Application of Artificial Neural Networks to Improve Steel Production Process

Abstract

The current work outlines application of a framework based on artificial neural networks and an integrated optimization module to adjustment of process parameters in steel production. The framework was originally developed for adjustment of parameters of material production processes in order to obtain the desired outcomes, and was primarily intended for use in the production of carbon nanomaterials in arc discharge reactors. Further development lead to more generalized procedures, applicable to a broad spectra of material production and processing. An example of optimizing the process parameters in continuous casting of steel on basis of expert knowledge and by the developed system is presented. Further steps are made towards modeling of the whole process chain in the steel plant, rather than just the casting process. Such models are in the development stage, and some preliminary results are shown where the model is used for performing some parametric studies.

Analysis and Optimization of Sand Casting Defects With the Help of Artificial Neural Network

“…Then they have been tested using the penetrant test (PT) and the ultrasonic test (UT) to describe the surface and subsurface defects respectively. It was found that when the pouring rate increases surface defects are significantly increases as the penetrant testing results showed Igor Gresovnik (2012) et al [13] had applied the Artificial Neural Network (ANN) to improve steel production process. They tried to optimize the steel production by optimizing the process parameters of different processes of steel production such as continuous casting.…”

Modeling of steelmaking process with effective machine learning techniques

“…However, there are some literature on ANN modeling of a steelmaking process to predict output parameters, like temperature of the liquid metal and the volume of necessary oxygen blow (Falkus, Pietrzkiewicz, Pietrzyk, & Kusiak, 2003), metallurgical length, shell thickness at the end of the mould and billet surface temperature (Gresovnik, Kodelja, Vertnik, & Sarler, 2012), percentage of phosphorus in the final composition of steel (Monteiro & Sant'Anna, 2012;Shukla & Deo, 2007). A comprehensive description of modern steelmaking processes along with physical and mathematical modeling and solution methodologies based on AI-based techniques, especially ANN, GA, CFD, and FLUENT software is provided by Mazumdar and Evans (2009).…”

Data Driven Performance Prediction in Steel Making

“…There are some works on the use of neural networks to predict output parameters, such as the temperature of the liquid metal and the volume of oxygen blowing [4], metallurgical length in continuous casting (CC) where the steel solidifies, shell thickness at the end of the mold and the billet surface temperature [5], percentage of phosphorus in the final composition of the steel [6,7]. Mazumdar and Evans [8] provide a complete description of modern steelmaking processes together with physical and mathematical models and solution methodologies based on artificial intelligence.…”