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Study on Heat State Forecasting Model of Blast Furnace Based on Particle Swarm Optimization Improved BP Neural Network

Author: XiongXin
Tutor: CaoChangXiu
School: Chongqing University
Course: Control Theory and Control Engineering
Keywords: System of furnaces cylinder heat Hot Metal Ti content BP neural network Particle Swarm Optimization Thermal state forecast
CLC: TF53
Type: Master's thesis
Year: 2008
Downloads: 276
Quote: 2
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Abstract


The steel industry is the pillar industry of the national economy, blast furnace iron making is an important part of the iron and steel industry. The blast furnace furnace blast anterograde guarantee is also an important indicator to judge the blast furnace conditions. How to create a state forecast model guidance blast furnace ironmaking furnace temperature control of the furnace heat not only has important theoretical value, but also has the important value of production practices. But from the point of view of cybernetics, the blast furnace process is a great delay nonlinear systems, have great difficulty in modeling. In particular the blast furnace furnace temperature expression, is more difficult. In this paper, the actual situation of a large-scale iron and steel enterprises 4 # blast proposed changes to determine furnace temperature to the chemical heat - Hot Metal Ti content instead of physical heat. On this basis, the static model has been developed to analyze 4 # blast furnace smelting process of law, the quantitative relationship; research by analyzing the difference of the main factors to affect the thermal regime of the hearth and the ordinary ore smelting heat system with the vanadium titanium heat system # 4 blast furnace hearth heat system features. In this paper, the premature convergence of particle swarm optimization (PSO), based adaptive learning factor adjustment ideological improved PSO algorithm and algorithm performance tests showed that: improved PSO algorithm converges fast, with convergence probability and the search accuracy; then improved PSO algorithm to train the BP neural network, in order to improve the effect of network training. According to the characteristics of the network model, it is necessary to control the number of input parameters to select parameters should try to choose a larger impact parameters of the output; This article is followed by the correlation analysis between process parameters and process parameters The correlation between the Ti content in hot metal analysis, the furnace temperature change is more sensitive parameters and process parameters affect the lag time the furnace temperature. On this basis, the selection of the input parameters of the network model. Finally, establish a dynamic forecasting model based on the state of the blast furnace heat of the PSO-BP neural network; # 4 blast furnace in the generation process data preprocessing, model simulation experimental combination treatment sample data, experimental results show that with improved BP-PSO network Hot Metal Ti content of forecast accuracy is higher than the standard BP network, this accuracy can meet the needs of actual production, and improvement of the BP-PSO network training time is significantly shorter than the standard BP network.

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CLC: > Industrial Technology > Metallurgical Industry > Ironmaking > Blast furnace melting milling process
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