Modelling of electrical energy consumption in an electric arc furnace using artificial neural networks
Само за регистроване кориснике
2016
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
The objective of this research was to use state-of-the-art artificial neural network approach to estimate the extent and effect of fluctuations in the chemical composition of stainless steel at tapping of an electric arc furnace, and thus scrap and alloy weights in the charge material mix, on the specific electrical energy consumption. Such an estimation would help to further evaluate process control strategies and optimize overall operation of the electric arc furnace. The multilayer perceptron architecture 5-5-1 with hyperbolic tangent function in the hidden layer and linear function in the output layer was used as an optimal neural network model. The model was built, tested and validated based on experimental melts of the electric arc furnace at a melt shop in Italy. The proposed model was presented as an adequate one based on the coefficient of determination (R-2) which was above 0.9 as well as other error parameters calculated. The highest effect on the electrical energy consumpti...on has carbon content. (C) 2015 Elsevier Ltd. All rights reserved.
Кључне речи:
Multilayer perceptron / Modeling / Electrical energy consumption / Scrap optimization / Electric arc furnaceИзвор:
Energy, 2016, 108, 132-139Напомена:
- 7th International Conference on Sustainable Energy and Environmental Protection (SEEP), Nov 23-25, 2014, Dubai, U Arab Emirates
DOI: 10.1016/j.energy.2015.07.068
ISSN: 0360-5442; 1873-6785
WoS: 000382414200014
Scopus: 2-s2.0-84938631478
Колекције
Институција/група
VinčaTY - JOUR AU - Gajic, Dragoljub AU - Savić-Gajić, Ivana AU - Savić Ivan AU - Georgieva, Olga AU - Di Gennaro, Stefano PY - 2016 UR - https://vinar.vin.bg.ac.rs/handle/123456789/7112 AB - The objective of this research was to use state-of-the-art artificial neural network approach to estimate the extent and effect of fluctuations in the chemical composition of stainless steel at tapping of an electric arc furnace, and thus scrap and alloy weights in the charge material mix, on the specific electrical energy consumption. Such an estimation would help to further evaluate process control strategies and optimize overall operation of the electric arc furnace. The multilayer perceptron architecture 5-5-1 with hyperbolic tangent function in the hidden layer and linear function in the output layer was used as an optimal neural network model. The model was built, tested and validated based on experimental melts of the electric arc furnace at a melt shop in Italy. The proposed model was presented as an adequate one based on the coefficient of determination (R-2) which was above 0.9 as well as other error parameters calculated. The highest effect on the electrical energy consumption has carbon content. (C) 2015 Elsevier Ltd. All rights reserved. T2 - Energy T1 - Modelling of electrical energy consumption in an electric arc furnace using artificial neural networks VL - 108 SP - 132 EP - 139 DO - 10.1016/j.energy.2015.07.068 ER -
@article{ author = "Gajic, Dragoljub and Savić-Gajić, Ivana and Savić Ivan and Georgieva, Olga and Di Gennaro, Stefano", year = "2016", abstract = "The objective of this research was to use state-of-the-art artificial neural network approach to estimate the extent and effect of fluctuations in the chemical composition of stainless steel at tapping of an electric arc furnace, and thus scrap and alloy weights in the charge material mix, on the specific electrical energy consumption. Such an estimation would help to further evaluate process control strategies and optimize overall operation of the electric arc furnace. The multilayer perceptron architecture 5-5-1 with hyperbolic tangent function in the hidden layer and linear function in the output layer was used as an optimal neural network model. The model was built, tested and validated based on experimental melts of the electric arc furnace at a melt shop in Italy. The proposed model was presented as an adequate one based on the coefficient of determination (R-2) which was above 0.9 as well as other error parameters calculated. The highest effect on the electrical energy consumption has carbon content. (C) 2015 Elsevier Ltd. All rights reserved.", journal = "Energy", title = "Modelling of electrical energy consumption in an electric arc furnace using artificial neural networks", volume = "108", pages = "132-139", doi = "10.1016/j.energy.2015.07.068" }
Gajic, D., Savić-Gajić, I., Savić Ivan, Georgieva, O.,& Di Gennaro, S.. (2016). Modelling of electrical energy consumption in an electric arc furnace using artificial neural networks. in Energy, 108, 132-139. https://doi.org/10.1016/j.energy.2015.07.068
Gajic D, Savić-Gajić I, Savić Ivan, Georgieva O, Di Gennaro S. Modelling of electrical energy consumption in an electric arc furnace using artificial neural networks. in Energy. 2016;108:132-139. doi:10.1016/j.energy.2015.07.068 .
Gajic, Dragoljub, Savić-Gajić, Ivana, Savić Ivan, Georgieva, Olga, Di Gennaro, Stefano, "Modelling of electrical energy consumption in an electric arc furnace using artificial neural networks" in Energy, 108 (2016):132-139, https://doi.org/10.1016/j.energy.2015.07.068 . .