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

Abstract

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–SiO₂–Al₂O₃–MgO system with an objective to be used in ferronickel refining processes. The resulting models are evaluated on: 1) coefficient of multiple determination (R²), 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.