Temperature Prediction in Electric Arc Furnace with Neural Network Tree

Kordos, Mirosław; Blachnik, Marcin; Wieczorek, Tadeusz

doi:10.1007/978-3-642-21738-8_10

Cited by 12 publications

(3 citation statements)

References 9 publications

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“…Artificial neural network (ANN) is a widely used regression machine learning method, which has been used as an appropriate method to solve various challenges related to EAF operation, such as temperature prediction and charge simulation [23][24][25][26][27]. Neural network model is used to predict the carbon content at the end point of EAF smelting based on different process variables.…”

Section: Artificial Neural Network (Ann) Methodsmentioning

confidence: 99%

Influence Factor Analysis and Prediction Model of End-Point Carbon Content Based on Artificial Neural Network in Electric Arc Furnace Steelmaking Process

Yang

Guo

et al. 2022

Coatings

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In this manuscript, we consider the accuracy of end-point carbon content prediction affected by oxygen injection in multiple stages of electric arc furnace (EAF) melting process. Such a prediction would help to further evaluate process control strategies and optimize overall operation of the electric arc furnace. Principal component analysis (PCA) was used to normalize the 13 input variables affecting the endpoint carbon content. log-sigmoid and tan-sigmoid functions were used to verify the same sample, and it was found that the Mean squared error(MSE) of the model under logsig + logsig function was smaller, indicating that the model was more stable. At the same time, different hidden layer nodes were tried, and finally the structure of the model was determined as 13 × 10 × 8 × 1, and the activation function was logsig + logsig. Using historical smelting data to train and test the neural network model, the correlation coefficient (R) of the verified model is 0.7632, the model prediction is in the range of ±0.03%, the hit rate of the model is 64.5%, and the hit rate of the model is 42% in the range of ±0.02%. Combining the verification basis of the model with the metallurgical reaction principle of the EAF steelmaking process, a pretreatment method of phased input of total oxygen is proposed. The oxygen is divided into three stages, which are the oxygen consumption volume of 0–5 min, 5–30 min and more than 30 min and other variables are kept unchanged. The same neural network is used to train and verify the same data. After verification, the R of the oxygen staged model is 0.8274. The model prediction is in the range of ±0.03%, the hit rate of the model is 78.5%, and the hit rate of the model is 58% in the range of ±0.02%. Finally, an on-line carbon content prediction system based on artificial neural network model is developed and applied to actual production. Running results illustrated that the hit rate of end-point carbon content is 96.67%, 93.33% and 86.67%, respectively when the prediction errors are within ±0.05%, ±0.03% and ±0.01%, the improved neural network model can effectively predict the end-point carbon content, which provides a good basis for the carbon content at the end point of EAF steelmaking process.

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Section: Artificial Neural Network (Ann) Methodsmentioning

confidence: 99%

Influence Factor Analysis and Prediction Model of End-Point Carbon Content Based on Artificial Neural Network in Electric Arc Furnace Steelmaking Process

Yang

Guo

et al. 2022

Coatings

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“…Gajic et al [4], for example, have developed the energy consumption model of an electric arc furnace (EAF) based on the feedforward ANNs. Temperature prediction models [5,6] for EAF were established using the neural networks. Rajesh et al [7] employed feedforward neural networks to predict the intermediate stopping temperature and end blow oxygen in the LD converter steel making process.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Methodology for Rule Extraction from ELM-Based Vacuum Tank Degasser Multiclassifier for Decision-Making

Wang

Zhang

et al. 2019

Energies

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The present work proposes an integrated methodology for rule extraction in a vacuum tank degasser (VTD) for decision-making purposes. An extreme learning machine (ELM) algorithm is established for a three-class classification problem according to an end temperature of liquid steel that is higher than its operating restriction, within the operation restriction and lower than the operating restriction. Based on these black-box model results, an integrated three-step approach for rule extraction is constructed to interpret the understandability of the proposed ELM classifier. First, the irrelevant attributes are pruned without decreasing the classification accuracy. Second, fuzzy rules are generated in the form of discrete input attributes and the target classification. Last but not the least, the rules are refined by generating rules with continuous attributes. The novelty of the proposed rule extraction approach lies in the generation of rules using the discrete and continuous attributes at different stages. The proposed method is analyzed and validated on actual production data derived from a No.2 steelmaking workshop in Baosteel. The experimental results revealed that the extracted rules are effective for the VTD system in classifying the end temperature of liquid steel into high, normal, and low ranges. In addition, much fewer input attributes are needed to implement the rules for the manufacturing process of VTD. The extracted rules serve explicit instructions for decision-making for the VTD operators.

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“…ANNs have been already proven as a suitable approach to various challenges related to the EAF operation such as temperature prediction [14,15] as well as simulation of its power load [16,17]. Olabi et al [18] employed the backpropagation ANN and the Taguchi approach to design and to find out the optimum levels of the welding speed, the laser power and the focal position for CO 2 keyhole laser welding of medium carbon steel butt weld.…”

Section: Introductionmentioning

confidence: 99%

Modelling of electrical energy consumption in an electric arc furnace using artificial neural networks

Gajić

Georgieva

et al. 2016

Energy

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Temperature Prediction in Electric Arc Furnace with Neural Network Tree

Cited by 12 publications

References 9 publications

Influence Factor Analysis and Prediction Model of End-Point Carbon Content Based on Artificial Neural Network in Electric Arc Furnace Steelmaking Process

Influence Factor Analysis and Prediction Model of End-Point Carbon Content Based on Artificial Neural Network in Electric Arc Furnace Steelmaking Process

An Integrated Methodology for Rule Extraction from ELM-Based Vacuum Tank Degasser Multiclassifier for Decision-Making

Modelling of electrical energy consumption in an electric arc furnace using artificial neural networks

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