scholarly journals Machine Learning Methods for the Prediction of the Inclusion Content of Clean Steel Fabricated by Electric Arc Furnace and Rolling

Metals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 914
Author(s):  
Estela Ruiz ◽  
Diego Ferreño ◽  
Miguel Cuartas ◽  
Lara Lloret ◽  
Pablo M. Ruiz del Árbol ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hot Rolling ◽  
Electric Arc Furnace ◽  
Electric Arc ◽  
Classification Model ◽  
Support Vector ◽  
Arc Furnace ◽  
Cold Forming ◽  
Inclusion Content ◽  
The Impact

Machine Learning classification models have been trained and validated from a dataset (73 features and 13,616 instances) including experimental information of a clean cold forming steel fabricated by electric arc furnace and hot rolling. A classification model was developed to identify inclusion contents above the median. The following algorithms were implemented: Logistic Regression, K-Nearest Neighbors, Decision Tree, Random Forests, AdaBoost, Gradient Boosting, Support Vector Classifier and Artificial Neural Networks. Random Forest displayed the best results overall and was selected for the subsequent analyses. The Permutation Importance method was used to identify the variables that influence the inclusion cleanliness and the impact of these variables was determined by means of Partial Dependence Plots. The influence of the final diameter of the coil has been interpreted considering the changes induced by the process of hot rolling in the distribution of inclusions. Several variables related to the secondary metallurgy and tundish operations have been identified and interpreted in metallurgical terms. In addition, the inspection area during the microscopic examination of the samples also appears to influence the inclusion content. Recommendations have been established for the sampling process and for the manufacturing conditions to optimize the inclusionary cleanliness of the steel.

Construction and survey of changes in electric arc furnace parameters based on energy conservation model using Pscad software

2021 ◽  
Vol 15 (1) ◽  
pp. 75-83
Author(s):  
Ngoc Toan Luong ◽  
◽  
Duc Tung Doan
Keyword(s):  
Energy Conservation ◽  
Power Quality ◽  
Power Level ◽  
Electric Arc Furnace ◽  
Electric Arc ◽  
Arc Furnace ◽  
Electricity Grid ◽  
Power Quality Improvement ◽  
Conservation Model ◽  
The Impact

Actual analysis showed that the arc furnace current contains many harmonics that adversely affect the power quality. There are many domestic and foreign reports on modeling and assessing the impact of EAF on the grid based on different models. However, EAF's selection of capacity for research and application of power quality improvement devices suitable to the power level has not been mentioned, these models are mainly built on Matlab Simulink software. should be primarily academic. PSCAD is one of the widely used software for electrical system simulation and is used by large companies such as ABB, Korean power corporation Kepco. Building EAF model with PSCAD software will increase the ability to apply simulation results into practice. The objective of the paper is to build an electric arc furnace model based on the energy conservation model with PSCAD software, thereby assessing the change of parameters in the model and the effect of this load on electricity grid during operation.

Processing and Recycling Experiments of Dust from Electric Arc Furnace

2011 ◽  
Vol 378-379 ◽  
pp. 719-722 ◽  
Author(s):  
Zorica Bacinschi ◽  
Cristiana Zizi Rizescu ◽  
Elena Valentina Stoian ◽  
Dan Nicolae Ungureanu ◽  
Aurora Anca Poinescu ◽  
...  
Keyword(s):  
Steel Production ◽  
Electric Arc Furnace ◽  
Electric Arc ◽  
Raw Material ◽  
Arc Furnace ◽  
Leaching Test ◽  
Fine Grained ◽  
Steel Making ◽  
Fine Grained Material ◽  
The Impact

The processing and recycling experiments of dust from Electric Arc Furnace (EAF) in industrial conditions aimed at highlighting the minimizing possibility of this waste by transforming it into a by-product that can represent either a secondary raw material for steel making in EAF or to recover iron, zinc and lead (the Waltz process). Electric-arc furnace dust (EAFD) is a by-product of steel production and recycling. This fine-grained material contains high amounts of zinc and iron as well as significant amounts of potentially toxic elements such as lead, cadmium and chromium. Therefore, the treatment and stabilization of this industrial residue is necessary. Leaching test is a method of evaluating the impact of waste that is stored (soil, water table).

