Basic Oxygen Furnace Steelmaking Research Articles

Just-in-time updating soft sensor model of endpoint carbon content and temperature in BOF steelmaking based on deep residual supervised autoencoder

The key of Basic oxygen furnace (BOF) endpoint control is to achieve accurate forecast of the endpoint carbon content and temperature. Due to the highly nonlinear and complex distribution characteristics of process data, the predictions of these two indicators are inaccurate. Although deep learning can extract abstract features of complex data, existing methods are pre-trained in an unsupervised manner. For soft sensor applications, the key is to highlight important information and extract features related to the target variables for prediction. Thus, this paper proposes a feature extraction model based on deep residual supervised autoencoder (DRSupAE). By incorporating important information in the feature of previous hidden layers into deeper feature learning in the form of residual connection, and each residual supervised autoencoder (RSupAE) utilizes the target variables to guide feature extraction. In this way, the stacking of new deep networks not only enhances the impact of important target-related information in layer-wise training, but also captures features related to target variables. Moreover, for the time-varying phenomenon of the BOF steelmaking process, a strategy of just-in-time updating regression network (JITRN) is proposed, which is used to solve the problem that the static regression model cannot adapt to the frequent changes of sample characteristics and causes the prediction performance to decline. An experimental study on BOF steelmaking process data is provided to demonstrate the effectiveness of the proposed method.

Hybrid Model for Predicting Oxygen Consumption in BOF Steelmaking Process Based on Cluster Analysis

The amount of oxygen blown into the converter directly affects the productivity of the converter and the quality of the molten steel, so it is crucial to accurately predict oxygen consumption. Herein, a hybrid model for predicting oxygen consumption in basic oxygen furnace steelmaking process is established. The oxygen consumption of converter is divided into two types: the determined oxygen consumption and the estimated oxygen consumption. The determined oxygen consumption is calculated by mechanism model. And the estimated oxygen consumption is calculated by statistical model. The method of cluster analysis is also introduced into the model. After clustering the data, a statistical model for predicting the estimated oxygen consumption in each subcategory is established, which further improves the prediction accuracy of the model. After dividing the original data into three categories, the hit rate of the hybrid model is 93.3%, when the relative errors of the model are restricted within 5%. As a result, the hybrid model is fully capable of predicting oxygen consumption in converters.

Investigations on the Interaction Behavior between Direct Reduced Iron and Various Melts.

Since the European Union defined ambitious CO2 emission targets, low-carbon-emission alternatives to the widespread integrated blast furnace (BF)—basic oxygen furnace (BOF) steelmaking strategy—are demanded. Direct reduction (DR) with natural gas as the reducing agent, already an industrially applied technology, is such an alternative. Consequently, the melting behavior of its intermediate product, i.e., direct reduced iron (DRI), in either an electric arc furnace (EAF) or a submerged arc furnace (SAF), is of great interest. Based on the conditions in these aggregates, a test series to experimentally simulate the first few seconds after charging DRI was defined. DRI samples with different carbon contents and hot briquetted iron (HBI) were immersed in high- and low-carbon melts as well as high- and low-iron oxide slags. The reacted samples were quenched in liquid nitrogen. The specimens were qualitatively evaluated by investigating their surfaces and cross sections. The dissolution of carbon-free DRI progressed relatively slowly and was driven by heat transfer. However, carbon, present either in the DRI sample or in the melt, not only accelerated the dissolution process, but also reacted with residual iron oxide in the pellet or the slag.

Prediction of oxygen-blowing volume in BOF steelmaking process based on BP neural network and incremental learning

Abstract In view of the characteristics of dynamic basic oxygen furnace (BOF) steelmaking process, prediction models based on backpropagation neural network and incremental learning (BPNN-IL) are proposed for total blow oxygen volume and second blow oxygen volume. The incremental learning is to adjust weights and thresholds of the BPNN according to the difference between the predicted value and actual value of each heat and to adapt to the change in furnace conditions. The combined BPNN-IL models are trained and tested by actual production data, and are further compared with multiple linear regression models and BPNN models. The results show that whether it is total blow oxygen volume or second blow oxygen volume, the BPNN-IL models could provide the most accurate prediction and the introduction of an incremental learning method could further improve the predictive accuracy. So the BPNN-IL method is effective in predicting the oxygen-blowing volume in the BOF steelmaking process.

