Development and study of a method for controlling anomalous behavior in the blast furnace process
DOI:
https://doi.org/10.15588/1607-6761-2024-1-5Keywords:
Time series, 3-sigma method, Kalman filter, blast furnace, forecasting; analysisAbstract
Purpose. To develop and investigate a method for controlling the blast furnace process in the absence of an analytical description of its behavior, which allows for the detection of anomalies in the production process.
Methodology. This study utilizes methods for evaluating and forecasting time series based on the Kalman filter algorithm, fractal analysis, and nonlinear dynamics.
Findings. A method has been developed that allows for the evaluation and forecasting of non-stationary stochastic processes with an unknown analytical model. This method includes an embedded anomaly detection procedure based on the 3-sigma method. Research was conducted on real-time series data of the chemical composition of cast iron at the blast furnace output. It has been demonstrated that the developed method effectively detects anomalies in the process behavior. A possible implementation of a control system using the proposed method has been considered.
Originality. For the first time, a modification of the suboptimal Kalman-type filter-forecaster has been developed, invariant to the properties of the processed process. This modification improves the reliability and accuracy of forecasting non-stationary processes when it is impossible to obtain their analytical model and detects production process anomalies using an embedded procedure based on the 3-sigma method. For the first time, a study of time series data on the chemical composition of cast iron at the blast furnace output using the 3-sigma method has been conducted, which allowed for the identification of zones with anomalous values and outliers, indicating significant deviations in the production process behavior.
Practical value. This research expands existing quality control methods in the metallurgical industry and demonstrates the effectiveness of the statistical 3-sigma method for monitoring and analyzing time series in real production conditions. The obtained results can be used to develop more accurate quality control systems and take prompt corrective actions.
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