The supply of defect-free, high-quality products is an important success factor for the long-
term competitiveness of manufacturing companies. Despite the increasing challenges of …

Radial basis function (RBF) model has been widely used in complex engineering design
process to replace the computational-intensive simulation models. This paper proposes a …

In the prediction of the end-point molten steel temperature of the ladle furnace, the influence
of some factors is nonlinear. The prediction accuracy will be affected by directly inputting …

Abstract General Regression Neural Network (GRNN) possesses distinct function
approximation capability and predictive power without the requirement of a prescribed …

The requirement for intelligent steelmaking has underlined the significance of data-driven
predictions of molten steel temperature in ladle furnace. Recently, predictors based on …

Multifidelity surrogates (MFSs) replace computationally intensive models by synergistically
combining information from different fidelity data with a significant improvement in modeling …

With the improvement of computer computing power and the development of big data
technology, neural networks have rapidly developed and been effectively applied in multiple …

With the continuous optimization of the steel production process and the increasing
emergence of smelting methods, it has become difficult to monitor and control the production …

The machine learning models of multiple linear regression (MLR), support vector regression
(SVR), and extreme learning machine (ELM) and the proposed ELM models of online …

In the present work, the endpoint sulfur content prediction model of Kambara Reactor (KR)
desulfurization in the steelmaking process is investigated. For Artificial Neural Network …