Fault identification for quality monitoring of molten iron in blast furnace ironmaking based on KPLS with improved contribution rate

Blast furnace (BF) ironmaking is one of the most important production links in modern iron–steel making. The operation conditions of BF and the molten iron quality (MIQ) should be monitored and analyzed in real-time to realize high quality with low energy consumption. Aiming at the problems of strong nonlinearity and few fault samples in BF processes, a novel fault identification method for MIQ monitoring based on kernel partial least squares (KPLS) with improved contribution rate is proposed in this paper. First of all, a KPLS model is established with the actual historical data in order to detect the quality-related faults accurately. The T² and SPE statistics are used to monitor the operation conditions of process from different aspects. Second, in view of the unclear physical meaning and complex computation of the existing fault identification methods based on KPLS with contribution rate, a scale factor vector is introduced into the new samples to calculate the T² and SPE statistics, so as to construct the monitoring indicators functions. By performing Taylor approximation on these constructed functions near the scale factor with the value of 1, two new statistics are obtained from the absolute value of the first-order partial derivative representing the contribution rate of each variable. Finally, in order to further improve the effect of fault identification, the relative contribution rate of each variable is used to identify the final faulty variables. The tests of MIQ monitoring in BF ironmaking process using actual industrial data verify the validity and practicability of the proposed method.