[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128449-en":3,"doc-seo-128449-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},128449,962085662650,"Jiven","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Data-driven classification of the chemical composition of calcine in a ferronickel furnace oven using machine learning techniques","Calcines’ chemical composition strongly affects ferronickel smelting quality, yet offline chemical tests require several days and can delay operations. The study proposes a data-driven method for online classification by combining a clustering approach with a mixed Principal Component Analysis (PCA) model, standardized data processing, and an Extreme Gradient Boosting (XGBoost) classifier. Predictions use current furnace operating conditions to estimate calcine chemistry without waiting for lab results. The approach achieved mean accuracy between 82.1% and 85.9%, showing promising performance compared with other methods.","Results in Engineering 18 (2023) 101028  \nContents lists available at ScienceDirect  \nResults in Engineering  \njournal [homepage:](homepage: www.sciencedirect.com/journal/results-in-engineering)[ www.sciencedirect.com/journal/results-in-engineering](homepage: www.sciencedirect.com/journal/results-in-engineering)  \n| Research paper\u003Cbr>Data-driven classification of the chemical composition of calcine in aferronickel furnace oven using machine learning techniques\u003Cbr>Diego A. Velandia Cardenas a, Jersson X. Leon-Medina b,c, Erwin Jose Lopez Pulgarin d, Jorge Iván Sofronyb,∗\u003Cbr>a Department of Electrical and Electronic Engineering, Universidad Nacional de Colombia, Sede Bogotá, Colombia b Department of Mechanical and Mechatronics Engineering, Universidad Nacional de Colombia, Sede Bogotá, Colombia\u003Cbr>c Control, Data and Artificial Intelligence (CoDAlab), Department of Mathematics, Escola d’Enginyeria de Barcelona Est (EEBE), Universitat Politècnica de Catalunya (UPC), Campus Diagonal-Besòs (CDB), Barcelona, Spain\u003Cbr>d Department of Electrical and Electronic Engineering (EEE), University of Manchester, Manchester, UK |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords: Clustering\u003Cbr>Factorial multivariate analysis\u003Cbr>FactoClass Furnace monitoring k-means clustering Search-grid XGBoost |  | Calcines’ chemical composition analysis is a key process in ferronickel smelting. These values allow for a clear understanding of the smelted product’s expected quality, catering for any required chemical upgrading of the raw material or modification in the furnace’s set-point if the calcine has undesired characteristics. Offline tests for calcines’ chemical composition can take several days, potentially delaying the whole operation. A datadriven approach to chemical composition classification using on-line data is proposed by combining clustering classification through a mixed Principal Component Analysis (PCA) model, data processing and standardization process, with a Machine Learning classification algorithm, i.e. Extreme Gradient Boosting (XGBoost). This allows for an online prediction of calcines’ chemical composition based on the furnace’s current operating conditions. The proposed method’s accuracy scored mean values between 82.1% and 85.9%, which is encouraging in comparison with other proposed methods. |\n\n1. Introduction  \nFerronickel is an alloy made of nickel and iron, and it is an essential component used to manufacture stainless-steel and other alloys used ina wide range of fields, such as medical tools and industrial machinery. Ferronickel is highly important for social growth, but unfortunately, its production is energy-intensive with a significant environmental impact. Cerro Matoso S.A (CMSA), one of the world’s largest nickel producers, is Colombia’s biggest consumer of electrical power on the national grid, representing approximately 2% of the country’s total installed capacity. This highlights the importance of optimizing the ferronickel smelting production processes and increasing the system’s energy efficiency.  \nCMSA is a lateritic nickel ore extraction, mining and smelting operation with open-pit exploitation which began in 1982, producing 36,000 tons of ferronickel per year [1]. Raw ore is extracted from an openpit mine, later classified into piles, dried out and pre-burned in a rotary kiln furnace to produce a base calcine that is then further processed. Arc electrodes provide the energy input to heat the calcine up to smelting  \n* Corresponding author.  \nE-mail address: [jsofronye@unal.edu.co](jsofronye@unal.edu.co) (J.I. Sofrony).  \ntemperatures. Throughout the process, smelted metal (FeNi) separates from other components and flows to the bottom of the furnace by density difference. Unwanted minerals clump into a by-product known as slag. Both ferronickel and slag are moved out from the furnace in liquid state by overflow through a dedicated runner system [2]. To monitor the structure of the furnace lin","cbCaibgmZXw4ikdp","https://ap.wps.com/l/cbCaibgmZXw4ikdp","pdf",1816187,1,12,"English","en",105,"# Abstract\n# Keywords\n# Introduction\n## Ferronickel smelting background\n## Calcine production and furnace monitoring\n## Furnace inputs, process, and outputs","[{\"question\":\"Why is classification of calcines’ chemical composition important in ferronickel smelting?\",\"answer\":\"It helps predict the expected quality of the smelted product, enabling chemical upgrading of raw material or adjustments to furnace set-point when calcine characteristics are undesired.\"},{\"question\":\"What problem does the proposed method address?\",\"answer\":\"Offline chemical composition tests take several days, which can delay the overall operation.\"},{\"question\":\"How does the method predict calcines’ chemical composition online?\",\"answer\":\"It combines clustering using a mixed PCA model, data processing and standardization, and an XGBoost machine learning classifier to infer composition from current furnace operating conditions.\"}]","Data-driven classification of the chemical composition of calcine in a ferronickel furnace oven using machine learning techniques | 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