Numerical simulation on the melting kinetics of steel scrap in iron-carbon bath

Steel scrap melting kinetics is a key factor that affects the scrap ratio and temperature trajectory of Basic Oxygen Furnace steelmaking process and the productivity and energy utilization of Electric Arc Furnace steelmaking process. In this paper, the interface between steel scrap and melt is analyzed and a novel theoretical model of steel scrap melting in iron-carbon bath is established. Moreover, the effects of bath temperature, bath carbon content, preheating temperature and characteristic length of steel scrap on the melting rate of steel scrap, mass transfer coefficient of carbon, heat transfer coefficient and interface carbon concentration were investigated. The results show that the ultimate steady melting rate increases from −2.04 × 10 −4 to −3.32 × 10 −3 m/s with the increases of bath temperature from 1793 K to 1873 K, the ultimate steady heat and mass transfer coefficient first increase and subsequently decrease, are in the range of 1.04 × 10 5 to 2.86 × 10 5 W/m 2 /K and 0 to 2.89 × 10 −4 m/s, respectively. As the bath carbon content increase from 1.0 wt% to 4.0 wt%, the ultimate steady melting rate increases from −8.85 × 10 −5 to −3.23 × 10 −4 m/s, the ultimate steady heat transfer coefficient first increases and subsequently decreases, is in the range of 7.59 × 10 4 to 1.01 × 10 5 W/m 2 /K. Furthermore, the ultimate steady mass transfer coefficient and the ultimate steady interface carbon content gradually decreases from 1.40 × 10 −4 to 5.21 × 10 −5 m/s and from 0.73 to 0.69 wt%, respectively. With the increase of preheating temperature and characteristic length of steel scrap, the ultimate steady melting rate, heat transfer coefficient between steel scrap and melt, mass transfer coefficient and interface carbon concentration remain almost constant, about −8.85 × 10 −5 m/s, 8.40 × 10 4 W/m 2 /K, 1.40 × 10 −4 m/s and 0.70 wt%, respectively.

70 Years of LD-Steelmaking—Quo Vadis?

Basic Oxygen Furnace (BOF) steelmaking is, worldwide, the most frequently applied process. According to the world steel organization statistical report, 2021, it saw a total production share of 73.2%, or 1371.2 million tons per year of the world steel production in 2020. The rest is produced in Electric Arc Furnace (EAF)-based steel mills (26.3%), and only a very few open-hearth and induction furnace-based steel mills. The BOF technology remains the leading technology applied based on its undoubted advantages in productivity and liquid steel composition control. The BOF technology started as the LD process 70 years ago, with the first heat applied in November 1952 in a steel mill in Linz, Austria. The name LD was formed from the first letters of the two sites with the first industrial scale plants, Linz and Donawitz, both in Austria. The history and development of the process have been honored in multiple anniversary publications over the last few decades. Nevertheless, the focus of the steel industry worldwide is significantly changing following a social and political trend and the requirement for fossil-free energy generation and industrial production to be in accordance with the world climate targets committed to in relation to the decades leading up to 2050. Iron and steel production is one of the major polluters of climate changing greenhouse gases; it must change to renewable primary energy sources and the use of climate-neutral reduction agents. Because it is very obvious that carbon, as the main component for steel strength properties, cannot be eliminated totally from the steel production process, the question arises of where a “zero carbon” approach can lead? This paper will review the ongoing success story of the LD-process, discuss the recent technology advancements, and give an outlook on the future role of the process in the steel industry.

Green LD (BOF) Steelmaking—Reduced CO2 Emissions via Increased Scrap Rate

The basic oxygen furnace (BOF) is the dominating primary steelmaking process. It is an autothermal process where hot metal and scrap are used as charging materials. The decarbonization and transformation of integrated BOF steelmaking will be the most important challenge in the coming years. Steel scrap is a charge material without new CO2 emissions, whose availability is expected to grow significantly and will play a key role in this decarbonization process. Several solutions have been developed by Primetals Technologies to provide additional energy for processing higher scrap rates in integrated BOF steelmaking. Such solutions include simple upgrade packages installed on existing converters such as process models for heat optimization, post-combustion, and scrap preheating lances. For higher scrap rates from 30% to 50%, a combination blowing converter and JET converter is required to provide sufficient mixing during scrap melting and the highest heat transfer from the increased post-combustion. Hybrid EAF–BOF operation and limitations regarding scrap quality also need to be considered for the transformation of steelmaking. Scrap sorting and processing can be a solution to reduce residual levels in crude steel for high scrap rates. Based on reference plant data, the CO2 reduction potential of the presented solution versus the effort and complexity of implementation is compared.

