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Анотація
In this paper was developed the control system of group of hot blast stoves, which operates on the basis of the packing heating control subsystem and subsystem of forecasting of modes duration in the hot blast stoves APCS of iron smelting in a blast furnace. With the use of multi-criteria optimization methods, implemented the adjustment of control system conduct, which takes into account the current production situation that has arisen in the course of the heating packing of each hot blast stove group. Developed a situation recognition algorithm and the choice of scenarios of control based on a decision tree.
Ключові слова:
hot blast stove, structural identification, parametric identification, fuzzy knowledge base, multi-criteria optimization, decision tree.
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Як цитувати
Kobysh, E., & Simkin, A. (2017). SITUATIONAL CONTROL OF HOT BLAST STOVES GROUP BASED ON DECISION TREE. Automation of Technological and Business Processes, 8(3). https://doi.org/10.15673/atbp.v8i3.564
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Розділ
МЕТОДИ ТА АЛГОРИТМИ ЕФЕКТИВНОГО УПРАВЛІННЯ ОБ‘ЭКТАМИ
Посилання
References
[1] Li Zhi, Wen Zhi, Su Fu-yong Analysis of efficient operating cycles for blast-furnace stoves // Metallurgist. – 2013. – Vol.
56. – P. 799-806.
[2] Hongwei GUO, Bingji YAN, Jianliang ZHANG, Shanshan CHEN Fuzzy control expert system of hot blast stove based
on simulation and thermal balance // Metalurgia. – 2013. – № 1. – P. 10-17.
[3] Yaowu Tang, Xiang Liu Hot blast stove temperature control system based on neural network predictive control // Applied
Mechanics and Materials. – 2015. – Vol. 709, – P. 281-284.
[4] Kobysh E.I., Simkin A.I. Subsystem of prediction the duration of heating blast period in hot blast stove based on fuzzy
knowledge base // Metall I lit’e Ukrainy. – 2016. – №6. – P. 10-17.
[5] Kobysh E.I., Simkin A.I., Kravchenko V.P. Algoritm upravlenija nagrevom nasadki domennogo vozduhonagrevatelja s
vnutrennej kameroj gorenija // Stal’. – 2014. – № 1. – P. 9-13.
[6] Kobysh E.I., Simkin A.I. Control model of the heating hot blast stove regenerative chamber based on fuzzy knowledge
with training set // Metallurgical and Mining Industry. – 2015. – No. 6. – P. 96-101.
[7] Kobysh O.I., Simkin O.I., Koifman O.O. Computer model of blast stoves // Reporter of the Priazovskyi state technical
university. Collections of scientific works – Mariupol, 2012. – Issue. 25. – P. 239-245.
[8] Hajkin S. Neural networks. A comprehensive foundation. Moscow, – 2006, – 1104 p.
[1] Li Zhi, Wen Zhi, Su Fu-yong Analysis of efficient operating cycles for blast-furnace stoves // Metallurgist. – 2013. – Vol.
56. – P. 799-806.
[2] Hongwei GUO, Bingji YAN, Jianliang ZHANG, Shanshan CHEN Fuzzy control expert system of hot blast stove based
on simulation and thermal balance // Metalurgia. – 2013. – № 1. – P. 10-17.
[3] Yaowu Tang, Xiang Liu Hot blast stove temperature control system based on neural network predictive control // Applied
Mechanics and Materials. – 2015. – Vol. 709, – P. 281-284.
[4] Kobysh E.I., Simkin A.I. Subsystem of prediction the duration of heating blast period in hot blast stove based on fuzzy
knowledge base // Metall I lit’e Ukrainy. – 2016. – №6. – P. 10-17.
[5] Kobysh E.I., Simkin A.I., Kravchenko V.P. Algoritm upravlenija nagrevom nasadki domennogo vozduhonagrevatelja s
vnutrennej kameroj gorenija // Stal’. – 2014. – № 1. – P. 9-13.
[6] Kobysh E.I., Simkin A.I. Control model of the heating hot blast stove regenerative chamber based on fuzzy knowledge
with training set // Metallurgical and Mining Industry. – 2015. – No. 6. – P. 96-101.
[7] Kobysh O.I., Simkin O.I., Koifman O.O. Computer model of blast stoves // Reporter of the Priazovskyi state technical
university. Collections of scientific works – Mariupol, 2012. – Issue. 25. – P. 239-245.
[8] Hajkin S. Neural networks. A comprehensive foundation. Moscow, – 2006, – 1104 p.