Temperature Field Analysis and Process Control Strategies for MgO Single Crystal Production Using Adaptive Neuro-Fuzzy Inference System

Tie Li*, Zhen Wang, Ninghui Wang
School of Electrical Engineering, Dalian University of Technology, China

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© 2011 Li et al;

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the School of Electrical Engineering, Dalian University of Technology, China; Tel: +86-411-84706489; Fax: +86-411-84706489; E-mail:


To grow high-purity and large sizes MgO single crystals with twin-electrode DC submerged arc furnace requires that the temperature distribution be well understood and the processing temperature be precisely controlled. For the complexity of the production of MgO single crystals and the difficulty to measure the temperature inside the furnace, the temperature distribution was studied by using finite element method (FEM), and the temperature control was realized by the process control strategies with adaptive neuro-fuzzy inference system (ANFIS). Experiments were carried out to verify the effectiveness of the method. The result of experiments indicated that using the adaptive neuro-fuzzy control system can improve the quality and the quantity of the MgO single crystal production.

Keywords: Finite element analysis, twin-electrode DC submerged arc furnace, temperature field, ANFIS.