Optimization Research For The Operations Of Blast Furnace Based On The Operational Characteristic Of Auxiliary Materials

Blast Furnace (BF) Steelmaking is the most important parts of the iron producing industry,the primary task of which is to provide melted iron which meets the requirements of steelmaking.The optimized research on the operation of BF is an important prerequisite for cutting down cost,reducing energy consumption and environmental emissions.The auxiliary materials form complex characteristics about space and time during theprocess of the transformation of the main materials. Different auxiliary materials’ characteristicssuch as time, space and way etc, may lead to different conversion efficiency of the main materialresources, and affect the system resource consumption and environmental emissions. With thestability of the main material’s operational characteristics such as the kind, grade, composition,particle size, temperature and metallurgical properties of iron ore and the flexibility incontrolling the operational characteristic of auxiliary materials, the significance of studying theoptimized run of BF from the the Angle of the working characteristic of the auxiliary materials isoutstanding. Appropriate ratio and the quantity between the the main materials and the auxiliarymaterials added into BF is an important prerequisite for BF working smoothly, stably andreducing the consumption of coke. And is also an important way to reduce ironmaking cost.Therefore, after the analysis of the blast furnace process based on the operational characteristicof auxiliary materials, a ratio optimization model for burden design is established to optimizeironmaking cost, and a coke ratio optimization model is established to reduce the consumption ofcoke on certain BF.With the resources’ least cost as the target function and subjected to the conservation ofmatter and energy, a model for optimization of charging mixing ratio which on the basis of theanalysis is established, the optimized strategy shows the enterprise saves25.3Yuan and thesolvent amount is reduced slightly. On the basis of the main influence factors analysis on BFcoke rate, the coke rate forecasting model is established based on BP neural network. Use themodified BP algorithm with N-W method to initialize weights and threshold, adding momentumand changing learning rate to train the network. The forecasting results show the precision ratioof the model is about98.6158%and the generalization ability of the neural network model issatisfying. With the high-accuracy forecast of neural network model, the coke rate optimizationmodel based on BP-GA is established. The optimal values are presented by GA solving themodel. The minimum coke ratio is358kg and this result is reduced24.47kg than the averagevalue of this BF all in the certain year. The results of two models proved the optimization methodthat basis on characteristic of subsidiary material’s movement is effective and can be furtherstudied in later time.