EAF process optimization based on continuous analysis of off-gases and real-time control of chenical package parameters: the case of TAMSA

Techint Goodfellow Technologies (formerly Stantec) implemented their Goodfellow EFSOP™ system at Tubos de Acero de Mexico, SA (TAMSA) in late 2002. The technology was used to optimize the use of chemical energy within the electric arc furnace (EAF). The project was a success providing TAMSA a 4.4% reduction in conversion costs (oxygen, methane, electricity and carbon) and a reduction of 1 minute in power-on-time. Subsequently, in late 2003, Techint installed and commissioned their KT-Chemical package at TAMSA. The Goodfellow EFSOP™ system was again used to optimize chemical energy usage within the EAF. The Goodfellow Expert Furnace System Optimization Process (Goodfellow EFSOP™) is a proprietary process that uses continuous off-gas analysis, along with process monitoring to optimize the use of chemical energy within the electric arc furnace (EAF). Optimization is achieved by adjustments to the electric furnace process (carbon charge practice, injected carbon, methane and oxygen), according to analysis of off-gas measurements. Further benefits are provided through dynamic control of oxygen and methane in response to real-time off-gas composition. This paper details the application of the Goodfellow EFSOP™ optimization process to the KT chemical package at TAMSA and concludes with a summary of the achievements provided by the merging of these two technologies. Ultimately, a reduction in electrical energy (12.3%) and methane consumption (33%) were achieved at TAMSA. Economically, these savings outweigh the increase in total carbon usage (11%) and oxygen consumption (14.6%) and have provided an overall 2% reduction in power-on-time (1 minute), considering an increase in tapping weight by 11% (from 142 to 158) tons liquid steel. Iron oxidation has also been reduced, as indicated by slag chemistry, from over 40% initially to 32% at present. Electrode consumption has been reduced by 9%

Potential of Organic Rankine Cycles (ORC) for waste heat recovery on an Electric Arc Furnace (EAF)

The organic Rankine cycle (ORC) is a mature technology to convert low temperature waste heat to electricity. While several energy intensive industries could benefit from the integration of an ORC, their adoption rate is rather low. One important reason is that the prospective end-users find it difficult to recognize and realise the possible energy savings. In more recent years, the electric arc furnaces (EAF) are considered as a major candidate for waste heat recovery. Therefore, in this work, the integration of an ORC coupled to a 100 MWe EAF is investigated. The effect of working with averaged heat profiles, a steam buffer and optimized ORC architectures is investigated. The results show that it is crucial to take into account the heat profile variations for the typical batch process of an EAF. An optimized subcritical ORC (SCORC) can generate an electricity output of 752 kWe with a steam buffer working at 25 bar. However, the use of a steam buffer also impacts the heat transfer to the ORC. A reduction up to 61.5% in net power output is possible due to the additional isothermal plateau of the steam

Case study of an Organic Rankine Cycle (ORC) for waste heat recovery from an Electric Arc Furnace (EAF)

The organic Rankine cycle (ORC) is a mature technology for the conversion of waste heat to electricity. Although many energy intensive industries could benefit significantly from the integration of ORC technology, its current adoption rate is limited. One important reason for this arises from the difficulty of prospective investors and end-users to recognize and, ultimately, realise the potential energy savings from such deployment. In recent years, electric arc furnaces (EAF) have been identified as particularly interesting candidates for the implementation of waste heat recovery projects. Therefore, in this work, the integration of an ORC system into a 100 MWe EAF is investigated. The effect of evaluations based on averaged heat profiles, a steam buffer and optimized ORC architectures is investigated. The results show that it is crucial to take into account the heat profile variations for the typical batch process of an EAF. An optimized subcritical ORC system is found capable of generating a net electrical output of 752 kWe with a steam buffer working at 25 bar. If combined heating is considered, the ORC system can be optimized to generate 521 kWe of electricity, while also delivering 4.52 MW of heat. Finally, an increased power output (by 26% with combined heating, and by 39% without combined heating) can be achieved by using high temperature thermal oil for buffering instead of a steam loop; however, the use of thermal oil in these applications has been until now typically discouraged due to flammability concerns

On the long-term correlations and multifractal properties of electric arc furnace time series

In this paper, we study long-term correlations and multifractal properties elaborated from time series of three-phase current signals coming from an industrial electric arc furnace plant. Implicit sinusoidal trends are suitably detected by considering the scaling of the fluctuation functions. Time series are then filtered via a Fourier-based analysis, removing hence such strong periodicities. In the filtered time series we detected long-term, positive correlations. The presence of positive correlations is in agreement with the typical V--I characteristic (hysteresis) of the electric arc furnace, providing thus a sound physical justification for the memory effects found in the current time series. The multifractal signature is strong enough in the filtered time series to be effectively classified as multifractal

Applications of thermal energy storage to process heat and waste heat recovery in the iron and steel industry

The system identified operates from the primary arc furnace evacuation system as a heat source. Energy from the fume stream is stored as sensible energy in a solid medium (packed bed). A steam-driven turbine is arranged to generate power for peak shaving. A parametric design approach is presented since the overall system design, at optimum payback is strongly dependent upon the nature of the electric pricing structure. The scope of the project was limited to consideration of available technology so that industry-wide application could be achieved by 1985. A search of the literature, coupled with interviews with representatives of major steel producers, served as the means whereby the techniques and technologies indicated for the specific site are extrapolated to the industry as a whole and to the 1985 time frame. The conclusion of the study is that by 1985, a national yearly savings of 1.9 million barrels of oil could be realized through recovery of waste heat from primary arc furnace fume gases on an industry-wide basis. Economic studies indicate that the proposed system has a plant payback time of approximately 5 years

Multi-Objective Optimization of Electric Arc Furnace Using the Non-Dominated Sorting Genetic Algorithm II

Combining classical technologies with modern intelligent algorithms, this paper introduces a new approach for the optimisation and modelling of the EAF-based steel-making process based on a multi-objective optimisation using evolutionary computing and machine learning. Using a large amount of real-world historical data containing 6423 consecutive EAF heats collected from a melt shop in an established steel plant this work not only creates machine learning models for both EAF and ladle furnaces but also simultaneously minimises the total scrap cost and EAF energy consumption per ton of scrap. In the modelling process, several algorithms are tested, tuned, evaluated and compared before selecting Gradient Boosting as the best option to model the data analysed. A similar approach is followed for the selection of the multi-objective optimisation algorithm. For this task, six techniques are tested and compared based on the hypervolume performance indicator to just then select the Non-dominated Sorting Genetic Algorithm II ( NSGA-II ) as the best option. Given this applied research focus on a real manufacturing process, real-world constraints and variables such as individual scrap price, scrap availability, tap additives and ambient temperature are used in the models developed here. A comparison with an equivalent EAF model from the literature showed a 13% improvement using the mean absolute error in the EAF energy usage prediction as a comparative metric. The multi-objective optimisation resulted in reductions in the energy consumption costs that ranged from 1.87% up to 8.20% among different steel grades and scrap cost reductions ranging from 1.15% up to 5.2%. The machine learning models and the optimiser were ultimately deployed with a graphical user interface allowing the melt-shop staff members to make informed decisions while controlling the EAF operation

Solar silicon via the Dow Corning process

Carbon, as a reductant for quartz, must be made available so as to have suitable reactivity in conjunction with high purity, especially with respect to boron and phosphorus. A detailed experimental plan was developed to do this. Different sources of carbon were selected to be subjected to various purification methods and reactivity-enhancement processes. A developmental scale arc furnace was installed to perform quartz-carbon reactivity testing