Unit Of Time-based Zinc Production Forecast Closed Blast Furnace Conditions,

Imperial Smelting Furnace (ISF) smelting process is a very complex system with nonlinearity, time variety, coupling and uncertainty and very difficult to describe with conventional mathematics functions. Mainten stability of the Furnace status is basic quarantee of improving production and quality of the Zinc.The ISF status evaluation research based on Zn-output by applying existing evaluation theory into ISFsmelting process is important for the following smelting optimized controlling, Which can improve the productivity, reduce pollution and make plants more competitive.Unit time Zinc-output is confined the key indicators to reflect the status of the ISF. In the thesis, an evaluation approach of ISF status based on unit time Zinc-output forecasting model is used. Original samples are normalized, noise samples are filtrated, and the mechanism analysis and rough set theories are applied to the historical sample data to select variables used for modeling. BP Artificial Neural Network forecasting model is formed to predict the unit time Zinc output. In rough set theories, a new method for discretization of continuous attributes based on Gaussian mixture model is presented. Another method-the SPSS statistical software, is also used, which improved the discretization mothed is efficient. Applying with extension theory, an estimation model based on extension theory of ISF status is built. the results indicated that the extension method is feasible.Based on the theory research, a system of ISF smelting process status evaluation was developed. The software was developed with VC++, realized functions of parameter monitoring, ISF status prediction, data query and help. Debugged and tested in the plant, the reliability of fault diagnosis accuracy of the software have been proved.