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.

Estimation of phosphorus distribution ratio at the end of blowing in BOF

In integrated steel plants, the removal of phosphorous normally takes place during the primary basic oxygen furnace (BOF) steelmaking process. Phosphorous is usually introduced to the integrated steelmaking process through blast furnace additions, such as iron ore, coke, sinter, and fluxes. Among the others parameters such as optimizing the charging system, oxygen supply system, oxygen lance parameters of the converter, the flux quality in combination with temperature process control can improve the BOF efficiency of Dephosphorization. Phosphorus partition ratio (LP) is usually used to evaluate the thermodynamic efficiency of the dephosphorization of slags with different compositions in steelmaking processes. However, this parameter is only useful in equilibrium conditions, and it is not accurate when used to evaluate slag efficiency in industrial processes. Because of this, the aim of this work was to study the phosphorus partition ratio estimated from the experimental results in real plant conditions of two different BOF steel plants and compare them with well-known published models. In the present study, data from two steel plants (further Plant A and Plant B) were evaluated applying Healy’s, Suito and Inoui’s, Zhang’s as well as Assis’s equations. The calculated values were compared against measured values.

Optimal control of the blowing mode parameters during basic oxygen furnace steelmaking process

The oxygen converter is intended for production of steel from liquid cast iron and steel scrap at blowing by oxygen. Nowadays, Basic Oxygen Furnace process is the main method for steelmaking. The main disadvantage of the basic oxygen furnace is the limited ability to increase the part of scrap metal. The task of the proposed approach is to control of the blowing mode parameters to establish the optimal level of CO2 that will ensure a minimum specific cost of steel in the presence of restrictions and boundary conditions of basic oxygen furnace steelmaking process. A model predictive control taking into account the constraints on the input signals and the quadratic functional is proposed. The design of Model Predictive Control is based on mathematical model of an object. This approach minimizes the cost function that characterizes the quality of the process. The result of the automatic control system modeling shows that the Model Predictive Control approach provides retention of carbon dioxide level when oxygen consumption is changing. The obtained quadratic functional is optimized to find the optimal control of blowing parameters.

Multichannel profile-based monitoring method and its application in the basic oxygen furnace steelmaking process

Many industrial processes are equipped with a large number of sensors, which usually generate multichannel high-dimensional profiles that can be used to monitor the health condition and detect anomalies of the processes. However, the data irregularity, information obscurity, complex correlations, and nonlinear structures of the multichannel data pose significant challenges for the development of anomaly detection methodologies. To address these challenges, this article proposes a method, Mahalanobis Distance-based Functional Derivative Support Vector Data Description (MD-FDSVDD), for the process monitoring of applications with multichannel profiles. The proposed method first estimates a smooth function of each profile from its irregularly acquired observations and then takes its derivative function to enhance the characteristics associated with anomalies. Next, the smoothed derivative functions are transformed based on Mahalanobis distance to address the strong linear correlation challenge. Finally, the transformed derivative data are used to construct a functional SVDD model to detect anomalies. The effectiveness of the proposed method is evaluated using a simulated dataset and a real-world dataset from a Basic Oxygen Furnace steelmaking process.

End-point prediction of 260 tons basic oxygen furnace (BOF) steelmaking based on WNPSVR and WOA

Basic oxygen furnace (BOF) steelmaking plays an important role in steelmaking process. Hence, it is necessary to study BOF steelmaking modeling. In this paper, a novel regression algorithm is proposed by using nonparallel support vector regression with weight information (WNPSVR) for the end-point prediction of BOF steelmaking. The weight information is excavated by K-nearest neighbors (KNNs) algorithm. Since the whale optimization algorithm (WOA) has the characteristics of fast convergence speed and a few adjustment parameters, WOA is applied to optimize the parameters in the objective function of WNPSVR. Compared with traditional prediction models, WNPSVR-WOA is not easy to fall into local minimum values and is insensitive to noise. Thus, the prediction and control of molten steel end-point information are more accurate. Experimental results verify the effectiveness and feasibility of the proposed model. Within different error bounds (0.005 wt.% for carbon content model and 10°C for temperature model), the hit rates of carbon content and temperature are 89% and 95%, respectively. Meanwhile, a double hit rate of 85% is achieved. The above results conclude that our WNPSVR-WOA has important reference value for actual BOF application and can improve the steel product quality. Moreover, WNPSVR-WOA can also be used to other fields.

