1. Soft-sensing method for slag-crust state of blast furnace based on two-dimensional decision fusion
- Author
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Min Wu, Jinhua She, Jianqi An, Takao Terano, and Jialiang Zhang
- Subjects
0209 industrial biotechnology ,Blast furnace ,Cognitive Neuroscience ,Metallurgy ,Slag ,Crust ,02 engineering and technology ,Computer Science Applications ,020901 industrial engineering & automation ,Artificial Intelligence ,Soft sensing ,visual_art ,0202 electrical engineering, electronic engineering, information engineering ,visual_art.visual_art_medium ,Environmental science ,Decision fusion ,020201 artificial intelligence & image processing - Abstract
Slag crusts on the cooling stave of a blast furnace offer greatly protection for the furnace wall. Frequent forming and shedding of slag crusts (FSSCs) cause severe erosion on the wall, which affects the life of a blast furnace. However, it is very difficult to detect the FSSCs directly because of restrictions imposed by the structure of a blast furnace and detecting costs. This paper presents a soft-sensing method based on two-dimensional decision fusion to detect the state of slag crust (SSC) of a blast furnace. First, a soft-sensing scheme for SSC is put forward on the basis of features of slag crusts and the temperature detected in cooling stave. Next, methods for calculating a temperature threshold (TT) and a change-rate threshold of temperature (CRTT) are presented according to the characteristics of slag crusts. Finally, a two-dimensional decision method is presented by fusing the TT and CRTT to determine the SSC of the blast furnace. The experiment results based on industrial data demonstrate the effectiveness of the method.
- Published
- 2018
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