Physical and Mathematical Modelling of Inert Gas Shrouded Ladle Nozzles, and their Role ion Fluid Flow Patterns and Slag Behaviour in a Four Strand Billet Caster Tundish

In the present study inert gas shrouding practices were simulated using a full scale, four strand, water model of a 12 tonne delta shaped tundish. Compressed air was aspirated into the ladle shroud, so as to model volumetric flow rates ranging between 2% and 10% of steel entry flows. Bubble trajectories, slag layer movements, and flow fields, were visualized. Flow fields were visualised using Particle Image Velocimetry (PIV). A numerical model was also developed using Discrete Phase Modelling (DPM), along with the standard K-£ turbulence model with two way turbulence coupling. Predicted flow fields and bubble trajectories were in good agreement with the water model experiments. From both the physical and mathematical modelling results it was evident that reversed flows were generated within the tundish in the vicinity of the ladle shroudwhich swept away the protective layer of slag and thereby created an exposed 'eye' of steel. The area of this exposed 'eye' increased with increasing amount of shroud gasDans la présente étude, la technologie d'injection de gaz inerte fut simulée à l'aide d'un modèle pleine grandeur de panier répartiteur d'une capacité de 12 tonnes, en forme delta et possédant quatre drains de coulée. De l'air comprimé fut aspiré dans le jet de coulée du creuset de façon à modéliser des débits volumiques de gaz variant entre 2% et 6% du volume d'acier entrant. Les trajectoires des bulles, les mouvements du laitier, et les champs vectoriels des écoulements furent observés. Les champs d'écoulement furent rendu visibles à l'aide d'un « Particle Image Velocimeter (PlV). Un modèle numérique fut aussi développé en utilisant la modélisation biphasée (Discrète Phase Modelling, DPM) et le modèle standard K-E de turbulence avec couplage bidirectionnel (des bulles au fluide et du fluide aux bulles). Les champs d'écoulement et les trajectoires des bulles prévues concordent bien avec les expérimentations sur le modèle réelle utilisant l'eau. À partir des résultats obtenus des deux modèles, mathématique et physique, il est évident que des écoulements inverses sont formés dans le panier répartiteur autour du jet principal par le gaz injecté. Ces écoulements inverses dispersent la couche protectrice de laitier créant ainsi une zone en forme d'œil exposé à l'air. La surface de cet œil augmente avec le débit de gaz

Numerical Modeling of Volatile Organic Compounds (VOC) Emissions during Preheating of Magnesia-Carbon Bricks in a Basic Oxygen Furnace

The refractory preheating process in oxygen furnaces is a dynamic input of energy in a chemically complex system requiring special attention to chemical emissions relative to permissible release limits. This particular industrial and regulatory interest is the emission of volatile organic compounds (VOC), given their detrimental impacts on human health. In the present work, a mathematical model was developed to predict the emission rates of volatile organics during the preheating of a 260-ton basic oxygen furnace. A numerical heat transfer model was developed using finite difference techniques to obtain the thermal profile and then integrated with chemical thermodynamics using FactSage 7.0 (CRCT, Polytechnique Montreal Quebec Canada, H3C 3A7). The parameters that affected VOC emissions were preheating process times, burner gas composition, heating rate, and burner geometry. Two different preheating procedures were compared, and emission rates were predicted with extended use of a top burner providing the greatest degree of emissions control. The mathematical model was validated against plant data with respect to average emission rates of CO, CO2, SOX, and NOX

Effect of Vacuum Heat Treatment on the Microstructure of a Laser Powder-Bed Fusion-Fabricated NiTa Alloy

