Abstract
Blast furnace (BF) is the main method of modern iron-making. Ensuring the stability of the BF conditions can effectively improve the quality and output of iron and steel. However, operations of BF depend on mainly human experience, which causes two problems: (1) human experience is not objective and is difficult to inherit and learn and (2) it is difficult to acquire knowledge that contains time information among multiple variables in BF. To address these problems, a data-driven method is proposed. In this article, we propose a novel and efficient algorithm for discovering underlying knowledge in the form of temporal association rules (TARs) in BF iron-making data. First, a new TAR mining framework is proposed for mining temporal frequent patterns. Then, a novel TAR mining algorithm is proposed for mining underlying, up-to-date, and effective knowledge in the form of TARs. Finally, considering the updating of the BF database, a rule updating method is proposed that is based on the algorithm that is proposed in this article. Our extensive experiments demonstrate the satisfactory performance of the proposed algorithm in discovering TARs in comparison with the state-of-the-art algorithms. Experiments on BF iron-making data have demonstrated the superior performance and practicability of the proposed method.
Highlights
Iron and steel are two of the most important raw materials in modern society. eir quality and output are important indices for measuring a country’s economic strength and play an incomparable role in a country’s development. e iron-making process is the upstream process of the iron and steel industry; it is important for the output and quality of the whole iron and steel production process
To maintain temporal association rules (TARs) that were mined from the blast furnace (BF) database, we propose a method for rapidly updating TARs in dynamically updated temporal databases
BF is a typical process industry, namely, its production data are sequential, which satisfies the mining conditions of the algorithm that is proposed in this article. e proposed method will be applied to mine the TSAR from the authentic blast furnace data of a steel plant in China. e data are discrete time series with a sampling time of 30 min
Summary
Iron and steel are two of the most important raw materials in modern society. eir quality and output are important indices for measuring a country’s economic strength and play an incomparable role in a country’s development. e iron-making process is the upstream process of the iron and steel industry; it is important for the output and quality of the whole iron and steel production process. BF is a typical black-box system, and its smelting process has the following characteristics: multivariable coupling, large time delay, and nonlinearity. It emerged as a method for identifying patterns and trends from big data [3, 4] It includes many algorithms, such as clustering, classification, association rule mining, and regression. The above methods can find out relevant knowledge of stabilizing furnace conditions, the knowledge obtained by the above methods has limitations in application because of the characteristics of multivariable coupling and large time delay of BF. E TAR algorithm can find the temporal relations among the multivariables well, and the rules can play a better role in stabilizing the furnace state.
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