Research On Foundamental And Application Of Optimizing Proportion Of Iron Ore For Sintering

With the development of modern steel industry, iron ore resource shortage becomes more and more severe, which causes major fluctuations of domestic raw material structure, and the production is very unstable. Obtaining qualified and economical ore proportioning scheme and corresponding technological parameters under the condition of limited resources to stabilize sinter production under fluctuant raw materials condition becomes a hotspot and key point of sintering and even iron making groups.In this paper, sinter plant of Liangang which is outsourcing materials relied is the main object of research and application. Expert system of ore proportioning optimization of sintering is constructed combining computation model, BP neural network model, genetic optimization model and expert system, based on study of physical and chemical properties of raw materials, granulation properties, metallogenic properties, sintering properties and their relationships. This system was put into application and good results were achieved.Database was built up by using Access2000database technology, including basic properties database of raw materials (physical and chemical properties, granulation properties and metallogenic properties), sintering properties database (data of single ore and ore proportioning sinter pot test), sintering information database (ore proportioning schemes, technological parameters and indicators of yield and quality), knowledge base of ore proportioning, model library (model parameters and BP network model), etc., Data management, modeling and knowledge management platform was built using VC++. Integrated management of text, data and images information, as well as files adding, deleting, modifying, locating, loading and saving were achieved.Based on results of sinter pot tests, combined with production experience, sinter quality and productivity indicators of sinter were predicted using improved three-layer BP neural network model, with chemical composition, mineral composition, basicity, particle content as input parameters and sintering speed (productivity), tumbler strength and solid fuel consumption as outputs. The model accuracy was over85%. Modeling platform was provided, input parameters, output parameters, learning parameters could be modified based on forecast results, and good adaptability was achieved.Calculation model of suitable moisture and fuel proportions was built based on results of physical and chemical properties of raw materials, granulation properties, metallogenic properties and sintering properties, using linear regression analysis method. The model accuracy was over93%.Procedures of obtaining optimal ore proportioning scheme are as follow:under the given conditions of raw materials (physical and chemical properties, supply conditions) and required indicators of sinter yield and quality, ore proportioning scheme groups which meet the requirements of sinter chemical compositions are obtained by linear programming method; predict indicators of sinter yield and quality and proper technological parameters of each scheme using prediction model of sinter yield and quality and optimization model of technological parameters, evaluate the economical efficiency with economic model of ore proportion; within the ore proportioning scheme group which can meet the requirements of chemical compositions, sinter yield and quality indicators are selected as constraint function and economical efficiency as evaluation function, ore proportioning scheme is obtained with satisfying sinter yield and quality, as well as good economical efficiency using genetic algorithm, and corresponding technological parameters and indicators of sinter yield and quality are provided; the scheme can be modified based on expertise using expert system of ore proportioning modification, until the users are satisfied.