The semiconductor industry uses a physical vapor-deposition process, with a nickel-tantalum (NiTa) alloy-sputtering target, to apply an amorphous NiTa thin film layer between the magnetic soft underlayer and substrate of a heat-assisted magnetic-recording hard disk drive. Currently, the alloy-sputtering target is produced through a hot-pressing (HP) process followed by a hot isostatic pressing (HIP). In this study, we demonstrate a better process for producing the sputtering targets, using laser powder-bed fusion (L-PBF) followed by vacuum heat treatment (VHT), to produce alloy targets with superior microstructural characteristics that will produce better-quality thin films. We compare as-fabricated (just L-PBF) specimens with specimens produced by L-PBF and then annealed at different conditions. Where the as-fabricated specimens are characterized by columnar dendrites, annealing at 1275 °C for 4 h produces a uniform equiaxed grain microstructure and a uniformly dispersed fcc Ta precipitate. In addition, the average microhardness value is reduced from 725 ± 40 to 594 ± 26 HV0.2 and the maximum compressive residual stress is reduced from 180 ± 50 MPa to 20 ± 10 MPa as the result of dislocation elimination during the recovery and recrystallization process. Finally, due to microstructure recrystallization, the VHT-treated L-PBF NiTa specimens exhibit a smaller grain size (2.1 ± 0.2 µm) than the traditional HIP-treated HP specimens (6.0 ± 0.6 µm)

Effect of Vacuum Heat Treatment on the Microstructure of a Laser Powder-Bed Fusion-Fabricated NiTa Alloy

The semiconductor industry uses a physical vapor-deposition process, with a nickel-tantalum (NiTa) alloy-sputtering target, to apply an amorphous NiTa thin film layer between the magnetic soft underlayer and substrate of a heat-assisted magnetic-recording hard disk drive. Currently, the alloy-sputtering target is produced through a hot-pressing (HP) process followed by a hot isostatic pressing (HIP). In this study, we demonstrate a better process for producing the sputtering targets, using laser powder-bed fusion (L-PBF) followed by vacuum heat treatment (VHT), to produce alloy targets with superior microstructural characteristics that will produce better-quality thin films. We compare as-fabricated (just L-PBF) specimens with specimens produced by L-PBF and then annealed at different conditions. Where the as-fabricated specimens are characterized by columnar dendrites, annealing at 1275 °C for 4 h produces a uniform equiaxed grain microstructure and a uniformly dispersed fcc Ta precipitate. In addition, the average microhardness value is reduced from 725 ± 40 to 594 ± 26 HV0.2 and the maximum compressive residual stress is reduced from 180 ± 50 MPa to 20 ± 10 MPa as the result of dislocation elimination during the recovery and recrystallization process. Finally, due to microstructure recrystallization, the VHT-treated L-PBF NiTa specimens exhibit a smaller grain size (2.1 ± 0.2 µm) than the traditional HIP-treated HP specimens (6.0 ± 0.6 µm)

Least Squares Twin Support Vector Machines to Classify End-Point Phosphorus Content in BOF Steelmaking

End-point phosphorus content in steel in a basic oxygen furnace (BOF) acts as an indicator of the quality of manufactured steel. An undesirable amount of phosphorus is removed from the steel by the process of dephosphorization. The degree of phosphorus removal is captured numerically by the ‘partition ratio’, given by the ratio of %wt phosphorus in slag and %wt phosphorus in steel. Due to the presence of multitudes of process variables, often, it is challenging to predict the partition ratio based on operating conditions. Herein, a robust data-driven classification technique of least squares twin support vector machines (LSTSVM) is applied to classify the ‘partition ratio’ to two categories (‘High’ and ‘Low’) steels indicating a greater or lesser degree of phosphorus removal, respectively. LSTSVM is a simpler, more robust, and faster alternative to the twin support vector machines (TWSVM) with respect to non-parallel hyperplanes-based binary classifications. The relationship between the ‘partition ratio’ and the chemical composition of slag and tapping temperatures is studied based on approximately 16,000 heats from two BOF plants. In our case, a relatively higher model accuracy is achieved, and LSTSVM performed 1.5–167 times faster than other applied algorithms

Understanding Dephosphorization in Basic Oxygen Furnaces (BOFs) Using Data Driven Modeling Techniques

