NDLI: The research of end-point prediction based on combination model of electric arc furnace

Content Provider	IEEE Xplore Digital Library
Author	Shaode Zhang Xuefei Mao Xueqin Mao
Copyright Year	2010
Description	Author affiliation: Department of Electrical Engineering & Information, Anhui University of Technology, Maanshan, China (Shaode Zhang; Xuefei Mao; Xueqin Mao)
Abstract	Because the end-point parameters of an electric arc furnace (EAF) are affected by both quantitative factors and non-quantitative factors, we combine Nonlinear Gray Bernoulli-Markov model with Support Vector Machine (SVM) to produce a Nonlinear Gray Bernoulli-Markov-SVM prediction model for estimating the end-point parameter values of an EAF. The effects from the non-quantitative factors on the prediction values of end-point parameters are reflected by Nonlinear Gray Bernoulli-Markov model; while the effects from the quantitative inputs are reflected by the SVM. The Nonlinear Gray Bernoulli model that reflects non-quantitative factors is established firstly, and then, its prediction values are revised by the Markov chain. Because the effect from the quantitative inputs can not be reflected by Nonlinear Gray Bernoulli-Markov model, the Nonlinear Gray Bernoulli-Markov model is certainly not free from prediction errors from the quantitative inputs. These prediction errors are compensated by the SVM model. Further, combining Differential Evolution (DE) with Particle Swarm Optimization (PSO), a Differential Evolution Particle Swarm Optimization (DEPSO) is proposed. The parameter of Nonlinear Gray Bernoulli Model is optimized by the PSO algorithm, while that of SVM are optimized by the DEPSO algorithm. The final prediction values of the end-point parameters in EAF are thus obtained. Simultaneously, a rolling forecasting mechanism is introduced for improving the prediction precision.
Starting Page	1531
Ending Page	1536
File Size	282779
Page Count	6
File Format	PDF
ISBN	9781424450459
e-ISBN	9781424450466
DOI	10.1109/ICIEA.2010.5514684
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-15
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Predictive models Furnaces Support vector machines Particle swarm optimization Steel Temperature Smelting Slag Parameter estimation Phosphors Differential Evolution Particle Swarm Optimization Electric Arc Furnace Support Vector Machine Differential Evolution Particle Swarm Optimization
Content Type	Text
Resource Type	Article

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