Electric arc modeling of the EAF using differential evolution algorithm

Stopar, Klemen; Kovačić, Miha; Kitak, Peter; Pihler, J.

doi:10.1080/10426914.2016.1257859

Cited by 8 publications

(5 citation statements)

References 46 publications

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“…In the current industrial scenario, the ferrous production industries need to run the processes under very tight optimization, in order to stay competitive and continue strategies of high productivity and less emission along with the other requirements. As demonstrated in a number of recent studies [27][28][29][30][31][32][33] the use of data driven evolutionary approaches, like what has been adopted here, are clearly emerging as one of the very effective strategies to reach that goal.…”

Section: Resultsmentioning

confidence: 99%

Evolutionary Data Driven Modeling and Multi Objective Optimization of Noisy Data Set in Blast Furnace Iron Making Process

Mahanta

Chakraborti

2018

steel research int.

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Data driven models are constructed for the tuyere cooling heat loss, total blast furnace gas flow, tuyere velocity, productivity, and coke rate for an operational blast furnace of an integrated steel plant by using evolutionary computation methods like bi objective genetic programming (BioGP) and evolutionary neural network (EvoNN), which serve as the objectives for their optimization. The models are used to compute the Pareto tradeoff between these conflicting objectives with the help of predator prey genetic algorithm well tasted for computing the Pareto optimality earlier. The results of optimization and the performances of these models are thoroughly analyzed and accessed and a discussion regarding these data driven models and their influence in the ironmaking process are presented. The results are also compared against a similar calculation performed with the commercial software KIMEME.

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Section: Resultsmentioning

confidence: 99%

Evolutionary Data Driven Modeling and Multi Objective Optimization of Noisy Data Set in Blast Furnace Iron Making Process

Mahanta

Chakraborti

2018

steel research int.

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“…An in-house genetic programming system, programmed using AutoLISP, which is integrated into AutoCAD (i.e., commercial computer-aided design software), was used [15,[17][18][19][20]. Its settings were:…”

Section: Modeling Of Cutting Speed Where the Tool Wears Out Within 15...mentioning

confidence: 99%

Modeling and Optimization of Steel Machinability with Genetic Programming: Industrial Study

Kovačić

Salihu

Gantar

et al. 2021

Metals

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In this paper, machinability influences from the start to end of final product production in a steel plant were analyzed, including chemical composition, deoxidizing agents and casting parameters, which drastically influence the macrostructure and segregation (i.e., chemical nonhomogeneity) of continuously cast and subsequently rolled material. The data (seven parameters from secondary metallurgy, four parameters from the casting process and the content of ten chemical elements) from the serial production of calcium-treated steel grades (254 batches of 25 different steel grades from January 2018 to March 2020) were used for predicting machinability. Machinability was determined based on ISO 3685:1993, where the machinability of each individual batch is represented as the cutting speed and the tool is worn out within fifteen minutes. For the prediction of these cutting speeds, linear regression and genetic programming were used. Out of 25 analyzed steel grades, 20MnV6 steel grade was the most problematic and also the most often produced. Out of 57 produced batches of 20MnVS6 steel, 23 batches had nonconforming machinability. Based on the modeling results, the steelmaking process was optimized. Consequently, 40 additional batches of 20MnV6 (from March 2020 to July 2020) were subsequently produced based on an optimized steelmaking process. In all 40 cases, the required machinability was achieved without changing other properties required by the customers.

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“…In the large hot heel operation, the plasma arc is utilized to heat the molten steel at first, and then to melt the scrap through a heat exchange between the molten steel and scrap. Therefore, the fluid flow and heating rate of the molten steel is dominated by both complex energy and momentum transfer at the interface between the molten steel and plasma arc [7][8][9][10][11][12][13], which is closely related to the steelmaking productivity of EAFs using a large hot heel operation.…”

Section: Introductionmentioning

confidence: 99%

Characteristics Analysis of Fluid Flow and Heating Rate of a Molten Bath Utilizing a Unified Model in a DC EAF

Yao

Jiang

Zhu

et al. 2022

Metals

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Herein, a 2D unified model coupling a plasma arc–molten bath–cavity in a direct current electric arc furnace was developed for a characteristic analysis of the fluid flow and heating rate of the molten bath. The ‘local thermodynamic equilibrium–diffusion approximation’ method was employed for the physical phenomenon at the plasma arc/molten bath interface, and the volume-of-fluid method was used to track the free surface. After ensuring model validation, the formation processes of the cavity and the flow field and heating rate of the molten bath were investigated by utilizing the unified model. The numerical results showed that the formation processes of the cavity contained three stages, namely the expansion, compression, and dynamic equilibrium stages. The arc pressure and plasma shear stress both contributed to the cavity formation, and dominated the expansion of the cavity depth and diameter, respectively. Under plasma arc jet impingement, there were two flow patterns inside the molten bath: (i) a clockwise eddy on the top surface and lateral part of molten bath dominated by plasma shear stress, and (ii) a counter-clockwise eddy in the bottom part of the molten bath dominated by the electromagnetic force. Meanwhile, the main heated region of the molten bath with the plasma arc–molten bath–cavity coupling was in the radial range of 0.2–0.6 m, and a high-temperature region was formed on the top surface of the molten bath caused by plasma shear stress.

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Electric arc modeling of the EAF using differential evolution algorithm

Cited by 8 publications

References 46 publications

Evolutionary Data Driven Modeling and Multi Objective Optimization of Noisy Data Set in Blast Furnace Iron Making Process

Evolutionary Data Driven Modeling and Multi Objective Optimization of Noisy Data Set in Blast Furnace Iron Making Process

Modeling and Optimization of Steel Machinability with Genetic Programming: Industrial Study

Characteristics Analysis of Fluid Flow and Heating Rate of a Molten Bath Utilizing a Unified Model in a DC EAF

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