Space direction neighborhood preserving embedding-based monitoring and scheduling guidance for blast furnace gas system

Abstract

Blast furnace gas (BFG) system of steel enterprise generally accompanies with multi-dimension and nonlinear features. It's a hard assignment for energy scheduling operators to make real-time scheduling decision when monitoring such system. In this study, a novel dimensionality reduction method named Space Direction Neighborhood Preserving Embedding (SDNPE) is proposed for the BFG system monitoring and scheduling units determination. To maintain the system dynamic characteristic in the low dimension space, such method constructs a neighborhood graph that searches for nearest neighbors with respect to both the neighbors in spatial scales and fluctuation tendency of the gas flow data. Then, for the BFG system monitoring and scheduling units determination, Hotelling's T2 chart and score chart are constructed upon the SDNPE model. Experiments with real-time data of an iron enterprise in China demonstrated the effectiveness of the proposed method.