Abstract
Operation of the electric arc furnaces (EAFs) is a subject to consider fluctuations in terms of its key performance indicators, such as the electrical energy consumption (EEC), tap-to-tap time, steel yield, and others. In this paper, a more detailed analysis of the electric arc furnace data is performed, investigating its EEC. It is well known that the EEC is affected by the weight and the type of charged scrap, the operational delays, and the tapping temperature. On the other hand, one can also deduce that the feeds, such as the carbon and the oxygen, could also affect the EEC, due to their role in redox equations and impact to the bath energy balance. Therefore, special attention is devoted to the analysis of the carbon-to-oxygen ratio during the electric arc furnace operation and the consequent influence of the oxygen availability on the EEC. Statistical analysis of more than 2500 heats of data, which were clustered according to the produced steel grade and the charged scrap mixture, has revealed that besides the beforementioned factors, fluctuations in EEC could appear also due to different amounts of added carbon and oxygen. Since the furnace operators usually rely on predefined guidelines and their own experience when actuating the furnace, a simplistic statistical approach can be used to reveal some of the weaknesses in the control routines, which can be used as a starting point to improve their actuation, leading to decreased energy consumption.Graphical
Highlights
In the steel making industry, electric arc furnaces (EAFs) are considered as one of the systems with the largest consumption of electrical energy
The aim of the study is twofold, first to determine the list of influential factors, affecting the EAFs energy consumption (EEC), and second, to propose a relatively simple methodology based on statistical data manipulation and clustering that can be used to obtain some guidelines in the EAFs efficiency in terms of EEC prior to processing a certain heat, and/or to determine a more optimal charging patterns and EAF actuation
As proposed in [17, 18], operational delays should play an important role in overall EAF EEC
Summary
In the steel making industry, electric arc furnaces (EAFs) are considered as one of the systems with the largest consumption of electrical energy. Several approaches to enhance or optimize the EAF control or its subsidiary systems have been proposed [8,9,10,11,12], with the goal of lowering the production costs of an EAF. In this regard, electrical energy consumption (EEC) has been a subject of considerate attention. Several studies on prediction of the EAFs’ EEC using different modeling approaches have been performed [13,14,15,16], based either on commercial software, regression methods, or different artificial-intelligence approaches. The methods in such studies usually focus on calculating a reliable EEC forecast and not as much on the factors that cause the fluctuations in EECs, or on the ideas and solutions that would lead to lower EEC
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