Just-in-time-learning based prediction model of BOF endpoint carbon content and temperature via vMF mixture model and weighted extreme learning machine

Basic oxygen furnace (BOF) steelmaking is a complicated physical chemical process, in which the endpoint carbon content and temperature are two important indicators. In BOF steelmaking, the quality of raw materials varies greatly between different batches, which would lead to the inaccurate predictions for these two indicators. Additionally, there are imbalance problems in production process data of BOF steelmaking. For the time-varying problem, a novel similarity criterion based on von-Mises Fisher mixture model (VMM) is proposed in this paper and applied for sample selection of just-in-time-learning (JITL)-based endpoint carbon content and temperature prediction model. The V-shaped transfer function is utilized to develop weighted extreme learning machine (WELM) as local regression model to address the imbalance problems. The performance of the proposed methods is compared with other methods under JITL framework. The experimental results show that the proposed online model can provide a more accurate prediction.

Industrial Application of Bottom–Blown CO2 in Basic Oxygen Furnace Steelmaking Process

A significant amount of CO2 is emitted and utilized in the production processes engaged by iron and steel enterprises. Herein, the feasibility and principles of CO2 participation in the steelmaking process are analyzed, and an industrial examination of bottom‐blown CO2 smelting in a 120 t converter is performed; this serves to summarize and verify the metallurgical advantages of CO2 participation in the converter steelmaking process. The injection of CO2 improves the effects of dephosphorization and denitrification, reduces the end‐point oxygen content of molten steel, diminishes the consumption of the slagging agent, and contributes to a reduction in production costs and improvement in product quality. This study analyzes two different recycling paths of CO2 in iron and steel enterprises, providing a new mechanism for CO2 emission reduction.

Observation of the reactions between iron ore and metallurgical fluxes for the alternative ironmaking HIsarna process

ABSTRACT This work investigates the melting behaviour of iron ore with calcium-based fluxes, including lime, limestone and basic oxygen furnace (BOF) steelmaking slag. With an aim to explore the potential kinetic benefits that can be obtained through the formation of a liquid medium on the overall productivity of HIsarna. A HT-CLSM was used to rapidly heat and observation the interface between a given pair of chosen materials in-situ. Through this method, the rate at which the reaction progressed and the possible disruptive phenomenon that may occur was observed directly. The molten interface of fluxed materials is compared against each other offering insight into relative dissolution rate. Each flux shows promise of increasing ore fluidity to a varying degree with limestone generating above a 60% liquid fraction in the 1400°C reaction temperature.

Physicochemical Phenomena of CO2 Injection in Polymetallic Ironmaking

Abandoned basic oxygen furnace gas (ABOFG) is a kind of byproduct fuel resource produced in the basic oxygen furnace (BOF) steelmaking process with a high temperature of 1773–1873 K and 20–40% (vol.%) CO and 20–30% (vol.%) CO2 and is usually ignited and discharged as waste gas (34.7% abandoned rate). The effects and physicochemical phenomena of injecting CO2‐containing ABOFG into high‐Cr‐bearing vanadia−titania magnetite ironmaking are investigated. The thermodynamic calculation shows that the ABOFG injection changes the gas partial pressure and the oxygen potential in the slag−coke interfaces and suppresses the titanium carbonitride formation. According to the results of softening−melting−dripping experiments, the content of titanium carbonitride in the high‐titanium slag decreases from 2.53% to 1.14% as the CO2 content in ABOFG increases from 0 to 30%, which is conducive to improving the high‐temperature physicochemical properties of molten slag. The recoveries of vanadium and chromium are also improved with the decrease in iron loss. Moreover, the residual coke still maintains a considerable compressive strength with a slight effect from ABOFG injection, which is expected to reduce carbon consumption.

Black box operation optimization of basic oxygen furnace steelmaking process with derivative free optimization algorithm

Mastering the smelting status and making the material adding strategy are important and challenging issue in the basic oxygen furnace (BOF) steelmaking process. Unfortunately, complex physicochemical reaction with multi-input and multi-output makes the first-principles models unusable. To this end, a black box operation optimization (BBOO) problem integrated black box data analytics modeling and optimization is abstracted from the BOF steelmaking process. Moreover, a derivative free optimization (DFO) method based on the trust region framework is developed to solve the operation optimization problem. In the proposed algorithm, we integrate quadratic interpolation and sparse modeling to construct the surrogate model. Wherein, l0-norm optimization, which is solved through converting it into a convex optimization problem with introducing the slack variables, is used to determine the sparse surrogate model. Finally, we apply the proposed DFO algorithm and other DFO algorithms to solve the BBOO problem in BOF steelmaking process. The results demonstrate that the proposed method can be an alternative for providing solutions and insights of material addition in BOF process and the proposed algorithm is comparable.

