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.

No abstract

No abstract

The rotating cylinder method was applied to measure the viscosities of an industrial iron silicate slag and mixtures of this slag with 5, 10 and 15 wt-% alumina addition, in temperature range 1100-1300uC. The measured viscosities were compared with the predicted values using two of the commercially available software products for viscosity calculations, namely ThermoslagH1?5 and FactSage TM 6?2. As the models can only predict viscosities for a solid free melt, obtained values by FactSage TM 6?2 were modified using the Einstein-Roscoe equation. Results show that aluminium behaves as a network former cation in this type of slag, and by increasing the alumina concentration, the melt becomes progressively polymerised. Consequently, the viscosity of the slag increases at a given temperature, which is supported by thermodynamic predictions. According to the modified FactSage TM 6?2 calculations, the viscosity of the solid containing slag increases from 2?1 to 5?5 poise at the industrial operating temperature (y1250uC).

No abstract