Owing to the continuous deterioration in the quality of iron ore and scrap, there is an increasing focus on improving the Basic Oxygen Furnace (BOF) process to utilize lower grade input materials. The present paper discusses dephosphorization in BOF steelmaking from a data science perspective, which thus enables steelmakers to produce medium and low phosphorus steel grades. In the present study, data from two steel mills (Plant I and Plant II) were collected and various statistical methods were employed to analyze the data. While most operators in steel plants use spreadsheet-based techniques and linear regression to analyze data, this paper discusses on the suitability of selecting various statistical methods, and benchmarking tests to analyze such dephosphorization data sets. The data contains a wide range of operating conditions, both low and high phosphorus input loads, different slag basicity’s, different slag chemistries, and different end point temperatures, etc. The predicted phosphorus partition from various statistical models is compared against plant data and verified against previously published research

An Application of Decision Tree-Based Twin Support Vector Machines to Classify Dephosphorization in BOF Steelmaking

Ensuring the high quality of end product steel by removing phosphorus content in Basic Oxygen Furnace (BOF) is essential and otherwise leads to cold shortness. This article aims at understanding the dephosphorization process through end-point P-content in BOF steelmaking based on data-mining techniques. Dephosphorization is often quantified through the partition ratio (<inline-formula> <math display="inline"> <semantics> <mrow> <msub> <mi>l</mi> <mi>p</mi> </msub> </mrow> </semantics> </math> </inline-formula>) which is the ratio of wt% P in slag to wt% P in steel. Instead of predicting the values of <inline-formula> <math display="inline"> <semantics> <mrow> <msub> <mi>l</mi> <mi>p</mi> </msub> </mrow> </semantics> </math> </inline-formula>, the present study focuses on the classification of final steel based on slag chemistry and tapping temperature. This classification signifies different degrees (&lsquo;High&rsquo;, &lsquo;Moderate&rsquo;, &lsquo;Low&rsquo;, and &lsquo;Very Low&rsquo;) to which phosphorus is removed in the BOF. Data of slag chemistry and tapping temperature collected from approximately 16,000 heats from two steel plants (Plant I and II) were assigned to four categories based on unsupervised K-means clustering method. An efficient decision tree-based twin support vector machines (TWSVM) algorithm was implemented for category classification. Decision trees were constructed using the concepts: Gaussian mixture model (GMM), mean shift (MS) and affinity propagation (AP) algorithm. The accuracy of the predicted classification was assessed using the classification rate (CR). Model validation was carried out with a five-fold cross validation technique. The fitted model was compared in terms of CR with a decision tree-based support vector machines (SVM) algorithm applied to the same data. The highest accuracy (&ge;97%) was observed for the GMM-TWSVM model, implying that by manipulating the slag components appropriately using the structure of the model, a greater degree of P-partition can be achieved in BOF

Hybrid Method for Endpoint Prediction in a Basic Oxygen Furnace

Strict monitoring and prediction of endpoints in a Basic Oxygen Furnace (BOF) are essential for end-product quality and overall process efficiency. Existing control models are mostly developed based on thermodynamic principles or by deploying advanced sensors. This article aims to propose a novel hybrid algorithm for endpoint temperature, carbon, and phosphorus, based on heat and mass balance and a data-driven technique. Three types of static models were established in this study: firstly, theoretical models, based on user-specified inputs, were formulated based on mass and energy balance; secondly, artificial neural networks (ANN) were developed for endpoints predictions; finally, the proposed hybrid model was established, based upon exchanging outputs among theoretical models and ANNs. Data of steelmaking production details collected from 28,000 heats from Tata Steel India were used for this article. Machine learning model validation was carried out with five-fold cross-validation to ensure generalizations in model predictions. ANNs are found to achieve better predictive accuracies than theoretical models in all three endpoints. However, they cannot be directly applied in any steelmaking plants, due to possible variations in the production setting. After applying the hybrid algorithm, normalized root mean squared errors are reduced for endpoint carbon and phosphorus by 3.7% and 9.77%