Name: LETICIA CARVALHEIRO NAVARRO

Publication date: 07/03/2023
Advisor:

Namesort descending Role
THIAGO OLIVEIRA DOS SANTOS Advisor *

Examining board:

Namesort descending Role
ALBERTO FERREIRA DE SOUZA Internal Examiner *
THIAGO OLIVEIRA DOS SANTOS Advisor *

Summary: In the iron and steel industry, the stable operation of blast furnaces with efficient hot metal temperature
monitoring and control is a very important task in the process to generate high-quality hot metal. In general, the
operation of blast furnaces mostly relies on experienced based decisions of human operators, which use the most
recent measures of hot metal temperature and other operational variables to execute control decisions. However,
due to the large number of variables and complex interaction among them, the operation of such equipment is not an
easy task. This work proposes a prediction system as the first step of a larger and more complex control system for
improving the efficiency of iron production considering the scenario in Brazil. It compares several machine learning
models (K-Nearest Neighbors, Linear Regression, Extreme Boosting Machine, Light Gradient Boosting Machine,Random Forest, Support Vector Machine, XGBoost, and Multilayer Perceptron) in the task of hot metal temperature
prediction. A good temperature prediction system will allow to better plan the control actions ahead in order to
stabilize the furnace temperature during hot metal production. The proposed method was evaluated using real data
from an steel-producing company.

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