Deep Learning for the Prediction of Temperature Time Series in the Lining of an Electric Arc Furnace for Structural Health Monitoring at Cerro Matoso (CMSA)

Jersson X Leon-Medina,Whilmar Vargas,Bernardo Rueda,Diego Alexander Velandia Cardenas,Camilo Gutierrez-Osorio,Jaiber Camacho-Olarte,Diego Alexander Tibaduiza Burgos,Julián Esteban Salomón Torres,Felipe Restrepo-Calle,Ricardo Cesar Gomez Vargas,Daniel Alfonso Garavito Jimenez,Jorge Sofrony Esmeral,Cesar Pedraza Bonilla

doi:10.3390/ecsa-7-08246

Jersson X Leon-Medina, Whilmar Vargas + Show 11 more

Open Access

https://doi.org/10.3390/ecsa-7-08246

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Publication Date: Nov 14, 2020
Citations: 5	License type: CC BY 4.0

Affiliation: University of Córdoba, Universidad Nacional de Colombia

Abstract

Cerro Matoso SA (CMSA) is located in Montelibano, Colombia. It is one of the biggest producers of ferronickel in the world. The structural health monitoring process performed in the electric arc furnaces at CMSA is of great importance in the maintenance and control of ferronickel production. The control of thermal and dimensional conditions of the electric furnace aims to detect and prevent failures that may affect its physical integrity. A network of thermocouples distributed radially and at different heights from the furnace wall, are responsible for monitoring the temperatures in the electric furnace lining. In order to optimize the operation of the electric furnace, it is important to predict the temperature at some points. However, this can be difficult due the number of variables which it depends on. To predict the temperature behavior in the electric furnace lining, a deep learning model for time series prediction has been developed. Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and other combinations were tested. GRU characterized by its multivariate and multi output type had the lowest square error. A study of the best input variables for the model that influence the temperature behavior is also carried out. Some of the input variables are the power, current, impedance, calcine chemistry, temperature history, among others. The methodology to tune the parameters of the GRU deep learning model is described. Results show an excellent behavior for predicting the temperatures 6 h into the future with root mean square errors of 3%. This model will be integrated to a software that obtains data for a time window from the Distributed Control System (DCS) to feed the model. In addition, this software will have a graphical user interface used by the operators furnace in the control room. Results of this work will improve the process of structural control and health monitoring at CMSA.

Highlights

Electric arc furnace (EAF) is a kind of furnace that heats materials by the covered-arc smelting process
This paper presents a deep learning model to predict temperature for a electric arc furnace in Cerro Matoso SA (CMSA)
Because there were 16 temperature output variables, an average root mean square error was calculated for each deep learning model

Summary

Introduction

Electric arc furnace (EAF) is a kind of furnace that heats materials by the covered-arc smelting process. The efficiency of these furnaces depends on the control and prediction of some variables such as power, temperature of the furnace, feed delivered, calcine composition and others [1]. Analytical techniques have been used traditionally in EAF models to predict temperature and other variables [3]. These models have a low computational load to be implemented, they present problems when many input variables are included. Some of the advantages of these techniques are adaptive behaviour, multiple input and output variables, learning of hidden patterns and others [7]

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