Predictive Modeling of Blast Furnace Gas Utilization Rate Using Different Data Pre-Processing Methods

Abstract

The gas utilization rate (GUR) is an important indicator parameter for reflecting the energy consumption and smooth operation of a blast furnace (BF). In this study, the original data of a BF are pre-processed by two methods, i.e., box plot and 3σ criterion, and two data sets are obtained. Then, support vector regression (SVR) is used to construct a prediction model based on the two data sets, respectively. The state parameters of a BF are selected as input parameters of the model. Gas utilization after one hour (GUR-1h), two hours (GUR-2h), and three hours (GUR-3h) are selected as output parameters, respectively. The simulation result demonstrates that using the 3σ criterion to pre-process the raw data leads to better prediction of the model compared to using the box plot. Moreover, the model has the best predictive effect when the output parameter is selected as GUR-1h.

Keywords

• blast furnace
• data pre-processing
• extreme outlier
• gas utilization rate
• support vector regression