The Construction Of Evaluation System Of Iron Ore In Iron-making Process

There are many Iron and Steel companies in China which depend on the iron ores imported greatly. In some companies, the iron ore imported from abroad takes up even more than 80%. The price of the iron ores imported increased quickly because of the huge amount of demand in China, especially after 2004. The price increased from less than 30 to about 150 dollar per ton, meaning about 5 times as that 5 years ago. The Chinese association for steel negotiated about the price of iron each year with the iron ore providers from abroad since 1981. The negotiation is very hard after 2004. In every negotiation, the Chinese iron and steel companies are in the dock, because they need a huge of iron ores and cannot shut down due to the society pressure. Always, they cannot help but accept the high price. The high price of the iron ore made the iron ore incorporations use some low grade ores from domestic and some waste materials containing Fe to cut down the cost of raw materials. The chemical composition always vary a lot in the industry production. Therefore, the control of the process during granulation, sintering, and the blast furnace become difficult. Under this conditions, the system which can evaluate the iron ores in the whole process is very necessary for the iron and steel company in China.In present study, the iron making process is divided into granulation, sintering, and blast furnace process, and the behavior of the iron ore in each of the procedure were studied through the theoretical and experimental methods.The conception of moisture capacity, equipments, and measurement method was suggested for the optimization in the granulation process. The measurements indicated that the moisture capacity increases with the decrease of particle size. The mathematical models were developed for the prediction of the moisture capacity. The no-pore model give a good explanation of the phenomenon that the moisture capacity increase with decreasing the particle size. The pore model consider the effect of the pore size on the ability of water absorption, and calculate the difference between the open pore and closed pore. The calculations indicated that the closed pore has little effect on the moisture capacity. The equation was got by fitting the measurement that the moisture capacity is expressed by surface area per unit mass, pore volume, bulk density and the real density. The macro and micro kinetic models were developed based on the water absorption curves. The macro kinetic model indicated that the water absorption into the iron ore particles agree with the first order Lagergren kinetic equation, and got the mass transfer coefficients of the water in the iron ores. The micro kinetic model was based on the force analysis of the water in the particles. The calculations indicated that the size of space between the particles and the closed pore volume in the particle surface are the main factors for the kinetic. The granulation experiments with the rotating cylinder indicated that the optimal water content for the granulation increases with increasing the moisture capacity, and they obey a good linear relationship. For the system used in this study, the equation is y = 6 .94+0.12x.The artificial neural network was used to build the prediction model of the granulation results. The model used three layer BP structure. The number of nodes in the three layer, activation function, training function, training times were optimized. Finally built the multi-input and one output model with moisture capacity and water content added. The tendency predicted agrees well with the measurements, and the prediction accuracy is accepted. The models can be used to improve the industrial production.On the physical and chemical behaviors of the ore in the sintering, the phase transformation, mass of liquid, thermal effect with the temperature were calculated with FACTSage, and the results were validated by various experimental data. It was indicated that the calculation by FACTSage agreed well with the measurements. This calculation can be used to optimize the sintering parameters under various raw materials conditions. The thermal effect calculated have the same scale, but had a great deviation with the measurements. The probable reason is lack of right characterization of the chemical composition.As for the influence of burden on the sintering, the orthogonal method was used to check the effect of various factors and levels. The results showed that the coal dosage the first factor influencing the sinter properties and technical index of the sintering. The sintering velocity, utilization coefficient, strength of the sinter were all improved with increasing the coal dosage. The increase of basicity will lead the improvement. The prediction model of the sintering was built with the artificial neural network and the measurements. The parameters in the model were all optimized. The validations showed that the hit rate can reach >75% for the reduction ratio and utilization coefficient in the error limit, and that of tumbler index, sintering velocity reach 87.5%. The tendency predicted agree well with the measurements, and the models can be used in industry.The optimization model for the burden based on the physical and chemistry behavior in the sintering process was developed. However, the model is complex and huge, therefore, the linear program can not solve the model. The comparison among the many methods showed that the genetic algorithm can satisfied the demand of solution. On the importance of the restrictions, the punishments function in the algorithm can deal with this importance by adjust the punishments degree. This method can get the solution to agree with the operator's goal, realizing the intelligent burden.The mineralogy recognition and analysis were also studied. The reflective power model, mineralogy qualification model, and the texture features extraction mode were all developed based on the digital image processing techniques. The reflective power model is simple but reasonable, and the results are accuracy. The Gauss model of the gray distribution for the mineralogy agree with the feature of sinter. The distribution parameters were calculated by the model. The intelligent qualification of mineralogy was realized by combing the Gauss model and genetic algorithm. The texture feature extractive method based on the gray level co-existence matrix can get the features of various mineralogy exactly. The machine recognition of the mineralogy can be realized with canberra space distance. Finally the intelligent software was developed with the models above.In summary, the behaviors of the iron ore in the iron making were qualified in the sub-section, and finally developed the evaluation system. The software was developed with C# program language and SQL database. The software has been used in the plant, and got a good application.