{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:14:12Z","timestamp":1760148852349,"version":"build-2065373602"},"reference-count":33,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,6,12]],"date-time":"2023-06-12T00:00:00Z","timestamp":1686528000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>During the secondary production of aluminum, upon melting the scrap in a furnace, there is the possibility of developing an aluminothermic reaction, which produces oxides in the molten metal bath. Aluminum oxides must be identified and removed from the bath, as they modify the chemical composition and reduce the purity of the product. Furthermore, accurate measurement of molten aluminum level in a casting furnace is crucial to obtain an optimal liquid metal flow rate which influences the final product quality and process efficiency. This paper proposes methods for the identification of aluminothermic reactions and molten aluminum levels in aluminum furnaces. An RGB Camera was used to acquire video from the furnace interior, and computer vision algorithms were developed to identify the aluminothermic reaction and melt level. The algorithms were developed to process the image frames of video acquired from the furnace. Results showed that the proposed system allowed the online identification of the aluminothermic reaction and the molten aluminum level present inside the furnace at a computation time of 0.7 s and 0.4 s per frame, respectively. The advantages and limitations of the different algorithms are presented and discussed.<\/jats:p>","DOI":"10.3390\/s23125506","type":"journal-article","created":{"date-parts":[[2023,6,12]],"date-time":"2023-06-12T02:28:42Z","timestamp":1686536922000},"page":"5506","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Identification of Aluminothermic Reaction and Molten Aluminum Level through Vision System"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0201-6332","authenticated-orcid":false,"given":"Yuvan Sathya","family":"Ravi","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, Politecnico di Milano, 20156 Milan, Italy"}]},{"given":"Fabio","family":"Conti","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Politecnico di Milano, 20156 Milan, Italy"}]},{"given":"Paolo","family":"Fasoli","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Politecnico di Milano, 20156 Milan, Italy"}]},{"given":"Emanuele Della","family":"Bosca","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Politecnico di Milano, 20156 Milan, Italy"}]},{"given":"Maurizio","family":"Colombo","sequence":"additional","affiliation":[{"name":"One-Off Innovation, 23900 Lecco, Italy"}]},{"given":"Andrea","family":"Mazzoleni","sequence":"additional","affiliation":[{"name":"One-Off Innovation, 23900 Lecco, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2640-1764","authenticated-orcid":false,"given":"Marco","family":"Tarabini","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Politecnico di Milano, 20156 Milan, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,12]]},"reference":[{"key":"ref_1","unstructured":"Tabereaux, A.T., and Peterson, R.D. (2014). Treatise on Process Metallurgy, Elsevier."},{"key":"ref_2","unstructured":"Davis, J.R. (1993). Aluminum and Aluminum Alloys, ASM International."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"13","DOI":"10.4028\/www.scientific.net\/MSF.693.13","article-title":"A Historical Perspective on Dross Processing","volume":"693","author":"Peterson","year":"2011","journal-title":"Mater. Sci. Forum"},{"key":"ref_4","unstructured":"Roth, D.J. (2015). Light Metals 2015, Springer."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"04014018","DOI":"10.1061\/(ASCE)HZ.2153-5515.0000223","article-title":"Classification and reactivity of secondary aluminum production waste","volume":"18","author":"Jafari","year":"2014","journal-title":"J. Hazard. Toxic Radioact. Waste"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"104","DOI":"10.4028\/www.scientific.net\/MSF.693.104","article-title":"Cost Savings in the Cast House through Optimizing Furnace Operation, Staff Training and Associated Variables","volume":"693","author":"Grayson","year":"2011","journal-title":"Mater. Sci. Forum"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1080\/00986449808912342","article-title":"Mechanism of Structure Formation in Self-Propagating Thermite Reactions: The Case of Alumina as Diluent","volume":"163","author":"Simoncini","year":"1998","journal-title":"Chem. Eng. Commun."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1943","DOI":"10.1007\/s10694-020-00986-y","article-title":"Video Flame and Smoke Based Fire Detection Algorithms: A Literature Review","volume":"56","author":"Gaur","year":"2020","journal-title":"Fire Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"30","DOI":"10.32629\/jai.v2i1.35","article-title":"Flame recognition in video images with color and dynamic features of flames","volume":"2","author":"Chen","year":"2019","journal-title":"J. Auton. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/S0167-8655(01)00135-0","article-title":"Flame recognition in video","volume":"23","author":"Phillips","year":"2002","journal-title":"Pattern Recognit. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Li, W., Mao, K., Zhou, X., Chai, T., and Zhang, H. (2009, January 15\u201318). Eigen-flame image-based robust recognition of burning states for sintering process control of rotary kiln. Proceedings of the 48h IEEE Conference on Decision and Control (CDC) Held Jointly with 2009 28th Chinese Control Conference, Piscataway, NJ, USA.","DOI":"10.1109\/CDC.2009.5400123"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1365","DOI":"10.1016\/j.engappai.2012.05.007","article-title":"Fire flame detection in video sequences using multi-stage pattern recognition techniques","volume":"25","author":"Truong","year":"2012","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_13","first-page":"111760W","article-title":"Evaluation of the possibility of using RGB image analysis to co-firing process","volume":"11176","author":"Sawicki","year":"2019","journal-title":"Proc.SPIE"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Toreyin, B.U., Dedeoglu, Y., and Cetin, A.E. (2005, January 14). Flame detection in video using hidden Markov models. Proceedings of the IEEE International Conference on Image Processing 2005, Genoa, Italy.","DOI":"10.1109\/ICIP.2005.1530284"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Pesatori, A., Norgia, M., and Svelto, C. (2009, January 5\u20137). Automated vision system for rapid fire onset detection. Proceedings of the 2009 IEEE Instrumentation and Measurement Technology Conference, Singapore.","DOI":"10.1109\/IMTC.2009.5168436"},{"key":"ref_16","first-page":"265","article-title":"Understanding color models: A review","volume":"2","author":"Ibraheem","year":"2012","journal-title":"ARPN J. Sci. Technol."},{"key":"ref_17","unstructured":"Grandfield, J.F., and McGlade, P.T. (1996). DC casting of aluminum: Process behaviour and technology. Mater. Forum-Rushcutters Bay, 29\u201351. Available online: http:\/\/www.congnghe-sx.com\/upload\/files\/casting1(1).pdf."},{"key":"ref_18","unstructured":"Geldenhuis, J.M.A., Miller, D., Van Beek, B., Ndlovu, J., and Hara, K.T. (2004). Development of alternative techniques for matte level measurements in sulphide smelting furnaces. Int. Platin. Conf. Platin. Adding Value, 25\u201332. Available online: http:\/\/saimm.org.za\/Conferences\/Pt2004\/025_Geldenhuis.pdf."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"39","DOI":"10.7733\/jnfcwt.2015.13.S.39","article-title":"Liquid level measurement by the detection of abrupt pressure changes in a tube in contact with a liquid surface","volume":"13","author":"Lee","year":"2015","journal-title":"J. Korean Radioact. Waste Soc."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3456","DOI":"10.1109\/TIM.2018.2879548","article-title":"Molten Steel Level Detection from Thermal Image Sequence Based on the Characteristics of Adhesive Flux","volume":"68","author":"He","year":"2019","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"224","DOI":"10.2355\/isijinternational.43.224","article-title":"Level meter for the electromagnetic continuous casting of steel billet","volume":"43","author":"Kim","year":"2003","journal-title":"ISIJ Int."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3118","DOI":"10.1109\/TIM.2019.2929613","article-title":"Electromagnetic Measurement of Molten Metal Level in Pyrometallurgical Furnaces","volume":"69","author":"Saleem","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhuang, Z., Zhang, Y., Zhou, T., and Ji, S. (2020, January 15\u201317). Capacitance Measurement of Molten Metal Level in Continuous Casting System. Proceedings of the 2020 International Conference on Sensing, Measurement & Data Analytics in the Era of Artificial Intelligence (ICSMD), Xi\u2019an, China.","DOI":"10.1109\/ICSMD50554.2020.9261745"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1674","DOI":"10.2355\/isijinternational.51.1674","article-title":"Molten Steel Level Measurement in Tundish with Heat Transfer Analysis","volume":"51","author":"Hu","year":"2011","journal-title":"ISIJ Int."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Zhou, Y., Liu, H., Zhang, L., and Wang, H. (2019). Visual measurement of water level under complex illumination conditions. Sensors, 19.","DOI":"10.3390\/s19194141"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Bobovnik, G., Mu\u0161i\u010d, T., and Kutin, J. (2021). Liquid level detection in standard capacity measures with machine vision. Sensors, 21.","DOI":"10.3390\/s21082676"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"223","DOI":"10.25007\/ajnu.v7n4a293","article-title":"Real-time liquid level and color detection system using image processing","volume":"7","author":"Samann","year":"2018","journal-title":"Acad. J. Nawroz Univ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.measurement.2018.05.100","article-title":"Automatic water-level detection using single-camera images with varied poses","volume":"127","author":"Lin","year":"2018","journal-title":"Measurement"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1496","DOI":"10.1117\/1.600686","article-title":"Three-dimensional measurement using a single image","volume":"35","author":"Iovenitti","year":"1996","journal-title":"Opt. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1145\/361237.361242","article-title":"Use of the Hough transformation to detect lines and curves in pictures","volume":"15","author":"Duda","year":"1972","journal-title":"Commun. ACM"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"813","DOI":"10.1109\/TIP.2014.2387020","article-title":"Accurate and Robust Line Segment Extraction Using Minimum Entropy with Hough Transform","volume":"24","author":"Xu","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1016\/j.ijleo.2017.08.040","article-title":"High precision Fast Line Detection of alignment and coupling for planar Optical Waveguide device","volume":"145","author":"Zheng","year":"2017","journal-title":"Optik"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1109\/TIP.2005.863021","article-title":"Line detection in images through regularized hough transform","volume":"15","author":"Aggarwal","year":"2006","journal-title":"IEEE Trans. Image Process."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/12\/5506\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:52:51Z","timestamp":1760125971000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/12\/5506"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,12]]},"references-count":33,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["s23125506"],"URL":"https:\/\/doi.org\/10.3390\/s23125506","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,6,12]]}}}