Closed Blast Furnace Smelting Process Furnace Conditions Intelligent Monitoring And Forecasting System

Imperial Smelting Furnace (ISF) smelting process is a quite complex process. The optimizing control model and software are not fully developed at present, which make it hard to give scientific direction to the fieidwork. The ISF conditions monitoring and predicting system is designed for this target, which is significant for the optimization of ISF production by applying artificial intelligence, besides a integrative software of optimizing and modeling possessing self-copyright is expected.In this thesis Intelligent Monitoring and Predicting System of ISF conditions in Shaoguan Smelt is designed. Firstly the key factors that influence the ISF conditions are gained based on the analysis of mechanism of ISF, then the solution to problems in ISF smelting process is proposed. Intelligent monitoring and predicting system of ISF conditions is divided into permeability module, agglomerate Softening-Point module and charge level module, which concerns some models, including permeability prediction model based on the theory of Back Propagation Neural Network, intelligent integrated prediction model of agglomerate softening-point based on the theories of Linear Regression and neural network, charge level model. At last the system is expatiated in its structure and function, which is implemented by software and hardware design, here the thought and the methods of system-design are emphasized. The system software, developed by VC, comprises communication module, main monitoring module, permeability module, agglomerate Softening-Point module and charge level module. Functions of parameters monitoring, agglomerate softening-point predicting and charge level simulating are realized in debugging and running, the precision and reliability have been proved.