Processing of Goethitic Iron Ore Fines

Sharma, Jyoti; Sharma, T.; Mandre, N. R.

doi:10.1007/s40033-015-0075-7

Cited by 12 publications

(18 citation statements)

References 3 publications

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“…, [2], [3], [4], [5], [6] MgO-SiO2 [1], [7], [8] CaO-Al2O3 [1], [9], [10], [11], [12], [13] CaO-MgO-SiO2 [7], [14] CaO-Al2O3-SiO2 [1], [4], [10], [13], [14], [15], [16], [17], [18] MgO-Al2O3-SiO2 [8], [19] CaO-MgO-Al2O3 [17], [19] CaO-MgO-Al2O3-SiO2 [20], [14], [16], [21], [22], [23], [24], [25], [26], [27], [28] Group II MnO-SiO2 [2], [5], [6], [8] MnO-Al2O3 [8] CaO-MnO-SiO2 [2], [5], [6], [29] MgO-MnO-SiO2 [8], [30]<...>…”

Section: Systemsmentioning

confidence: 99%

Estimation of Sulfide Capacities of Multicomponent Slags using Optical Basicity

2013

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An empirical model has been developed based on optical basicity to calculate the sulfide capacity of metallurgical molten slag. The model estimated sulfide capacities agree well with the experimental data for CaO-MgO-FeO-MnO-TiO2-Al2O3-SiO2-CaF2 multicomponent slags. It is found that the abilities of increasing sulfide capacity in MO-SiO2 (M=Ca, Mg, Fe and Mn) melts follow the order: MnO>FeO>CaO>MgO. Also, substitution of CaO for CaF2 decreases the sulfide capacity and substitution of TiO2 for SiO2 increases the sulfide capacity.

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Section: Systemsmentioning

confidence: 99%

Estimation of Sulfide Capacities of Multicomponent Slags using Optical Basicity

2013

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“…9,13,[20][21][22][23][24][25][26][27][28][29][30][31][32] These studies examine different aspects of the slag system and thus represent a good staging point for the modeling of the whole spectrum of sulphide capacities. The variables of interest were those that are chemically pertinent to the determination of sulphide capacity (LogCs).…”

Section: Visualizationmentioning

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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“…Figure shows the relationship between the corrected optical basicity and experimental measured sulfide capacity at different temperature. The corrected optical basicity was calculated using the slag composition data taken from the literature . It should be noted that only basic Al 2 O 3 containing slags were selected to make sure the Al 3+ exits in the form of tetrahedral coordination.…”

Section: Model Descriptionmentioning

confidence: 99%

“…Sulfide capacity defined by Fincham and Richardson describes the potential ability of slag to absorb sulfur and it depends only on slag composition and temperature . Sulfide capacities of CaO‐Al 2 O 3 ‐SiO 2 ‐MgO slags (at temperature of 1673–1928 K) have been measured by many researchers . Also, several models have been established to predict the sulfide capacity of slags.…”

Section: Introductionmentioning

confidence: 99%

A New Sulfide Capacity Model for CaO-Al₂O₃-SiO₂-MgO Slags Based on Corrected Optical Basicity

Hao

Wang

2015

steel research int.

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A new sulfide capacity model for CaO‐Al2O3‐SiO2‐MgO slags has been developed based on corrected optical basicity. The present model has taken charge compensation into consideration and postulated that Al exits in the form of [1/2Ca(AlO4)]4– when NCaO is higher than NAl2O3 in slag. The model predicted sulfide capacities agree well with experimental measured values for CaO‐Al2O3, CaO‐Al2O3‐SiO2, CaO‐Al2O3‐MgO, and CaO‐Al2O3‐SiO2‐MgO slags. A mean deviation of 3.23% is achieved for the present model in basic Al2O3 containing slags and this value is obviously lower than that of other models. The applicable range of the present model is given.

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Processing of Goethitic Iron Ore Fines

Cited by 12 publications

References 3 publications

Estimation of Sulfide Capacities of Multicomponent Slags using Optical Basicity

Estimation of Sulfide Capacities of Multicomponent Slags using Optical Basicity

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

A New Sulfide Capacity Model for CaO-Al₂O₃-SiO₂-MgO Slags Based on Corrected Optical Basicity

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Processing of Goethitic Iron Ore Fines

Cited by 12 publications

References 3 publications

Estimation of Sulfide Capacities of Multicomponent Slags using Optical Basicity

Estimation of Sulfide Capacities of Multicomponent Slags using Optical Basicity

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

A New Sulfide Capacity Model for CaO-Al2O3-SiO2-MgO Slags Based on Corrected Optical Basicity

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A New Sulfide Capacity Model for CaO-Al₂O₃-SiO₂-MgO Slags Based on Corrected Optical Basicity