Data-driven classification of the chemical composition of calcine in a ferronickel furnace oven using machine learning techniques

Abstract

Calcines' chemical composition analysis is a key process in ferronickel smelting. These values allow for a clear understanding of the smelted product's expected quality, catering for any required chemical upgrading of the raw material or modification in the furnace's set-point if the calcine has undesired characteristics. Offline tests for calcines' chemical composition can take several days, potentially delaying the whole operation. A data-driven approach to chemical composition classification using on-line data is proposed by combining clustering classification through a mixed Principal Component Analysis (PCA) model, data processing and standardization process, with a Machine Learning classification algorithm, i.e. Extreme Gradient Boosting (XGBoost). This allows for an online prediction of calcines' chemical composition based on the furnace's current operating conditions. The proposed method's accuracy scored mean values between 82.1% and 85.9%, which is encouraging in comparison with other proposed methods.

Keywords

• Clustering
• Factorial multivariate analysis
• FactoClass
• Furnace monitoring
• k-means clustering
• Search-grid