Application of Long Short-Term Memory Neural Networks for Electric Arc Furnace Modelling

Klimas, Maciej; Grabowski, Dariusz

doi:10.1007/978-3-030-91608-4_17

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…There are many publications in the literature related to EAF modeling [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29]. Different methods are used to describe the electrical arc in these studies.…”

Section: Figure 1 Power System Of the Eafmentioning

confidence: 99%

“…In [28], a hybrid discrete wavelet transform (DWT) and radial basis function neural network (RBFNN)-based approach has been introduced to model the dynamic voltage-current characteristics of the EAF. In [29], new models based on long short-term memory (LSTM) networks that are compatible with different operating conditions of the EAF have been developed. In [30], two improved EAF models based on the Schavemaker model, in which the time-varying behaviors of the model parameters are characterized by ARMA models, have been derived.…”

Section: Figure 1 Power System Of the Eafmentioning

confidence: 99%

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Comparison of a Dynamic Model of Electric Arc Furnace with Actual Operation Data for Voltage Flicker Analysis in Electrical Power Network

KÖROĞLU

2023

Çukurova Üniversitesi Mühendislik Fakültesi Dergisi

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Electric arc furnaces (EAFs) used in the iron and steel manufacturing industry for melting and reﬁning scrap metals are one of the most disturbing loads that exhibit unbalanced and highly nonlinear characteristics. Serious voltage fluctuations occur in the power system as a result of the rapid change in the current drawn from the grid by the EAF. Voltage fluctuations lead to a power quality problem known as flicker, which is defined as observable changes in light sources that affect the production environment, cause eye fatigue in personnel, and lower the work concentration levels. To investigate the voltage flicker problem, an accurate mathematical model describing the behavior of the EAF load is required. In this study, a dynamic EAF model that can be adjusted to different operating conditions has been developed in the time domain. The electric arc voltage has been modeled as an externally controllable voltage source. The instantaneous arc voltage has been expressed as a function of the arc length independent of the current. The arc resistance, which varies with time and is nonlinear, has also been calculated with differential equations using the instantaneous arc voltage value. To measure the short-term flicker severity index caused by the EAF in the power system, a flicker meter in compliance with the International Electrotechnical Commission (IEC) 61000-4-15 standard has been designed. The current-voltage characteristics of the EAF, its effect on the power system, and the flicker severity occurring at the point of common coupling (PCC) have been analyzed with simulation studies using the PSCAD/EMTDC software. Besides, the simulation results of the dynamic model of the EAF have been compared with the results obtained from the model based on the measured ﬁeld data.

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Section: Figure 1 Power System Of the Eafmentioning

confidence: 99%

Section: Figure 1 Power System Of the Eafmentioning

confidence: 99%

Comparison of a Dynamic Model of Electric Arc Furnace with Actual Operation Data for Voltage Flicker Analysis in Electrical Power Network

KÖROĞLU

2023

Çukurova Üniversitesi Mühendislik Fakültesi Dergisi

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“…The lining monitoring variables comprise temperature, heat fluxes, water quality, remaining thickness refractory, sidewall erosion and protective layer formation, among others [14]. However, the development of temperature lining prediction models in an EAF is still an open research field because of the reduced number of works in this area [15,16].…”

Section: Introductionmentioning

confidence: 99%

Attention-Based Deep Recurrent Neural Network to Forecast the Temperature Behavior of an Electric Arc Furnace Side-Wall

Godoy-Rojas

Leon-Medina

Rueda

et al. 2022

Sensors

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Structural health monitoring (SHM) in an electric arc furnace is performed in several ways. It depends on the kind of element or variable to monitor. For instance, the lining of these furnaces is made of refractory materials that can be worn out over time. Therefore, monitoring the temperatures on the walls and the cooling elements of the furnace is essential for correct structural monitoring. In this work, a multivariate time series temperature prediction was performed through a deep learning approach. To take advantage of data from the last 5 years while not neglecting the initial parts of the sequence in the oldest years, an attention mechanism was used to model time series forecasting using deep learning. The attention mechanism was built on the foundation of the encoder–decoder approach in neural networks. Thus, with the use of an attention mechanism, the long-term dependency of the temperature predictions in a furnace was improved. A warm-up period in the training process of the neural network was implemented. The results of the attention-based mechanism were compared with the use of recurrent neural network architectures to deal with time series data, such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The results of the Average Root Mean Square Error (ARMSE) obtained with the attention-based mechanism were the lowest. Finally, a variable importance study was performed to identify the best variables to train the model.

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Application of Artificial Neural Networks in Electric Arc Furnace Modeling

Klimas,

Grabowski

2023

Lecture Notes in Computer Science

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Application of Long Short-Term Memory Neural Networks for Electric Arc Furnace Modelling

Cited by 4 publications

References 14 publications

Comparison of a Dynamic Model of Electric Arc Furnace with Actual Operation Data for Voltage Flicker Analysis in Electrical Power Network

Comparison of a Dynamic Model of Electric Arc Furnace with Actual Operation Data for Voltage Flicker Analysis in Electrical Power Network

Attention-Based Deep Recurrent Neural Network to Forecast the Temperature Behavior of an Electric Arc Furnace Side-Wall

Application of Artificial Neural Networks in Electric Arc Furnace Modeling

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