The Study Of Multi Objective Ptimization Model Of Blast Furnace Production And Prediction Of Blast Furnace Gas

The iron-making process is the important production process in iron and steel plants, the energy consumption in this process accounts for about 70% of the total energy consumption in iron and steel plants, the blast furnace accounted for about 50% of energy consumption in iron and steel enterprise, so the energy saving and consumption reducing of the blast furnace plays a key role in iron and steel plants. The premise of saving energy and reducing consumption of blast furnace is the establishment of energy model and the corresponding two energy generation model. Based on the existing energy consumption model of blast furnace and the blast furnace gas prediction model and the purpose of establishing a reliable, easy to update, a universal model, this paper put forward the model of blast furnace energy consumption using support vector machine and blast furnace gas combination forecasting model based on wavelet analysis method, the advantage is the operation variables can be integrated optimized on the whole in blast furnace and the prediction of blast furnace gas generated has high accuracy which lay the foundation for the blast furnace iron-making process comprehensive optimization and iron and steel plants gas resource optimization scheduling system.Based on the craft process of blast furnace iron-making, firstly, this paper studies the multiple production goals in the iron-steel plants, then it is necessary to identify the influential operating variables and status variables by analyze the iron-making process. Secondly, this paper confirm the key influential operating variables by computing the relatives between the objective variables and operating variables using gray relationship analysis(GRA). Lastly, the production goals’ prediction model based on support vector machine is built, then the method of multiple goals integrated optimized models is studied, the optimized values of the operating variables of the optimized model can be achieved through the multiple optimized algorithms, then it is easier to conduct the production process in iron-making. For the blast furnace gas in iron-making, the paper build the prediction model by combined methods based on the wavelet analysis, the prediction models of trend sequence and fluctuate sequence of blast furnace gas are built by the least square support vector machine and time sequence analysis algorithm. The results show that the energy consumption model and the blast furnace gas prediction model has high accuracy which are basis for the production multiple goals integrated optimized in blast furnace iron-making and the gas system optimized scheduling in iron and steel plants.