THE EXPERIMENTAL INVESTIGATION OF THE EFFECTIVENESS OF THE MODIFIED PROCEDURE FOR FORECASTING FRACTAL PROCESSES WITH KALMAN

Abstract

Introduction. In modern metallurgy, rapid technological advancements present challenges in improving product quality and optimizing production processes. One of the key components of this process is controlling and ensuring the necessary chemical composition of cast iron at the blast furnace stage. In this context, automated control and prediction of the chemical composition of cast iron become important tools for enhancing the efficiency of the production process for the quality of the final product of the blast furnace – cast iron. The most accurate correspondence of the chemical composition of cast iron to the specified parameters is a fundamental condition for achieving the necessary characteristics of the final production of the blast furnace. Automated control allows for rapid responses to changes in the production process and maintaining the chemical composition of the desired quality. This reduces waste, improves product quality, and reduces economic losses. Scientific novelty. As a solution to the stated tasks, the authors propose a modified Kalman filter algorithm. This method is an extension of the classical Kalman filter. In the context of the metallurgical industry, the modified Kalman filter can be applied to predict the chemical composition of cast iron based on previously obtained data on the chemical composition at the blast furnace output. The implementation of the proposed modified algorithm will optimize the raw material mixing processes, minimizing losses, and improving the quality of the final product.