OVERVIEW OF THE CURRENT STATE OF DETERMINING SODIUM POLLUTION SOURCES IN STEEL PRODUCTION
DOI:
https://doi.org/10.32339/0135-5910-2026-6-60-67Keywords:
sodium, steel production, sodium sources, pollution sources, control methodsAbstract
This article provides an expanded overview of modern approaches to the identification of sources of sodium contamination in steelmaking, as well as analyzes in detail the current methods of its analytical control and the difficulties that arise. In modern metallurgy, impurity control is a critical factor in ensuring the quality of finished products. Sodium is one of the elements whose presence in steels requires special attention. Depending on the context, sodium can act as a useful alloying agent, or as an undesirable impurity that degrades metal properties or affects technological processes. For manufacturers of special steels and alloys, where the composition is strictly regulated, the ingress of sodium is a defective sign. Even in small amounts, it can affect the structure of non-metallic inclusions, hardenability, or properties of the finished rolled product. Therefore, monitoring its intake from the charge, fluxes or process media is a critically important task. Identification of the source of pollution begins with a thorough audit of all material and energy flows involved in the production cycle. All ways of sodium entering steel can be divided into several main groups, presented in the article. Reliable, sensitive and, preferably, express tools are needed to effectively search for the source of contamination. The analysis of sodium in steels and raw materials has its own specifics related to matrix effects and low detectable concentrations. The current state of the problem is characterized by a transition from simple detection of the presence of an impurity to the construction of integrated control and investigation systems. The introduction of statistical process control (SPC) methods allows for the dynamic monitoring of sodium content in finished products and raw materials over time. Identification of statistically significant deviations and correlations with other melting parameters (flux flow rate, scrap lot number, lining durability) helps to narrow the search range.
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