–281 (2018).
  4.  “SeeBridge”. [Online]. Available: https://seebridge.net.technion.ac.il/.
  5.  R. Sacks et al., “SeeBridge as next generation bridge inspection: Overview, Information Delivery Manual and Model View Definition”, Autom. Constr. 90, 134–145 (2018).
  6.  P. Hüthwohl, I. Brilakis, A. Borrmann, and R. Sacks, “Integrating RC Bridge Defect Information into BIM Models”, J. Comput. Civ. Eng. 32(3), (2018).
  7.  P. Hüthwohl and I. Brilakis, “Detecting healthy concrete surfaces”, Adv. Eng. Informatics 37, 150–162 (2018).
  8.  P. Hüthwohl, R. Lu, and I. Brilakis, “Multi-classifier for reinforced concrete bridge defects”, Autom. Constr. 105, 102824 (2019).
  9.  R. Lu, I. Brilakis and C. R. Middleton, “Detection of Structural Components in Point Clouds of Existing RC Bridges”, Comput. Civ. Infrastruct. Eng. 34(3), 191–212 (2019).
  10.  R. Lu and I. Brilakis, “Digital twinning of existing reinforced concrete bridges from labelled point clusters”, Autom. Constr. 105, 102837 (2019).
  11.  D. Isailović, V. Stojanovic, M. Trapp, R. Richter, R. Hajdin, and J. Döllner, “Bridge damage: Detection, IFC-based semantic enrichment and visualization”, Autom. Constr. 112, 103088 (2020).
  12.  C.S. Shim, H. Kang, N.S. Dang, and D. Lee, “Development of BIM-based bridge maintenance system for cable-stayed bridges”, Smart Struct. Syst. 20(6), 697–708 (2017).
  13.  N.S. Dang and C.S. Shim, “BIM authoring for an image-based bridge maintenance system of existing cable-supported bridges”, IOP Conf. Ser. Earth Environ. Sci. 143(1), 012032 (2018).
  14.  S. Dang, H. Kang, S. Lon, and S. Changsu, “3D Digital Twin Models for Bridge Maintenance”, 10th Int. Conf. Short Mediu. Span Bridg., 2018, pp. 73.1‒73.9.
  15.  C.S. Shim, N.S. Dang, S. Lon, and C.H. Jeon, “Development of a bridge maintenance system for prestressed concrete bridges using 3D digital twin model”, Struct. Infrastruct. Eng. 15(10), 1319–1332 (2019).
  16.  Z. Ma and S. Liu, “A review of 3D reconstruction techniques in civil engineering and their applications”, Adv. Eng. Informatics 37, 163–174 (2018).
  17.  Q. Wang and M.K. Kim, “Applications of 3D point cloud data in the construction industry: A fifteen-year review from 2004 to 2018”, Adv. Eng. Informatics 39, 306–319 (2019).
  18.  C. Popescu, B. Täljsten, T. Blanksvärd, and L. Elfgren, “3D reconstruction of existing concrete bridges using optical methods”, Struct. Infrastruct. Eng. 15(7), 912–924 (2019).
  19.  S. Izadi et al., “KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera”, in Proceedings of the 24th annual ACM symposium on User interface software and technology – UIST ’11, 2011, p. 559.
  20.  J. Hoła, J. Bień, Ł. Sadowski, and K. Schabowicz, “Non-destructive and semi-destructive diagnostics of concrete structures in assessment of their durability”, Bull. Polish Acad. Sci. Tech. Sci. 63(1), 87–96 (2015).
  21.  J. Bień, T. Kamiński, and M. Kużawa, “Taxonomy of non-destructive field tests of bridge materials and structures”, Arch. Civ. Mech. Eng. 19(4), 1353–1367 (2019).
  22.  J. Bień, M. Kużawa, and T. Kamiński, “Strategies and tools for the monitoring of concrete bridges”, Struct. Concr. 21(4), 1227–1239 (2020).
  23.  “OpenCV AI Kit”. [Online]. Available: https://www.kickstarter.com/projects/opencv/opencv-ai-kit.
  24.  B. Liu, H. Cai, Z. Ju, and H. Liu, “RGB-D sensing based human action and interaction analysis: A survey”, Pattern Recognit. 94, 1–12 (2019).
  25.  Y.-D. Hong, Y.-J. Kim, and K.-B. Lee, “Smart Pack: Online Autonomous Object-Packing System Using RGB-D Sensor Data”, Sensors 20(16), 4448 (2020).
  26.  M.R. Jahanshahi, F. Jazizadeh, S.F. Masri, and B. Becerik-Gerber, “Unsupervised approach for autonomous pavement-defect detection and quantification using an inexpensive depth sensor”, J. Comput. Civ. Eng. 27(6), 743–754 (2013).
  27.  D. Roca, S. Lagüela, L. Díaz-Vilariño, J. Armesto, and P. Arias, “Low-cost aerial unit for outdoor inspection of building façades”, Autom. Constr. 36, 128–135 (2013).
  28.  C. Bellés and F. Pla, “A Kinect-Based System for 3D Reconstruction of Sewer Manholes”, Comput. Civ. Infrastruct. Eng. 30(11), 906–917 (Nov. 2015).
  29.  M. Abdelbarr, Y.L. Chen, M.R. Jahanshahi, S.F. Masri, W.M. Shen, and U.A. Qidwai, “3D dynamic displacement-field measurement for structural health monitoring using inexpensive RGB-D based sensor”, Smart Mater. Struct. 26(12) (2017).
  30.  Z. Xu, S. Li, H. Li, and Q. Li, “Modeling and problem solving of building defects using point clouds and enhanced case-based reasoning”, Automation in Construction 96(February), 40–54 (2018).
  31.  M. Nahangi, T. Czerniawski, C.T. Haas, and S. Walbridge, “Pipe radius estimation using Kinect range cameras”, Autom. Constr. 99 (March 2017), 197–205 (2019).
  32.  G.H. Beckman, D. Polyzois, and Y.J. Cha, “Deep learning-based automatic volumetric damage quantification using depth camera”, Automation in Construction 99(November 2018), 114–124 (2019).
  33.  H. Kim, S. Lee, E. Ahn, M. Shin, and S.-H. Sim, “Crack identification method for concrete structures considering angle of view using RGB-D camera-based sensor fusion”, Struct. Heal. Monit., 1–13 (2020).
  34.  Intel, “Intel® RealSenseTM Camera D400 series Product Family Datasheet” (2019).
  35.  C. Rother, V. Kolmogorov, and A. Blake, “‘GrabCut’: interactive foreground extraction using iterated graph cuts”, in ACM SIGGRAPH 2004 Papers on – SIGGRAPH ’04, 2004, p. 309.
  36.  Y. Li, J. Sun, C.K. Tang, and H.Y. Shum, “Lazy snapping”, ACM SIGGRAPH 2004 Pap. SIGGRAPH 2004, 303–308 (2004).
  37.  M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: Active contour models”, Int. J. Comput. Vis. 1(4), 321–331 (Jan. 1988).
  38.  M.A. Fischler and R.C. Bolles, “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography”, Commun. ACM 24(6), 381–395 (1981).
  39.  D. Holz and S. Behnke, “Fast Range Image Segmentation and Smoothing Using Approximate Surface Reconstruction and Region Growing”, 2013, 61–73.
  40.  M. Garland and P.S. Heckbert, “Surface simplification using quadric error metrics”, in Proceedings of the 24th annual conference on Computer graphics and interactive techniques – SIGGRAPH ’97, 1997, 209–216.
  41.  Intel, “Intel® RealSenseTM Camera: Depth testing methodology”, 2018.
  42.  B. Wójcik and M. Żarski, “Asesment of state-of-the-art methods for bridge inspection: case study”, Arch. Civ. Eng. 66(4), 343‒362 (2020).
  43.  K. He, G. Gkioxari, P. Dollar, and R. Girshick, “Mask R-CNN”, IEEE Trans. Pattern Anal. Mach. Intell. 42(2), 386–397 (2017).
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Authors and Affiliations

