Optimal Setting Of The Amount Of Alloying Additions For Ladle Furnace And Its Application

As one of the important equipment in external refining, ladle furnace has been widely applied in the iron and steel enterprise in China. Through ladle furnace refining, functions such as the precise regulation for the temperature and composition of the molten steel, purification of molten steel, regulation for the rhythm of production and etc. can be realized. The effective approaches for improving the steel quality, reducing the production cost, shortening the smelting cycle and saving energy is to take full advantage of the refining function of the ladle furnace and to optimize various refining operations. Accurate and quickly adjustment of the content of alloying compositions in molten steel by adding alloy materials is one of the important role of ladle furnace refining. It can ensure that the content of alloying compositions of the molten steel is qualified when the molten steel arriving at ladle turret. While, the accuracy of the setting values of the amount of alloy materials to be added are mainly determined by the prediction accuracy of alloying element yield rates. In smelting practice, the determination of this parameter still stays in the stage of human estimation with lower accuracy. Therefore, targeting at improving the estimation precision of the alloying element yield rate, and on the basis of further analyzing the smelting mechanism for molten steel alloying, the study on the modeling for element yield rate estimation during the ladle refining process has been conducted. The major research work is summarized as follows:1. In order to determine the input variables of the prediction model of alloying element yield rate, firstly, the major factors influencing the yield rate of the alloying element have been analyzed simply by starting from the basic principles of alloying. However, due to the limitations in detection means, some influencing factors cannot be obtained directly. In order to solve this problem, the corresponding relationship between influencing factors and measurable variables has been figured out according to the smelting mechanism, which will be convenient for reflecting the yield indirectly with measurable variables. Finally, a group of variables have been determined as the input variables of the yield rate prediction model. Through data simulation, the effectiveness of variables in this group has been testified from the perspective of statistics, which has laid a foundation for the establishment of yield rate prediction model.2. Fuzzy modeling method based on the rule fusion has been proposed to establish yield rate prediction model of alloying element by targeting at the characteristic that there is little data for some steel grades. In this method, the smelting experiential knowledge has been adopted to make up for the shortages in the covering scope of training data, and experiential knowledge has been introduced into fuzzy model in the form of TSK (Takagi-Sugeno-Kang) fuzzy rules. The fuzzy rule fusion method has been proposed, and in the structure identification process of modeling, the experiential knowledge has been combined to the data rule extracted from the data so as to determine the initial fuzzy rules. In the parameter identification stage, the experiential knowledge evaluation parameter has been introduced into the objective function of original gradient descent method to balance the contribution from sample data and experiential knowledge to prediction model. The testing results of data simulation and engineering project show that, in this method, the experiential knowledge and sample data can be used effectively to make the prediction results much more reliable and precise.3. In accordance with the substantial data for common steel grades, multi-model modeling method based on working condition division has been proposed to establish the yield rate prediction model for alloying element. The accurate division of the working condition is the basis of this method. Therefore, according to the characteristic that the input variables obey normal distribution approximately, data division method based on hyper-ellipsoid has been proposed, which divides the data into sparse subset and dense subset according to the density of data distribution, and then independent prediction models have been established on the two data sets respectively. There is little data distributing sparsely in the sparse subset, and as a result, clustering variable construction method based on the improved genetic algorithm has been proposed to employ this data set for modeling, in which the clustering variable simplicity is convenient for the identification of working condition. After the acquisition of simplified input variables, the working condition will be divided fuzzily with fuzzy ISODATA clustering method. Original input variables belong to the divided sparse data sets are adopted to establish the multi-model prediction model based on the classification and regression tree for the prediction of alloying element yield rates of the heat affiliated to the sparse data region. The dense data set is characterized with substantial data and concentrated distribution, to modeling in a more effectively way, the online just-in-time learning method has been adopted, and the evaluation criterion of data similarity has been improved according to the distribution characteristic of the input data, so that the referential data selected can reflect the current working condition of the current heat comprehensively and conveniently. The characteristic of this method lies in that only the data concerning the current heat working condition are used for modeling, thus the risks of introducing abnormal data have been reduced. Finally, it can be seen from the results of simulation experiments that, the multi-model modeling method based on the division of working condition has higher prediction precision than other modeling methods with single model.4. Taking the refining section in some steel mill as the specific research object, the three-layer architecture composed by execution layer, control layer and optimization layer has been adopted. Under the support of basic automation system and on the basis of the theory stated above, the operating system software for the optimal control of alloying compositions in ladle furnace refining has been designed and developed. And the estimation calculation for the yield rates of alloying elements and optimization of alloying ingredient has been achieved. When applying the software into the refining operation for three steel grades, better control effect for alloying composition and economic benefit have been achieved.