Prediction of Sulfur in the Hot Metal based on Data Mining and Artificial Neural Networks

In recent years, interest in artificial intelligence and the integration of Industry 4.0 technologies to improve and monitor steel production conditions has increased. In the current scenario of the world economy, where the prices of energy and inputs used in industrial processes are increasingly volatile, strict control of all stages of the production process is of paramount importance. For the steel production process, the temperature of the metal in the liquid state is one of the most important parameters to be evaluated, since its lack of control negatively affects the final quality of the product. Every day, several models are proposed to simulate industrial processes. In this sense, data mining and the use of artificial neural networks are competitive alternatives to solve this task. In this context, the objective of this work was to perform data mining in a Big Data with more than 300,000 pieces of information, processing them using an artificial neural network and probabilistic reasoning. It is concluded that data mining and neural networks can be used in practice as a tool for predicting and controlling impurities during the production of hot metal in a blast furnace.