scholarly journals Characteristics of the Supersonic Combustion Coherent Jet for Electric Arc Furnace Steelmaking

Materials ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 3504
Author(s):  
Fei Zhao ◽  
Rong Zhu ◽  
Wenrui Wang
Keyword(s):  
Turbulence Intensity ◽  
Dynamic Pressure ◽  
Electric Arc Furnace ◽  
Electric Arc ◽  
Supersonic Combustion ◽  
Arc Furnace ◽  
Coherent Jet ◽  
Jet Characteristics ◽  
The Impact ◽  
Stirring Intensity

Herein, a supersonic combustion coherent jet is proposed based on current coherent jet technology to improve the impact capacity of a coherent jet and increase the stirring intensity of the electric arc furnace (EAF) bath. Further, numerical simulations and an experimental analysis are combined to study the supersonic combustion coherent jet characteristics, including the Mach number, dynamic pressure, static temperature, vorticity, and turbulence intensity, in the EAF steelmaking environment. The results show that the supersonic combustion coherent jet exhibits stable combustion in a high-temperature EAF steelmaking environment. The supersonic combustion flame generated by the supersonic shrouding fuel gas can envelop the main oxygen jet more effectively than current coherent jets. Furthermore, the velocity attenuation, vorticity, and turbulence intensity performances of the supersonic combustion coherent jet are better when compared with those of the current coherent jet. The velocity core length of the main oxygen jet for the supersonic combustion coherent jet is 30% longer than that of the current coherent jet, resulting in an improved impact capacity and stirring intensity of the molten bath.

scholarly journals Influence of lance height and angle on the penetration depth of inclined coherent and conventional supersonic jets in electric arc furnace steelmaking

2020 ◽  
pp. 19-19
Author(s):  
Xuetao Wu ◽  
Rong Zhu ◽  
Guangsheng Wei ◽  
Kai Dong
Keyword(s):  
Theoretical Model ◽  
Penetration Depth ◽  
Supersonic Jet ◽  
Electric Arc Furnace ◽  
Electric Arc ◽  
Supersonic Jets ◽  
Velocity Variation ◽  
Arc Furnace ◽  
Lance Height ◽  
The Impact

Nowadays, coherent and conventional supersonic jets are widely used in electric arc furnace (EAF)steelmaking processes. Generally, these jets are installed in the EAF oven wall with a tilt angle of 35-45?. However, limited studies have been conducted on the impact characteristics of these inclined supersonic jets. This study developed an optimized theoretical model to calculate the penetration depth of inclined coherent and conventional supersonic jets by combining theoretical modeling and numerical simulations. The computational fluid dynamics results are validated against water model experiments. A variable k is newly defined to reflect the velocity variation, which is related to the jet exit at the jet free distance. The results of the optimized theoretical model show that the lance height and lance angle influence the penetration depth of the inclined supersonic jet. At the same lance angle, the penetration depth decreases with the increase in the lance height. Similarly, it decreases with the decrease in lance angle at the same lance height. In addition, the penetration depth of an inclined coherent supersonic jet is larger than that of an inclined conventional supersonic jet under the same conditions. An optimized theoretical model can accurately predict the penetration depths of the inclined coherent and conventional supersonic jets.

scholarly journals Reliable Identification of Oolong Tea Species: Nondestructive Testing Classification Based on Fluorescence Hyperspectral Technology and Machine Learning

Agriculture ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1106
Author(s):  
Yan Hu ◽  
Lijia Xu ◽  
Peng Huang ◽  
Xiong Luo ◽  
Peng Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Principal Component ◽  
Classification Model ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Oolong Tea ◽  
The Impact ◽  
T Distribution

A rapid and nondestructive tea classification method is of great significance in today’s research. This study uses fluorescence hyperspectral technology and machine learning to distinguish Oolong tea by analyzing the spectral features of tea in the wavelength ranging from 475 to 1100 nm. The spectral data are preprocessed by multivariate scattering correction (MSC) and standard normal variable (SNV), which can effectively reduce the impact of baseline drift and tilt. Then principal component analysis (PCA) and t-distribution random neighborhood embedding (t-SNE) are adopted for feature dimensionality reduction and visual display. Random Forest-Recursive Feature Elimination (RF-RFE) is used for feature selection. Decision Tree (DT), Random Forest Classification (RFC), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) are used to establish the classification model. The results show that MSC-RF-RFE-SVM is the best model for the classification of Oolong tea in which the accuracy of the training set and test set is 100% and 98.73%, respectively. It can be concluded that fluorescence hyperspectral technology and machine learning are feasible to classify Oolong tea.