Bartosz Wójcik
1
ORCID: ORCID
Mateusz Żarski
1
ORCID: ORCID

  1. Department of Mechanics and Bridges, Faculty of Civil Engendering, Silesian University of Technology, ul. Akademicka 5, 44-100 Gliwice, Poland
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Abstract

Present paper is a continuation of works on evaluation of red, green, blue (RGB) to hue, saturation, intensity (HSI) colour space transformation in regard to digital image processing application in optical measurements methods. HSI colour space seems to be the most suitable domain for engineering applications due to its immunity to non-uniform lightning. Previous stages referred to the analysis of various RGB to HSI colour space transformations equivalence and programming platform configuration influence on the algorithms execution. The main purpose of this step is to understand the influence of computer processor architecture on the computing time, since analysis of images requires considerable computer resources. The technical development of computer components is very fast and selection of particular processor architecture can be an advantage for fastening the image analysis and then the measurements results. In this paper the colour space transformation algorithms, their complexity and execution time are discussed. The most common algorithms were compared with the authors own one. Computing time was considered as the main criterion taking into account a technical advancement of two computer processor architectures. It was shown that proposed algorithm was characterized by shorter execution time than in reported previously results.

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Authors and Affiliations

Andrzej Ziemba
Elżbieta Fornalik-Wajs

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