An automated system for calibration table calculation of cylindrical horizontal tanks
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Main Article Content
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Abstract
The article presents the results of the development and implementation of the software "Automated Calculation of Calibration Table" for the calibration of steel cylindrical horizontal tanks obtained using 3D laser scanning. The proposed system automates the processing of 3D point clouds using a hybrid segmentation method (RANSAC and DBSCAN) and improved geometric formulas for calculating a per-millilitre capacity table. The results of calculating volumes and capacity coefficients are compared with alternative enterprise software, which demonstrates an increase in accuracy by 5–15% and a reduction in processing time by 70–80%. The developed software adapts to tank deformations, ensuring reliability in industrial conditions, which is confirmed by data analysis for 10 tanks with a cloud density of 5–15 million points.
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References
References
[1] Adekitan, A. I., & Omoruyi, O. (2018). Stock keeping accuracy: A data based investigation of storage tank calibration challenges. Data in Brief, 19, 2155–2162. https://doi.org/10.1016/j.dib.2018.06.122 DOI: https://doi.org/10.1016/j.dib.2018.06.122
[2] Agboola, O. O., Akinnuli, B. O., Akintunde, M. A., Ikubanni, P. P., & Adeleke, A. A. (2019). Comparative Analysis of Manual Strapping Method (MSM) and Electro-Optical Distance Ranging (EODR) Method of Tank Calibration. Journal of Physics: Conference Series, 1378(2), 022062. https://doi.org/10.1088/1742-6596/1378/2/022062 DOI: https://doi.org/10.1088/1742-6596/1378/2/022062
[3] Agboola, O. O., Akinnuli, B. O., Kareem, B., Akintunde, M. A., Ikubanni, P. P., & Adeleke, A. A. (2024). Rating of Geometrical Methods of Tank Calibration: F-TOPSIS Approach. MAPAN, 39(3), 653–662. https://doi.org/10.1007/s12647-024-00748-z DOI: https://doi.org/10.1007/s12647-024-00748-z
[4] Castro, H. F. F. (2024). Mathematical modeling applied to the uncertainty analysis of a tank prover calibration: Understanding the influence of calibration conditions on the GUM validation using the Monte Carlo method. Flow Measurement and Instrumentation, 96, 102547. https://doi.org/10.1016/j.flowmeasinst.2024.102547 DOI: https://doi.org/10.1016/j.flowmeasinst.2024.102547
[5] del Horno, L., Segura, E., Somolinos, J. A., & Morales, R. (2023). A New Methodology-Based Sensorial System with Which to Determine the Volume of Liquid Contained in a Cylindrical Tank Subjected to Full Variations in Its Orientation. Journal of Marine Science and Engineering, 11(12), 2316. https://doi.org/10.3390/jmse11122316 DOI: https://doi.org/10.3390/jmse11122316
[6] Goda, I., L’Hostis, G., & Guerlain, P. (2019). In-situ non-contact 3D optical deformation measurement of large capacity composite tank based on close-range photogrammetry. Optics and Lasers in Engineering, 119, 37–55. https://doi.org/10.1016/j.optlaseng.2019.02.006 DOI: https://doi.org/10.1016/j.optlaseng.2019.02.006
[7] Hu, M.-S., & Tao, C.-R. (2016). Calculation of oil tank volume and report generation system with trim and list corrections. Transactions of the Canadian Society for Mechanical Engineering, 40(5), 835–845. https://doi.org/10.1139/tcsme-2016-0068 DOI: https://doi.org/10.1139/tcsme-2016-0068
[8] International Organization for Standardization, Petroleum and liquid petroleum products. (2001). Tank calibration by liquid measurement – Incremental method using volumetric meters (ISO 4269:2001).
[9] International Organization for Standardization, Petroleum and liquid petroleum products. (2002). Calibration of horizontal cylindrical tanks – Part 2: Internal electro-optical distance-ranging method (ISO 12917-2:2002).
[10] International Organization for Standardization, Petroleum and liquid petroleum products. (2017). Calibration of horizontal cylindrical tanks – Part 1: Manual methods (ISO 12917-1:2017).
