Predicting viscosity for steelmaking slag: A stacking regression approach

Abstract

The viscosity of steel slag plays a pivotal role in steelmaking, impacting various aspects of the steelmaking operation and product quality. This paper introduces a novel approach for predicting the viscosity of turndown slag from a basic oxygen furnace using a stacking regression model. The stacking model in this approach incorporates four base models: multiple linear regression (LR), random forest (RF), k-nearest neighbour (KNN) and AdaBoost (ADB), while random forest is selected as a meta-model. The selection of meta-models is done through a systematic framework provided for identifying suitable meta-models in a stacking model. Similarly, the framework also demonstrated the workflow for constructing and evaluating a series of stacking models with different configurations. On the evaluation of 20 stacking models (including two and three-layers), the optimal configuration is found to be LR + KNN with R2 value of 0.993 and the shortest training time. Additionally, a correlation analysis of slag viscosity with composition and temperature is done, which will help the operators of steel melting shops in optimize the steelmaking process based on the required slag viscosity. Ultimately, this study emphasizes the efficacy of optimal-configured stacking regression models in industrial applications, achieving both maximum accuracy and minimal computational time.