Estimation of FeO content in the steel slag using infrared imaging and artificial neural network

Strąkowski, R.; Pacholski, K.; Więcek, B.; Olbrycht, R.; Wittchen, W.; Borecki, M.

doi:10.1016/j.measurement.2017.12.031

Cited by 13 publications

(8 citation statements)

References 17 publications

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“…Artificial neural network (ANN), a technique for artificial intelligence and machine learning, is often applied as a tool to deal with nonlinear problems and offer predictions in civil engineering [1][2][3][4], material science [5,6], etc. The extension of its applications into the iron and steel industry is also reported [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 87%

Influence of Variation/Response Space Complexity and Variable Completeness on BP-ANN Model Establishment: Case Study of Steel Ladle Lining

et al. 2019

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Artificial neural network (ANN) is widely applied as a predictive tool to solve complex problems. The performance of an ANN model is significantly affected by the applied architectural parameters such as the node number in a hidden layer, which is largely determined by the complexity of cases, the quality of the dataset, and the sufficiency of variables. In the present study, the impact of variation/response space complexity and variable completeness on backpropagation (BP) ANN model establishment was investigated, with a steel ladle lining from secondary steel metallurgy as the case study. The variation dataset for analysis comprised 160 lining configurations of ten variables. Thermal and thermomechanical responses were obtained via finite element (FE) modeling with elastic material behavior. Guidelines were proposed to define node numbers in the hidden layer for each response as a function of the node number in the input layer weighted with the percent value of the significant variables contributing above 90% to the response, as well as the node number in the output layer. The minimum numbers of input variables required to achieve acceptable prediction performance were three, five, and six for the maximum compressive stress, the end temperature, and the maximum tensile stress. empirical equations into other fields are far less satisfying. Equations (1) and (2) show more robust applications, yet they yield a rather larger number for trials.

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Section: Introductionmentioning

confidence: 87%

Influence of Variation/Response Space Complexity and Variable Completeness on BP-ANN Model Establishment: Case Study of Steel Ladle Lining

et al. 2019

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“…The most significant oxides composing steelmaking slag are O, K2O and other are also oxide compounds [11,12,15,16]. FeO cant meaning, and thus knowledge of the said parameter facilitates application of adequate technological procedures and improves production process.…”

Section: Presence Of Particular Components In Slag and Mutual Proportmentioning

confidence: 99%

FeO content estimation in the steel slag using Raman spectroscopy in NIR range

Shatarah¹,

Imiela²,

Surmacki³

et al. 2018

Proceedings of the 2018 International Conference on Quantitative InfraRed Thermography

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“…Specifically, among the factors that must be considered there are multiple chemical reactions, changes in the temperature, and the formation of gases and other intermediate species. In order to overcome these issues, many techniques have been proposed for characterizing and monitoring pyrometallurgical processes based on the measurement of optical properties such as the visible-near-infrared (VIS-NIR) spectral emissions [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ]. On the one hand, in the iron and steel industry, several optical studies based on radiometry and spectral imaging have measured molten-phase temperature, the steel emissivity, and the slag absorption and reflection coefficients.…”

Section: Introductionmentioning

confidence: 99%

“…Based on these indicators, it has been possible to develop an optical technique for online slag detection and estimation of the melt depth and the slag layer thickness, which are key for the control and proper monitoring of the siderurgical processes. [ 17 , 18 , 19 , 20 , 21 ]. On the other hand, only few developments based on optical technology for control and monitoring of copper pyrometallurgical processes have been reported [ 10 , 11 , 12 , 13 , 14 , 15 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

A Radiometric Technique for Monitoring the Desulfurization Process of Blister Copper

Vásquez

Pérez

Roa

et al. 2021

Sensors

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In this paper, a novel optical technique for following the progress of the blister copper desulfurization process is presented. The technique is based on the changes observed in the continuous spectrum of the visible–near-infrared (VIS–NIR) radiation that the blister melt emits while the chemical reactions of the sulfur elimination process are taking place. Specifically, the proposed technique uses an optical probe composed of an optical fiber, a collimating lens, and a quartz tube, which is immersed in the melt. This optical probe provides a field of view of the blowing zone where the desulfurization reaction occurs. The experimental results show that the melt VIS–NIR total irradiance evolves inversely to the SO2 concentration reported by a gas analyzer based on differential optical absorption spectroscopy. Furthermore, the blister copper spectral emissivity as well as the total emissivity observed throughout the process show strong correlation with the sulfur content during desulfurization reaction.

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Estimation of FeO content in the steel slag using infrared imaging and artificial neural network

Cited by 13 publications

References 17 publications

Influence of Variation/Response Space Complexity and Variable Completeness on BP-ANN Model Establishment: Case Study of Steel Ladle Lining

Influence of Variation/Response Space Complexity and Variable Completeness on BP-ANN Model Establishment: Case Study of Steel Ladle Lining

FeO content estimation in the steel slag using Raman spectroscopy in NIR range

A Radiometric Technique for Monitoring the Desulfurization Process of Blister Copper

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