[11] Knyva, M., Knyva, V., Meškuotienė, A., Kuzas, P., Gailius, D., & Nakutis, Ž. (2020). 3D Laser Scanning Pointcloud Processing Uncertainty Estimation for Fuel Tank Volume Calibration. MAPAN, 35(3), 333–341. https://doi.org/10.1007/s12647-020-00367-4 DOI: https://doi.org/10.1007/s12647-020-00367-4
[12] Knyva, M., Knyva, V., Nakutis, Ž., Dumbrava, V., & Saunoris, M. (2017). A Concept of Fuel Tank Calibration Process Automation Within IoT Infrastructure. MAPAN, 32(1), 7–15. https://doi.org/10.1007/s12647-016-0193-1 DOI: https://doi.org/10.1007/s12647-016-0193-1
[13] Meškuotienė, A., Kaškonas, P., Raudienė, E., Dobilienė, J., & Urbonavičius, B. G. (2022). Calibration Periodicity of Fuel Tanks Assigned to Legal–Industrial Metrology: A Case Study. Sustainability, 14(16), 9817. https://doi.org/10.3390/su14169817 DOI: https://doi.org/10.3390/su14169817
[14] Milinkovic, A. (2018). 3D laser scanning and imaging laser scanning in storage tanks metrology control process. STEPGRAD, 13(1). https://doi.org/10.7251/STP1813794M DOI: https://doi.org/10.7251/STP1813794M
[15] Milinković, A., García-Balboa, J. L., & Ruiz-Armenteros, A. M. (2020). An Overview of Methods Applied To Quality Control Of Storage Tanks Volume. 17th IMEKO TC10 Conference "Global trends in Testing, Diagnostics & Inspection for 2030”, Dubrovnik, Croatia.
[16] Okeke, A. (2021). Towards sustainability in the global oil and gas industry: Identifying where the emphasis lies. Environmental and Sustainability Indicators, 12, 100145. https://doi.org/10.1016/j.indic.2021.100145 DOI: https://doi.org/10.1016/j.indic.2021.100145
[17] Proskurenko, D., & Bezuglyi, M. (2025). Integrated approach for 3D point cloud segmentation in tank calibration. Bulletin of Kyiv Polytechnic Institute. Series Instrument Making, (69(1)), 75–81. https://doi.org/10.20535/1970.69(1).2025.333512 DOI: https://doi.org/10.20535/1970.69(1).2025.333512
[18] Samoilenko, O., & Zaets, V. (2022). Calibration of Tanks and Ships’ Tanks for Storage and Transportation of Liquids by Laser Scanning. In O. Velychko (Ed.), Applied Aspects of Modern Metrology. IntechOpen. https://doi.org/10.5772/intechopen.100565 DOI: https://doi.org/10.5772/intechopen.100565
[19] State Enterprise "All-Ukrainian State Scientific and Production Center for Standardization, Metrology, Certification and Consumer Rights Protection". (2016). Horizontal Cylindrical Steel Tankers. Methodology for Verification (Calibration) by Geometric Method Using Geodetic Instruments (DSTU Patent No. 7475).
[20] Wan, Y., Wang, L., Gao, F., Tian, P., & Lin, J. (2021). Processing Method of Horizontal Tank Capacity Based on VAB and OLE Technology. 2021 IEEE 15th International Conference on Electronic Measurement & Instruments (ICEMI), 292–295. https://doi.org/10.1109/ICEMI52946.2021.9679585 DOI: https://doi.org/10.1109/ICEMI52946.2021.9679585
[21] Zhou, J., & Zhang, J. (2016). Monte Carlo Simulation on Automatic Calibration Method of Horizontal Tanks. In E. Qi (Ed.), Proceedings of the 6th International Asia Conference on Industrial Engineering and Management Innovation (pp. 571–578). Atlantis Press. https://doi.org/10.2991/978-94-6239-145-1_54 DOI: https://doi.org/10.2991/978-94-6239-145-1_54
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