Designing of Synergistic Waste Mixtures for Multiphase Reactive Smelting

Manojlović, Vaso; Kamberović, Željko; Sokić, Miroslav; Gavrilovski, Milorad; Korać, Marija

doi:10.3390/met6060138

Cited by 5 publications

(3 citation statements)

References 23 publications

(49 reference statements)

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“…It has given good results in system identification in three-phase ferrosilicon coated arc furnaces [9]. Heat losses and energy needs have been estimated [10,11]. Force Control of Hydraulic Actuator is provided [12].…”

Section: Introductionmentioning

confidence: 99%

The Use of Artificial Neural Networks in the Control of Electric Arc Furnaces

YILDIZ>¹,

Özdemir

2022

International Journal of 3D Printing Technologies and Digital Industry

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Today, control systems have become an important branch of science in parallel with the increase of production and quality needs. There are purpose-specific automatic control systems and algorithms controlling them for production in industrial facilities. In this study, modeling electric arc furnace scrap melting plant, which has an essential place in the iron-steel industry has been made using artificial neural networks. The facility where the study is carried out is in active production and controlled by classical algorithms. Artificial neural networks were trained using the data taken over the current control system and pressure sensors attached to the electrodes and the modeling and control of the arc furnace with the trained network was carried out. The software developed with an artificial neural network to control the electrodes used in electric arc furnaces provided 98% success in monitoring the system including the operator’s intervention out of the algorithm. All input and output data of an active production facility were copied to the network with the developed software. Since this software does not require various calculations, calibrations and parameter changes, it responds faster than the classical control algorithm used in the factory.

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Section: Introductionmentioning

confidence: 99%

The Use of Artificial Neural Networks in the Control of Electric Arc Furnaces

YILDIZ>¹,

Özdemir

2022

International Journal of 3D Printing Technologies and Digital Industry

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“…Around 70% of global iron-carbon alloy is produced in basic oxygen furnaces (BOFs), which consume 770 kg of coking coal to produce 1 tonne of steel. Furthermore, 28% of iron-carbon alloy/steel is produced in electric arc furnaces (EAF), and significant quantities of electricity used for the EAF process are generated from coal-fired power stations [2,3]. There is a need to reduce the carbon footprint in iron-carbon alloy industries all over the world, and hence it is crucial to explore environment friendly carbon resources as a replacement of coal or coke.…”

Section: Introductionmentioning

confidence: 99%

Carbon Dissolution Using Waste Biomass—A Sustainable Approach for Iron-Carbon Alloy Production

Mansuri

Farzana

Rajarao

et al. 2018

Metals

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This paper details the characterisation of char obtained by high-temperature pyrolysis of waste macadamia shell biomass and its application as carbon source in iron-carbon alloy production. The obtained char was characterised by ultimate and proximate analysis, X-ray diffraction (XRD), Raman spectroscopy, Fourier-transform infrared spectroscopy (FTIR), X-ray photon spectroscopy (XPS), Brunauer-Emmett-Teller (BET) surface area via N 2 isothermal adsorption and scanning electron microscopy (SEM). The results indicated that obtained char is less porous, low in ash content, and high in carbon content. Investigation of iron-carbon alloy formation through carbon dissolution at 1550 • C was carried out using sessile drop method by using obtained char as a carbon source. Rapid carbon pickup by iron was observed during first two minutes of contact and reached a saturation value of 5.18 wt % of carbon after 30 min. The carbon dissolution rate using macadamia char as a source of carbon was comparatively higher using than other carbonaceous materials such as metallurgical coke, coal chars, and waste compact discs, due to its high percentage of carbon and low ash content. This research shows that macadamia shell waste, which has a low content of ash, is a valuable supplementary carbon source for iron-carbon alloy industries.

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“…The main function of traditional electric arc furnace (EAF) steelmaking process is melting scrap and heating [1][2][3][4]. With the development of continuous casting and refining technology, the raw material prices and power consumption, most of the EAF charging hot metal instead of scrap.…”

Section: Introductionmentioning

confidence: 99%

Research on the Molten Bath of EBT Area with Bottom-Blowing in EAF Steelmaking Process

Yang¹,

Jiang²,

Li³

et al. 2017

Preprint

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Bottom-blowing, as widely adopted in electric arc furnace (EAF) steelmaking technology, stir the molten bath by blowing inert gas (Ar, N2) from the bottom of EAF to promote fluid flow and uneven metallurgical reaction in the molten bath. However, for eccentric structure design of electric arc furnace, the problem still exists in EBT(Eccentric Bottom Tapping)area. In this study, by the analysis of previous research of molten bath at different bottom-blowing gas flow rates, it is found that the dead zone mainly appears in the region of the EBT area. So the ways that increase bottom-blowing flow in the EBT area to reduce the dead zone area have been proposed. A computational fluid dynamics model was established to investigate the effects of bottom-blowing gas flow rate in EBT area on the fluid flow characteristics in the EAF molten bath. Increasing bottom-blowing flow in the EBT area can more effectively improve molten steel flow and reduce the dead zone area. Based on industrial application research, the dynamic conditions of steelmaking process is improved with bottom-blowing by increasing flow in EBT area, the lime consumption, steel consumption and tap-to-tap time are reduced, the contents of FeO and endpoint carbon-oxygen equilibrium is reduced, and the yield of Si, Mn and Al is increased before the refining process.

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Designing of Synergistic Waste Mixtures for Multiphase Reactive Smelting

Cited by 5 publications

References 23 publications

The Use of Artificial Neural Networks in the Control of Electric Arc Furnaces

The Use of Artificial Neural Networks in the Control of Electric Arc Furnaces

Carbon Dissolution Using Waste Biomass—A Sustainable Approach for Iron-Carbon Alloy Production

Research on the Molten Bath of EBT Area with Bottom-Blowing in EAF Steelmaking Process

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