The examples that have been presented show but a few of the effects that can Influence energy efficiency in the EAF. If these are extrapolated further to Include chemistry variations in the feed materials, it can be seen that the EAF operator has a monumental task trying to track and control EAF operations. The conditions in the EAF change not only from heat to heat but also on a minute-by-minute basis throughout the tap-to-tap cycle. In the past, most information dealing with energy use and efficiency in the EAF has been based on gross balances made over a heat and in many cases these involve implicit assumptions which may not be entirely accurate. Energy efficiency and energy losses dictate what energy input level is required for a given operation. However, over the past few years, several tools have been developed to help to provide real-time feedback to EAF operations. If an energy balance is performed in "real time", It can be used to understand periods of high and low efficiency in the EAF operation. This method also provides instant feedback to the operator when he deviates from standard practices and allows the operator to see whether these deviations are beneficial or not. Many factors can affect energy efficiency - scrap chemistry, slag foaming, equipment configuration, sizing of equipment- all play a part In determining how efficient: the energy transfer to the steel is in the EAF. Many operations log a significant amount of data but have difficulty turning this data into useable information in a timely manner. It has been demonstrated that some energy inputs/outputs can be highly variable and that many process parameters are highly inter-related. As a result, the first step in understanding energy utilization in any given operation is to develop an effective benchmarking system. Development of a simple energy balance model can be an effective start to quantifying energy use in the EAF. As additional information becomes available and the database is expanded to include melt-in chemistry, slag chemistry, EAF dust generation rates and analysis and tracking of scrap charge make-up, it becomes possible to make more accurate process assumptions through statistical analysis. This allows for a better overall understanding of a particular operation and sets the path forward towards EAF optimization.
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