Influence of alumina on physical properties of an industrial zinc-copper smelting slagPart 1 – viscosity

Mostaghel, Sina; Matsushita, Taishi; Samuelsson, Caisa; Björkman, Bo; Seetharaman, Seshadri

doi:10.1179/1743285512y.0000000029

Cited by 15 publications

(9 citation statements)

References 35 publications

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“…Thus, how alumina is coordinated in the system is dependent on the base slag and the ratio of basic/acidic oxides in the system. The amphoteric behavior of alumina in melts, slags and glasses has been investigated quite broadly by measuring physical properties such as density, viscosity, surface tension, thermal and electrical conductivity [15][16][17][18]. As alumina influences the chemical and physical properties of the slags, naturally it has an influence on the minor element behavior, too.…”

Section: Introductionmentioning

confidence: 99%

The Influence of Aluminum on Indium and Tin Behaviour During Secondary Copper Smelting

Avarmaa

2018

The Minerals, Metals &Amp; Materials Series

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Aluminum and copper are large volume metals in electronic appliances, while tin and indium exist as common minor elements. All of these non-ferrous metals are aimed to be separated and recycled from the end-of-life electronics into non-ferrous scrap fraction(s), and further through pyrometallurgical and/or hydrometallurgical processes to pure metals. Depending on the mechanical pre-treatment processes, aluminum and copper liberation from each other varies. This study focuses on the influence of alumina on indium and tin distributions between copper alloy and iron silicate slags with 0, 9 and ~16 wt% of Al2O3. The experiments were executed with an equilibration-quenching-EPMA technique in an oxygen pressure range of 10-10-10-5 atm at 1300 °C. The metal-slag distribution coefficient of indium remains constant as a function of alumina in slag, while that of tin increases. Therefore, aluminum in feed or alumina addition to the slag improves the recovery of tin into copper. Nevertheless, oxygen pressure has clearly more significant influence on the behavior of both the metals in the smelting conditions.

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Section: Introductionmentioning

confidence: 99%

The Influence of Aluminum on Indium and Tin Behaviour During Secondary Copper Smelting

Avarmaa

2018

The Minerals, Metals &Amp; Materials Series

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“…It can be observed that as the SRM addition changes from 1 ton to 23 tons, the liquidus temperature of the slag decreases by 40°C. The decrease of the liquidus temperature and the increase of the viscosity could be related to the addition of Al 2 O 3 to the slag, as Mostaghel et al [14,15] reported in their study of the effect of the addition of Al 2 O 3 to slag after the fuming process in the Boliden-Rönnskär smelter. Additionally, the increase of the SiO 2 /Fe ratio could lead to a decrease of the liquidus temperature, as was stated by Hayes et al [9].…”

Section: Parameters Influencing Utilisation Of Srm In the Processmentioning

confidence: 83%

Evaluating the potential of plastic-containing materials as alternative reducing agents

Lotfian

Vikström

Lennartsson

et al. 2019

Canadian Metallurgical Quarterly

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The amount of discarded plastic-containing materials is increasing, and one option to help with this issue is to use these materials in bath smelting processes. The injection of plastic-containing materials to partially substitute coal in zinc-fuming processes has been studied in an industrial trial at Boliden-Rönnskär smelter. To evaluate the potential of plastic-containing materials, thermodynamic calculations were performed in this study. In the first step, a thermodynamic calculation was performed for trials with only coal injection, and then this calculation was applied to trials with the co-injection of plastic materials. The thermodynamic calculation shows that not all the injected coal participates in the reactions within the slag. Similarly, the calculation with the co-injection of plastic-containing materials shows that different amounts of each plastic material participate in the reactions within the slag bath. RÉSUMÉ La quantité de matériaux contenant du plastique et mise au rebut augmente, et une option pour aider avec ce problème est d'utiliser ces matériaux dans les procédés de fusion de bain. On a étudié l'injection de matériaux contenant du plastique pour substituer partiellement le charbon dans les procédés de volatilisation du zinc lors d'un essai industriel au four de fusion de Boliden-Rönnskär. Pour évaluer le potentiel des matériaux contenant du plastique, on a effectué des calculs thermodynamiques dans cette étude. Au cours de la première étape, on a effectué un calcul thermodynamique pour les essais avec uniquement des injections de charbon, et l'on a ensuite appliqué ce calcul aux essais de co-injection de matériaux plastiques. Le calcul thermodynamique montre que ce n'est pas tout le charbon injecté qui participe dans les réactions avec la scorie. Similairement, le calcul avec la coinjection de matériaux contenant du plastique montrait que différentes quantités de chaque matériau plastique participent dans les réactions avec le bain de scorie.

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“…Similarly, work by Kang et al 16) using FactSage predicted a positive correlation but only at high mole fractions ( > 0.6). Conversely, KTH's Thermoslag, 18) showed a negative correlation.…”

Section: )mentioning

confidence: 86%

“…Some of the more famous frameworks in this area have been incorporated into proprietary software such as FactSage (developed by CRCT and GTT Technologies), 16,17) and ThermoSlag (developed by the Royal Institute of Technology (KTH)). 18) Comprising many different databases as well as frameworks, FactSage is an all-purpose thermodynamics software. It's sulphide capacity framework, developed by Kang and Pelton,16) is based on the extrapolation of all permutations of cations and anions on a pseudo-lattice.…”

Section: Short Range Ordermentioning

confidence: 99%

Development of an Artificial Neural Network to Predict Sulphide Capacities of CaO–SiO2–Al2O3–MgO Slag System

Mostaghel

Chattopadhyay

2017

ISIJ Int.

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Depletion of the high quality ores around the world has forced ferronickel producers to extract metal values from low-grade ore bodies with significant amounts of impurities. Under this condition, maintaining alloy quality is of utmost importance for the smelters; however still, accessibility of a reliable sulphide capacity model for FeNi refining processes is an issue. Many of the current models, such as those incorporating optical basicity, have proven to be erroneous and unreliable for wide ranges of composition and temperature. These models are typically developed and tested without a proper validation method thus allowing for great correlations which may not fare well with the introduction of new data. Models built from fundamental thermodynamic data perform much better in predicting sulphide capacities but are not only complicated to formulate but also too complicated to be used by operators on a day to day basis as multitude of inputs are needed. Hence, development of a reliable model based on fundamentals, which can also be directly used by plant operators is very much demanded by the industry. In the current study, an artificial neural network (ANN) approach has been used to predict sulphide capacities of slag compositions in the CaO-SiO 2 -Al 2 O 3 -MgO system with an objective to be used in ferronickel refining processes. The resulting models are evaluated on: 1) coefficient of multiple determination (R 2 ), 2) correlation strength (r), 3) root mean square error (RMSE) and 4) computation speed. The ANN based model has shown to be superior in predicting sulphide capacities to current models.

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Influence of alumina on physical properties of an industrial zinc-copper smelting slagPart 1 – viscosity

Cited by 15 publications

References 35 publications

The Influence of Aluminum on Indium and Tin Behaviour During Secondary Copper Smelting

The Influence of Aluminum on Indium and Tin Behaviour During Secondary Copper Smelting

Evaluating the potential of plastic-containing materials as alternative reducing agents

Development of an Artificial Neural Network to Predict Sulphide Capacities of CaO–SiO<sub>2</sub>–Al<sub>2</sub>O<sub>3</sub>–MgO Slag System

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