Electricity-based steelmaking with electric arc furnaces (EAFs) has been increasing during the past decades, currently amounting to around a third of the global steel production. [1] Reasons for this, to name a few, are lower carbon dioxide emissions and better energy efficiency compared with the traditional ore-based steelmaking. [1,2] Furthermore, favoring the recycled metal as the raw material over the ore-based steelmaking has a key role in sustainable resource and energy use. [3] Due to these reasons, electricity-based steelmaking can be expected to increase even more in the future. Recycled metal is one of the main raw materials for an EAF. One of the downsides of the recycled metal is its highly varying composition and particle size. Thus, the composition of the slag that accumulates on top of the molten steel is unique for every batch. The slag is quantitatively a major byproduct in the steelmaking but the varying composition causes problems for the final slag product. [4] A common way to determine the slag composition is to make an offline X-ray fluorescence (XRF) analysis, which requires careful sample preparation [5] causing a time delay between the slag sampling and obtaining the XRF results. [6] Therefore, the results of the XRF slag composition analysis have limited use during the melting process in practice. In contrast, online evaluation of the slag composition would allow the furnace operator to decide how much and which additive materials, such as lime or ferrosilicon, should be added before the end of the melt. Also, the timing of these additions could be adjusted to optimal instances. By getting the information of the slag composition in advance, the operator would be able to plan the use of additive materials from the resource use and efficiency point of view. Due to the demand for sophisticated modeling of the EAF processes and experimental validation of the methods, the fundamental EAF research has increased over the years. Especially, the online measurements and modeling have gained a lot of attention from the steel industry because online data analysis would contribute to more efficient resource use, real-time modification of the steel composition, and anticipation of abnormal and even hazardous phenomena in the furnace. From the melting point of view, Logar et al. [7] developed a computational model that can be used online due to low computational demand to estimate the heat transfer coefficient in the EAF. Fathi et al. [8] presented a computational model to estimate the arc energy distribution to conductive, convective, and radiative heat transfer processes with low enough computation times for online applicability. Li et al. [9] have proposed a model that combines offline and online aspects of the EAF process to adjust the electrode regulation system to optimum practice. Khoshkhoo et al. [10] introduced a model for efficient power control of EAFs that uses

With the strict standards for steel quality and high production rates, the demand for faster and more convenient slag composition analysis for both electric arc and ladle furnaces has become a major issue in industrial steel plants. To overcome the time-delay between slag sampling and results of the slag composition analysis, an on-line slag composition analysis is required. Such a method that can be used in on-line analysis and is also chemically sensitive to the slag composition is optical emission spectroscopy. In this work, the optical emissions from the arc have been measured in an industrial ladle furnace and used for slag composition analysis. This article focuses on CaF 2 and MgO, since the CaF 2 is a common additive material in the ladle treatment and high MgO content means that the ladle refractory lining is dissolving into the slag. The analysis has been carried out by comparing emission line ratios to the XRF analyzed ratios of CaF 2 /MgO and MnO/MgO, respectively. The results show that several atomic emissions lines of calcium, magnesium, and manganese can be used to evaluate the CaF 2 /MgO and MnO/MgO ratios in the slag. It was found out that the plasma temperature derived from Ca I emission lines has a non-linear relation with the CaF 2 content of the slag. Additionally, the dissociation pathways of molecular slag components were determined and studied in different plasma temperatures with equilibrium composition computation in order to determine the relations between the slag and plasma compositions.