Intelligent Control Algorithm of Electric-Fused Magnesia Furnace Based on Neural Network

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 238)


In order to solve the problems in electric-fused magnesia production such as large overshoot, electrode lift frequency and arc current instability that are caused by two-point control algorithm and three-phase electrode control, respectively, this chapter designs an integrated three-phase control model of electric-fused magnesia furnace which is based on improved BP neural network which trains the neural network control model by using the sample data got from production floor and establishes the neural network controller model through the weights of training results. Through research and simulation analysis, feasibility and availability of the control method are proved. This control model can solve the problem of large overshoot in traditional manufacturing method, and then reduce production energy consumption and improve the quality of products.


Input Multiple Output Neural Network Control Neural Network Controller Local Optimal Problem Adjustment Output 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was financially supported by Project Fund of Binzhou University (BZXYG1016).


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Flying Aviation Information Technology R&D CenterBinzhou UniversityBinzhouChina

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