Metal composition of ambient PM2.5 influences the pulmonary function of schoolchildren: A case study of school located nearby of an electric arc furnace factory

2018 ◽  
Vol 34 (4) ◽  
pp. 253-261 ◽  
Author(s):  
Bing-Fang Huang ◽  
Ya-Chi Chang ◽  
Ai-Ling Han ◽  
Hui-Tsung Hsu
Keyword(s):  
Pulmonary Function ◽  
Pulmonary Function Tests ◽  
Health Impact ◽  
Electric Arc Furnace ◽  
Control Area ◽  
Electric Arc ◽  
Epithelial Cell Line ◽  
Arc Furnace ◽  
The Impact ◽  
Mass Concentrations

The present study combined air sampling with pulmonary function tests (PFTs) to determine both the extent of air pollution proximal to an electric arc furnace (EAF) and its impact on human health. The mass concentrations of particulate matter with aerodynamic diameters less than 2.5 µm (PM2.5) in exposure areas were not significantly higher than the samples taken at a control area. However, the concentrations of five metal elements, Cd, Cr, Cu, Ni, and Zn in PM2.5 were significantly higher in the exposure area than that of the control area. PFTs showed that the average forced vital capacity (FVC) of boys was decreased with decreasing distance from the EAF factory. With normalization of pulmonary function by age, height, and weight, we found that the FVC became more negative with a decrease in distance from the EAF. Lastly, regression analysis was performed to analyze the impact of the concentrations of the five metals in PM2.5 on the performance of pulmonary function. The results showed that the metals can be ranked from the highest to the lowest in terms of impact on the FVC of boys as follows: Cr, Cd, Ni, Cu, and Zn. This finding is consistent with the ranking of metal toxicity reported in the literature for a rat lung epithelial cell line. The results of this study showed that only measuring PM2.5 mass concentrations may not provide a full explanation of its toxicity and health effects. The chemical composition of the PM2.5 can be an important factor that determined the health impact of PM2.5.

Numerical Modeling of the Dynamic Behavior of Shredder Hammers and Correlation With Material Damage

2012 ◽  
Author(s):  
Eugenio G. M. Brusa ◽  
Nicola Bosso ◽  
Stefano Morsut
Keyword(s):  
Finite Element ◽  
Dynamic Behavior ◽  
Electric Arc Furnace ◽  
Electric Arc ◽  
Industrial Plant ◽  
Strong Impact ◽  
Arc Furnace ◽  
Body Dynamics ◽  
Multi Body ◽  
The Impact

Pre-forming and fragmentation of the ferrous scrap used into the electric arc furnace for the melting process is a relevant activity for a steelmaking plant. Shredding machines are applied to suitably reduce the size of scrap. A set of hammers is connected to a main rotor. Rotation converts the high kinetic energy of each hammer into a strong impact against the scrap. Metallic parts are crushed and fed into the electric arc furnace. Damage of the hammer material is due to impact, vibration, wear and temperature. In addition fatigue affects its life. An effective prediction of the damage location as well as of its propagation in the hammer is rather difficult. A resident health monitoring system cannot be easily applied. Therefore a preliminary model was built to predict the dynamic behavior of each hammer in rotation and to compute the applied stress, while the impact is occurring. A rotor-dynamic analysis was performed by means of a Multi Body Dynamics and a Finite Element code, respectively. Magnitude, direction and frequency of the dynamic loads were first computed by the Multi Body Dynamics code. Stress exciting the hammer material was then computed by the Finite Element Method. Nonlinearities are crucial for the design operation. Friction among the materials, clearance between the pin and the hammer and the nonlinear behavior of materials are all relevant for the nonlinear dynamic response of the hammer. Numerical results were compared to some preliminary observations performed on an industrial plant. They allowed motivating the occurrence of cracks and wear effects in some critical points of the hammer. Some design criteria were defined and successfully tested to improve the performance of materials.

Sign in / Sign up

Export Citation Format

Share Document