Name: Flávio da Silva Vitorino Gomes
Type: PhD thesis
Publication date: 20/05/2016
Advisor:

Namesort descending Role
Jose Leandro Félix Salles Advisor *

Examining board:

Namesort descending Role
ALEXANDRE LOUREIROS RODRIGUES External Examiner *
Cláudio Garcia External Examiner *
Jose Leandro Félix Salles Advisor *
Jussara Farias Fardin Internal Examiner *
Patrick Marques Ciarelli Internal Examiner *

Summary: The operation of material extraction from blast furnace is carried out with a significant degree of uncertainty, among other reasons, because the measuring level of liquids cannot be measured directly. This thesis presents a system for forecasting the level of liquid in the blast furnace hearth by measuring the electromotive force generated in shell based on a model seasonal autoregressive integrated moving average (SARIMA). This work has
shown electromotive force is a non-stationary and nonlinear time series with a strong seasonal behavior that is strongly correlated with the level of liquids. Some comparisons were made with models based on artificial neural networks with time delay (TDNN) and the results indicated that the nonlinear model has better forecasting performance. This methodology consists of the strategy for analysis, identification, filtering and prediction of the level of liquids through TDNN models achieving at the end of the process a prediction with satisfactory accuracy. The forecast level of liquids with horizon up to 1 hour ahead can help operators and engineers during the control and process optimization of the
production of blast furnaces increasing safety and financial gains.

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