Dynamic modelling of the electric arc furnace process using artificial neural networks

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

“…In this respect, the ANN relates the effect of the content of carbon, chromium, nickel, silicon, and iron (ANN inputs) with respect to the EAF's electrical energy consumption (ANN output). Finally, Baumert et al [15] describes an ANN model for predicting electrical energy consumption, which differs from our proposal. After analyzing the different state-of-the-art ANN-EAF applications (widely cited in the open literature), it is possible to conclude that many ANN structures have been used for tackling different issues in EAF modeling, which makes it difficult to carry out a direct comparison among ANN-EAF models.…”

“…The multilayer perceptron architecture 5-5-1 with a hyperbolic tangent function in the hidden layer and a linear function in the output layer was used as an optimal neural network model. Baumert et al [15] presents one of the first studies for carrying out the dynamic modeling of the electric arc furnace process using artificial neural networks.…”

“…Today, there are several studies in the open literature addressing the problem of modeling EAFs with an artificial neural network (ANN) [10][11][12][13][14][15]. In this sense, inputs of interest are passed through an ANN and transformed by using activation functions to produce outputs.…”

Predicting the performance of submerged arc furnace with varied raw material combinations using artificial neural network

“…Recently, its applications were reported in various pyro-metallurgical processes and other very relevant processes such as control of submerged arc furnace for ferroalloy production, predictive control of a refractory gold plant, optimization of gas-loop process, and control of blast furnace operation [6][7][8][9][10]. It was used for modeling electrical arc furnaces and to establish the effects of relations between critical furnace process parameters [6,8]. In the area of blast furnace operations it was applied to predict slag and metal properties [9,[11][12][13].…”

“…The artificial neural networks approach has shown its creditability in various metallurgical processes including plant control and optimization. Recently, its applications were reported in various pyro-metallurgical processes and other very relevant processes such as control of submerged arc furnace for ferroalloy production, predictive control of a refractory gold plant, optimization of gas-loop process, and control of blast furnace operation [6][7][8][9][10]. It was used for modeling electrical arc furnaces and to establish the effects of relations between critical furnace process parameters [6,8].…”