Intelligent predictive model of ventilating capacity of imperial smelt furnace  

作　　者：唐朝晖 胡燕瑜 桂卫华 吴敏 

机构地区：[1]School of Information Science and Engineering

出　　处：《Journal of Central South University of Technology》2003年第4期364-368,共5页中南工业大学学报（英文版）

基　　金：Project ( 2 0 0 1AA4 11040)supportedbytheNationalHighTechnologyResearchandDevelopmentProgramofChina

摘　　要：In order to know the ventilating capacity of imperial smelt furnace(ISF), and increase the output of plumbum, an intelligent modeling method based on gray theory and artificial neural networks(ANN) is proposed, in which the weight values in the integrated model can be adjusted automatically. An intelligent predictive model of the ventilating capacity of the ISF is established and analyzed by the method. The simulation results and industrial applications demonstrate that the predictive model is close to the real plant, the relative predictive error is 0.72%, which is 50% less than the single model, leading to a notable increase of the output of plumbum.In order to know the ventilating capacity of imperial smelt furnace(ISF), and increase the output of plumbum, an intelligent modeling method based on gray theory and artificial neural networks(ANN) is proposed, in which the weight values in the integrated model can be adjusted automatically. An intelligent predictive model of the ventilating capacity of the ISF is established and analyzed by the method. The simulation results and industrial applications demonstrate that the predictive model is close to the real plant, the relative predictive error is 0.72%, which is 50% less than the single model, leading to a notable increase of the output of plumbum.

关 键 词：imperial SMELT FURNACE ventilating capacity INTELLIGENT PREDICTIVE model artificial NEURAL network GRAY theory adaptive fuzzy combination 

分 类 号：TP278[自动化与计算机技术—检测技术与自动化装置] TF813[自动化与计算机技术—控制科学与工程]