Recurrence Behavior Statistics of Blast Furnace Gas Sensor Data in Industrial Internet of Things

Abstract

Blast furnace gas (BFG) produced from steel industries is generally one of the most important energy supplies in enterprises. Due to a great deal of output, fluctuation, and heterogeneity in data, it is very difficult to provide profound insights into its internal dynamic. In this article, a novel analysis framework is developed for the BFG data processing, considering the recurrence plot (RP) and the recurrence quantification analysis (RQA). The specific aim is to investigate the relationship between BFG output and its potential influencing factors. This framework can be deemed as a uniform and consistent system with functional components of qualitative visualization and quantitative analysis. Concretely, the BFG outputs related to five factors are separately projected to high-dimensional spaces, followed by that their internal dynamics can be embodied through a 2-D recurrence representation of states. Finally, five RQA parameters are used to quantify the influence of these factors on the BFG output. This is the first attempt revealing the relations among the BFG data from the qualitative and quantitative perspectives. The experimental results show that RP can discover the BFG output patterns of laminar state, chaos, and instability over given three states of influencing factors, and the ranked influential degree can be given by a two-stage standard deviation of all considered RQA measures. Besides, we also demonstrate that the temperature of the hot-blast stove is most relevant to the BFG output, while the influence of considered other factors directly depends on the selected series length.