Research On The Model Of Dephosphorization And Oxygen Blowing In BOF

The processes of dephosphorization and oxygen blowing are two important parts of steelmaking in basic oxygen furnace. Phosphorus is a harmful element, which has many adverse effects on the performance of steel. Therefore, it is necessary to make the phosphorus content below a certain level. Oxygen blowing is the main control method in steelmaking, oxygen is blowed into the converter, then carbon, silicon, phosphorus, sulfur and other elements in the molten iron are removed by oxidation reaction, and the heat of oxidation is released, so that the bath temperature increases, at last qualified steel is got, so reasonable control of blowing oxygen is necessary. However, the steelmaking has many characteristics, such as complicated reactions, ever-changing production conditions, instability raw material, the overall regularity of production data is not strong, which brought great difficulties in controling oxygen volume blowing and phosphorus content. This paper make use of fuzzy c-means, rough sets, case-based reasoning algorithms to establish the dephosphorization model and oxygen blowing model.In the aspect of dephosphorization model, Based on the thermodynamics analysis of dephosphorization process in converter, this paper gives basic relationship of slag-metal phosphorous partition ratio, and proposes a method of multiple models by fuzzy weighted to calculate slag-metal phosphorous partition ratio. First, fuzzy c-means algorithm is used for clustering of data which is in the similar condition of the furnace, and get multiple regression phosphorous partition ratio models for each classified result. Then calculate the degrees of membership of the data to each model as fuzzy weights. Last, the fuzzy weighted method is used for the calculating results of each phosphorous partition ratio model to estimate the slag-metal phosphorous partition ratio. The simulation result of the model using the practical data from a steel plant proves the effectiveness of this dephosphorization model.In the aspect of oxygen blowing model, this paper uses case-based reasoning approach to calculate oxygen volume blowing, which mainly contains the processes of case retrieval and case adjust. In the case retrieval process, focusing on the limitation that traditional bayesian rough set model theory can only deal with the situation of two decision classes, a improved bayesian rough set model is proposed to deal with the problem of multiple decision classes. On this condition, a y dependency function is defined to evaluate the condition attributes significance to decision attributes, and it is proved that the y function is monotonic increase with condition attributes. In the end, an algorithm to compute attribute weight is proposed based on the monotonic property of y dependency function, using which to determine the weights of factors affecting oxygen blowing. On this basis, the most nearest neighbor method is used to retrieve a group of similar cases as the current furnace occasion. In the case adjust process, it trains the hierarchical mixture of experts model with this group of similar cases, then particle swarm optimization algorithm is applyed to optimize the model parameters. Finally, calculate oxygen volume blowing in current production condition. The simulation result of the model using the practical data from a steel plant proves the effectiveness of this oxygen blowing model.