A novel committee machine to predict the quantity of impurities in hot metal produced in blast furnace

In recent years, interest in artificial intelligence and the integration of Industry 4.0 technologies to improve and monitor steel production conditions has increased. Every day, several models are proposed to simulate industrial processes. In this sense, committee machines are presented as competitive alternatives to solve this task. A committee machine uses a multiple classification system in which the responses from multiple ANNs are combined into a single response. In this context, the purpose of this work was to develop committee machines using 108 independent artificial neural networks to predict the amount of impurities (silicon, phosphorus, and sulfur) in the production of cast iron in a blast furnace. It is concluded that neural networks operating in committee mode may be used in practice as a prediction and control tool due to the low RMSE values and high mathematical correlation between the database values and the values calculated by the committee machine.