Modelling of crystallizer wear

• Autorzy: David J. , Švec P. , Frischer R.
• Języki publikacji: EN

Abstrakty

EN

Purpose: In this paper will be described an analysis of control problems and technical lifetime modeling of continuous casting device crystallizers. A full exploitation of continuous casting equipment (CCE) advantages can only be achieved through a control system that minimizes all undesirable effects on the technological process. Some of the undesirable effects influencing the CCE process effectiveness are the failures and service interruptions. The failures and service interruptions are caused by a number of factors, impacts and processes that effect and run directly on the equipment in its individual parts during its operation. Design/methodology/approach: This problem was solved by connection of dependability theory and artificial neural networks. Findings: A prediction of crystallizer’s desk’s wear model was created on the basis of artificial neural networks and analytics diagnostics. Research limitations/implications: The limitations are given by operational data quantity. These limitations are for learning process and model adaptability. Practical implications: These problems are solved with cooperation with regional metallurgical companies. Gained results will be applied into the operational conditions. Originality/value: Signification consists of dependability theory and artificial neural networks, when solving a prediction model of crystallizers wear.

Słowa kluczowe

EN

crystallizer; wear; modelling; lifetime; artificial neural networks

PL

krystalizator; zużycie; modelowanie; sztuczne sieci neuronowe

• Wydawca: International OCSCO World Press
• Czasopismo: Archives of Materials Science and Engineering
• Rocznik: 2012
• Tom: Vol. 58, nr 2
• Strony: 193--198
• Opis fizyczny: Bibliogr. 8 poz.

Twórcy

autor

David J.

• Department of Automation and computing in Metallurgy, Vysoká škola báňská - Technical Univerzity of Ostrava, 17. listopadu 15, Ostrava- Poruba, 708 00, Czech Republic

autor

Švec P.

• Department of Automation and computing in Metallurgy, Vysoká škola báňská - Technical Univerzity of Ostrava, 17. listopadu 15, Ostrava- Poruba, 708 00, Czech Republic

autor

Frischer R.

• Department of Automation and computing in Metallurgy, Vysoká škola báňská - Technical Univerzity of Ostrava, 17. listopadu 15, Ostrava- Poruba, 708 00, Czech Republic

Bibliografia

• [1] J. David, M. Heger, M. Vrožina, L. Válek, Visualisation of data fields, Archives of Metalurgy and Materials 55/3 (2010) 795-801.
• [2] J. David, R. Frischer, Development of new progressive tools and systems for quality improving of dependable flat products on slab device of continuous casting, Acta Metallurgica Slovaca 1 (2010) 162-166.
• [3] M. Vrožina, et al., Usage of knowledge systems in maintenance control of metallurgical devices with help of continuous diagnostics into the solution, VŠB-TU Ostrava, 2004, Final report of grant project 106/05/2596 for period 2005-2007, VŠB-TU Ostrava, 2008.
• [4] J. David, et al., Stage 2 - Identification of diagnostics quantities and development of diagnostics system, Report about stage solving in TIP program, VŠB-TU Ostrava, 2010/12.
• [5] A. Carpinteri, Critical defect size distributions in concrete structures detected by the acoustic emission technique, Meccanica 43 (2008) 349-363.
• [6] V. Volkovas, J. Dulevicius, Acoustic, emission used for detection of crack generation in propellers of turbine-pump units, Russian Journal of Nondestructive Testing 42/4 (2006) 248-254.
• [7] R. Lenort, A. Samolejová, Analysis and identification of floating capacity bottlenecks in metallurgical production, Metalurgija 46/1 (2007) 61-66.
• [8] I. Spicka, M. Heger, J. Franz, The mathematical-physical models and the neural network exploitation for time prediction of cooling down low range specimen, Archives of Metallurgy and Materials 55/3 (2010) 921-926.