Electric Arc Furnace Modeling with Artificial Neural Networks and Arc Length with Variable Voltage Gradient

Garcia-Segura, Raul; Castillo, J. Vázquez; Martell, Fernando; Longoria-Gándara, O.; Aguilar, Javier

doi:10.3390/en10091424

Cited by 28 publications

(26 citation statements)

References 13 publications

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“…The electric arc was simulated using a sinusoidal voltage source with an amplitude depending on the arc length, which is the basic harmonic of the arc voltage. Such a model was adopted in [30].…”

Section: Voltage Fluctuationsmentioning

confidence: 99%

“…Using the relationship determined for power-voltage characteristics, and replacing the arc by the voltage source formula (28) and (29) at identical devices, we can present the coefficient K N in the following form: (30) and at different devices:…”

Section: Voltage Fluctuationsmentioning

confidence: 99%

“…In the research using computer measuring techniques, the following models can be distinguished: one based on a series connected resistor and inductance [10,11], the time-varying voltage source defined as a nonlinear function dependent on the length of the electric arc [12][13][14][15], and the classic based on nonlinear differential equations using Mayra equations and Cassie [16,17]. We should also mention the recently widely used simulation programs [18][19][20][21][22][23][24][25][26], including those based on neural networks [27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

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Estimating the Impact of Arc Furnaces on the Quality of Power in Supply Systems

Łukasik

Olczykowski

2020

Energies

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Arc furnaces, due to their high unit power and load nature, belong to the receivers affecting the power quality. A dynamically changing electric arc is the main source of disturbances generated by arc devices. This current article presents the results of model tests of disturbances caused by arc furnaces. It also presents the attempts to estimate the power supply conditions for arc furnaces, so that they do not generate unacceptable disturbances to the power system. Various models of the electric arc are proposed. The values of the elements making up the furnace supply system were based on actual parameters. In these networks, measurements of electricity quality indicators were carried out, which allowed us to refer to the obtained results of model tests with the real values. Accordingly, to the real conditions, the values of the short-circuit power of the network and the power of furnace transformers were also adopted in the tests.

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Section: Voltage Fluctuationsmentioning

confidence: 99%

Section: Voltage Fluctuationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimating the Impact of Arc Furnaces on the Quality of Power in Supply Systems

Łukasik

Olczykowski

2020

Energies

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“…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]. Some EAF work in the order of Mega Volt-Amperes which means any improvement in efficiency represents energy savings [2].…”

Section: Introductionmentioning

confidence: 99%

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)

León

Gómez

Gutierrez

et al. 2020

7th International Electronic Conference on Sensors and Applications

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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.

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“…Based on ANN, some papers have made predictions for wind power [22], wind speed [23], district-level electricity demand [24] and the water-alternating-CO 2 process [25]. Alireza Taheri-Rad et al simulated the energy flows for the production of various paddy rice cultivars [26]; Nadya et al simulated the relationship between spectral profiles and hardness values [27]; Raul et al modelled the electric arc furnace [28]. Besides, Marjan et al applied ANN in the field of chemistry [29], and Morse et al in the field of composite panels [30].…”

mentioning

confidence: 99%

Assessment Framework of Provincial Carbon Emission Peak Prediction in China: An Empirical Analysis of Hebei Province

Li¹,

Du²

2019

Pol. J. Environ. Stud.

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Greenhouse gas emission reduction has become the focus of today's research. In December 2009 the Chinese government proposed targets aimed at controlling greenhouse gas emissions. More specifically, it was decided that CO 2 emissions per unit gross domestic product (GDP) in China should be cut by 40-45% in 2020 compared to 2005. Then China and the United States jointly issued the "China-U.S. Joint Presidential Statement on Climate Change" in November 2014 [1], and China claimed to peak CO 2 emissions around 2030 and would endeavor to reach the carbon emission peak as soon as possible. Besides, China also made climate change action included in the "13 th Five-Year" Development Planning [2]. Since then, whether or not the target can be accomplished has become the focus of discussion. Hebei, as a large economic and energy consumption province, is located in the Bohai Sea Economic Zone and the Beijing Tianjin Hebei Economic Circle, with rich fossil energy and oil field resources [3]. Since the

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Electric Arc Furnace Modeling with Artificial Neural Networks and Arc Length with Variable Voltage Gradient

Cited by 28 publications

References 13 publications

Estimating the Impact of Arc Furnaces on the Quality of Power in Supply Systems

Estimating the Impact of Arc Furnaces on the Quality of Power in Supply Systems

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)

Assessment Framework of Provincial Carbon Emission Peak Prediction in China: An Empirical Analysis of Hebei Province

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