Abstract
Gas utilization ratio (GUR) is an important indicator that is used to evaluate the energy consumption of blast furnaces (BFs). Currently, the existing methods cannot predict the GUR accurately. In this paper, we present a novel data-driven model for predicting the GUR. The proposed approach utilized both the TS fuzzy neural network (TS-FNN) and the particle swarm algorithm (PSO) to predict the GUR. The particle swarm algorithm (PSO) is applied to optimize the parameters of the TS-FNN in order to decrease the error caused by the inaccurate initial parameter. This paper also applied the box graph (Box-plot) method to eliminate the abnormal value of the raw data during the data preprocessing. This method can deal with the data which does not obey the normal distribution which is caused by the complex industrial environments. The prediction results demonstrate that the optimization model based on PSO and the TS-FNN approach achieves higher prediction accuracy compared with the TS-FNN model and SVM model and the proposed approach can accurately predict the GUR of the blast furnace, providing an effective way for the on-line blast furnace distribution control.
Highlights
The blast furnace (BF) ironmaking process is the high energy-consuming process [1,2,3], producing high levels of environmental pollution which is becoming an increasingly seriously problem nowadays and making it necessary to devise energy-saving and consumption-reduction methods for iron and steel production [4], especially for blast furnaces (BFs) ironmaking
In order to ensure the accuracy of the model, this paper firstly analyzes the factors that affect the Gas utilization ratio (GUR)
The results show that the model can accurately predict the GUR, laying the foundation for the steady production of the BF
Summary
The blast furnace (BF) ironmaking process is the high energy-consuming process [1,2,3], producing high levels of environmental pollution which is becoming an increasingly seriously problem nowadays and making it necessary to devise energy-saving and consumption-reduction methods for iron and steel production [4], especially for BF ironmaking. Researchers all over the world a mainly focusing on the coke ratio prediction of the blast furnace because it is an important economic indicator during the BF ironmaking process. The mechanism models are mainly based on the smelting mechanism, expert experience and some statistical methods. This leads to inaccurate scheduling, serious waste of the resources and low efficiency of the BF production. The TS-FNN and particle swarm optimization (PSO) methods are used to establish the prediction model according to the field data.
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