Smart manufacturing of nonferrous metallurgical processes: Review and perspectives

Sun, Bin; Dai, Juntao; Huang, Keke; Yang, Chunhua; Gui, Weihua

doi:10.1007/s12613-022-2448-x

Cited by 15 publications

(2 citation statements)

References 174 publications

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“…With measurement differences between titrimetric and EPMA methods below 0.28% in the concentrations of bulk elements such as iron, the EPMA results could be considered quantitative as well. In complex materials containing tens of elements in bulk or trace concentrations, such as copper slags, the simultaneous analysis of all elements in the periodic table may omit the need for the normalization of compositional assays, which could further simplify the development of mathematical and general solution models [37,38].…”

Section: Challenges In Obtaining Comparable Chemical Composition Of T...mentioning

confidence: 99%

Feasibility of Recovering Valuable and Toxic Metals from Copper Slag Using Iron-Containing Additives

Mitrašinović,

Yuankun,

Stopic

et al. 2023

Metals

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One of the greatest environmental challenges in metal extraction is the generation of a large amount of slag. Most of these slags contain insufficient amounts of valuable metals for economical revalorization, but these concentrations may be harmful for the environment. At present, more than 80% of the global copper products are obtained by the smelting process, where the major by-products are various slags containing a broad range of almost all known elements. In this study, valuable and potentially harmful elements were recovered from mining waste using gravity separation and gravity settling. The settling process was enhanced by injecting coke, ferrocarbon, ferrosilicon, and ferrosulfide. In total, 35 elements were detected in the samples using electron probe microanalysis. After the treatment, 89.4% of the valuable, toxic, and trace elements gathered in the newly formed matte after maintaining the copper slag for four hours at 1300 °C and adding ferrosilicon. The metallic constituents of slags could be an important source of raw materials and they could be considered an environmentally beneficial source of copper and other materials. Suggested practices can prevent harmful elements from entering the environment, generate value from the gathered metals, and make the remaining slag suitable for construction or mine backfill materials. The present article also assesses the challenges in slag processing by the pyrometallurgical route and provides a roadmap for further investigations and large-scale studies.

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Section: Challenges In Obtaining Comparable Chemical Composition Of T...mentioning

confidence: 99%

Feasibility of Recovering Valuable and Toxic Metals from Copper Slag Using Iron-Containing Additives

Mitrašinović,

Yuankun,

Stopic

et al. 2023

Metals

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“…In the actual industrial processes, a large number of KPIs cannot be directly detected using instruments and measurements because of the harsh production environment. − In the traditional blast furnace ironmaking process, monitoring and estimation of KPIs mainly depend on the experience of the site-workers and offline testing. This manual method usually costs much time, and its accuracy is not high, causing difficulty in accurately controlling the product quality in real time.…”

Section: Introductionmentioning

confidence: 99%

A Survey of Data-Driven Soft Sensing in Ironmaking System: Research Status and Opportunities

Yan,

Kong,

et al. 2024

ACS Omega

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Data-driven soft sensing modeling is becoming a powerful tool in the ironmaking process due to the rapid development of machine learning and data mining. Although various soft sensing techniques have been successfully used in both the sintering process and blast furnace, they have not been comprehensively reviewed. In this work, we provide an overview of recent advances on soft sensing in the ironmaking process, with a special focus on data-driven techniques. First, we present a general soft sensing development framework of the ironmaking process based on the mechanism analysis and process characteristics. Second, we provide a detailed taxonomy of current soft sensing methods categorized by their predictive tasks (i.e., quality indicators prediction, state parameters prediction, etc.). Finally, we outline several insightful and promising directions, such as self-supervised learning and digital twins in the ironmaking process, for future research.

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A Method for Anode Effect Prediction in Aluminum Electrolysis Cells Based on Multi-scale Time Series Modeling

Qiang,

Li,

Zhang

et al. 2024

The Minerals, Metals &Amp; Materials Series

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Smart manufacturing of nonferrous metallurgical processes: Review and perspectives

Cited by 15 publications

References 174 publications

Feasibility of Recovering Valuable and Toxic Metals from Copper Slag Using Iron-Containing Additives

Feasibility of Recovering Valuable and Toxic Metals from Copper Slag Using Iron-Containing Additives

A Survey of Data-Driven Soft Sensing in Ironmaking System: Research Status and Opportunities

A Method for Anode Effect Prediction in Aluminum Electrolysis Cells Based on Multi-scale Time Series Modeling

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