Effect of blowing parameters on bath mixing efficiency during basic oxygen furnace steelmaking process

AbstractBasic Oxygen Furnace (BOF) steelmaking is the main method for steelmaking production, mixing efficiency of BOF vessel would greatly affect the uniformity of chemical composition and temperature in molten steel. Therefore, this paper conducted the effect of blowing parameters on bath mixing efficiency during BOF steelmaking process. Through measuring the mixing time of steel bath in BOF vessel, the effect of different blowing parameters was studied. Based on analysis of variance for the experimental results, the influence degree on mixing efficiency by different blowing parameters was acquired, and the optimized combination of blowing parameters was obtained. Moreover, the mixing index which indicated the mixing degree of steel bath in BOF vessel was proposed in this study, and an apparent nonlinear relation was acquired after determining relationship between the mixing index and blowing parameters. The optimal blowing parameters were applied to an 80 t industrial BOF vessel in a special steel plant, the end point carbon and oxygen equilibrium was decreased from 0.0032 to 0.0026, end point phosphorus and total Fe content in slag were reduced by 0.0012% and 2.21%, respectively. The mixing index and results presented in this work could be helpful to improve mixing efficiency in commercial BOF vessels.

Material flow analysis of zinc during the manufacturing process in integrated steel mills in China

The steel manufacturing process involves not only the substance flow of iron from iron‐bearing materials to steel products but also the circulation and movement of many additional elements. In this study, the flow and circulation of zinc during coking, sintering, blast furnace (BF) ironmaking, basic oxygen furnace (BOF) steelmaking, and continuous casting processes, which has been neglected, was analyzed in an integrated iron and steel mill throughout 2013. The results indicated that approximately 2,317 tonnes (t) of zinc from steelmaking raw materials were input into the studied steel mill in 2013, of which 58.8% originated from iron ore fines, 22.3% from pellets, and 8.8% from lumps. Although 1,212 t of zinc was discharged along with coking chemical products and steel slag after magnetic separation or iron‐bearing dust and sludge, 1,105 t of zinc remained within the circulation. According to this research, we inferred that approximately 142 kilotonnes of zinc, 2.7% of China's zinc production in 2013, and approximately 1.5 million tonnes in total from 2000 to 2017 were concentrated in BF and BOF dust or sludge. This large amount of zinc has been ignored by Chinese steelmakers and zinc producers, although it should be used as a resource for zinc recycling. Thus, integrated steel mills should develop pertinent treatments targeting these fractions to prevent and handle the hazards from the circulation and concentration of zinc inside their premises.

Comparative study of MCe0.75Zr0.25Oy (M = Cu, Mn, Fe) catalysts for selective reduction of NO by CO: Activity and reaction pathways

Basic oxygen furnace steelmaking leads to the production of CO-rich off-gas. When CO and NO are combined in off-gas, selective catalytic reduction by CO (CO-SCR) effectively achieves the synergistic removal of both pollutants. In this paper, CuCe0.75Zr0.25Oy, MnCe0.75Zr0.25Oy, and FeCe0.75Zr0.25Oy catalysts are prepared and evaluated for their CO-SCR activity, and the results show that the reaction system needs to be anaerobic; thus, the CO-SCR reaction can be dominant. The T90 values of CuCe0.75Zr0.25Oy and FeCe0.75Zr0.25Oy are 200 °C and 223 °C, respectively. The activities of these two catalysts are higher than that of MnCe0.75Zr0.25Oy (T90 = 375 °C). Linear nitrate and bridged bidentate nitrate are the main intermediate species involved in NO conversion on the catalyst surface, and bidentate CO32− coordination is the main intermediate species involved in CO conversion on the catalyst surface. CuCe0.75Zr0.25Oy has high lattice oxygen mobility and is more likely to react with NO and CO. In the presence of oxygen, most CO is oxidized by O2, which increases continuously to 100%, 100%, and 98% for CuCe0.75Zr0.25Oy, FeCe0.75Zr0.25Oy, and MnCe0.75Zr0.25Oy, respectively; additionally, CO is oxidized by O2, and the CO-SCR reaction cannot be carried out.