Hot Metal Quality Research Articles

Study of Tuyere Combustion Flame Temperature in Vanadium and Titanium Blast Furnaces by Machine Vision and Colorimetric Thermometry

The steel industry is an important foundation of the national economy and the livelihood of the people, producing a large amount of carbon dioxide gas, accounting for about 70% of the carbon dioxide gas generated in the steel industry, which occurs during the ironmaking process. Therefore, the key technology to reduce the pollution and improve competitiveness is to increase the stability of blast furnace production and the quality of hot metal. Since the operation requirements for temperature control in the vanadium-titanium blast furnace are dramatically different compared to the traditional ones due to the low fluidity of vanadium-titanium slag, maintaining the required hot metal temperature within a narrow range with smaller fluctuations is essential. In addition, the adjustment parameters of the lower part have a significant influence on the tuyere combustion flame temperature during the daily operation of blast furnaces. At present, there is no relevant research on the online detection and analysis of vanadium-titanium blast furnace tuyere combustion flame temperature. In this study, the temperature of four tuyeres in a 500 m3 vanadium and titanium blast furnace at Jianlong Steel was detected by an online detection system. The tuyere combustion flame temperature was then calculated using colorimetric temperature measuring methodology at various times and at four distinct locations. After that, the calibration analyses, imaging parameter and the temperature tendencies in different directions of the blast furnace were investigated. This study not only offers new methods for understanding the regularity of operation and increasing the degree of visualization in vanadium and titanium smelting blast furnaces but also provides technical support for intelligent and low-carbon operation in blast furnaces.

Improved back propagation neural network method for predicting sulfur content in hot metal

Blast furnace smelting is a traditional iron-making process. Its product, hot metal, is an important raw material for the production of steel. Steelmaking efficiency can be improved and steel product quality can be stabilized by using proper hot metal. Sulfur is an important indicator reflecting the quality of hot metal, it is necessary to establish an accurate prediction model to predict the sulfur content of hot metal, to effectively guide the production process. There is a non-linear relationship among the factors influencing the desulfurization effect during the blast furnace smelting process, and the back propagation neural network (BPNN) model has a strong ability to solve nonlinear problems. However, BPNN has the disadvantages of slow convergence speed and easy to fall into local minima. To improve the prediction accuracy, an improved algorithm combining Kmeans and BPNN is proposed in this paper. The study showed that compared with the BPNN model and case-based reasoning (CBR) model, the Kmeans-BPNN model has the lowest RMSE and MAPE values, which indicates a high degree of fit and a low degree of dispersion. The Kmeans-BPNN model has the largest HR value, which indicates the highest prediction accuracy. The proposed Kmeans-BPNN prediction model achieves a hit rate of 96%, which is 4.5% higher than before the improvement. It can effectively improve the prediction accuracy of hot metal sulfur content.

Hot metal quality monitoring system based on big data and machine learning

Hot metal quality monitoring system based on big data and machine learning

A Multiobjective Evolutionary Nonlinear Ensemble Learning With Evolutionary Feature Selection for Silicon Prediction in Blast Furnace.

In the blast furnace ironmaking process, accurate prediction of silicon content in molten iron is of great significance for maintaining stable furnace conditions, improving hot metal quality, and reducing energy consumption. However, most of the current research works employ linear correlation coefficient methods to select input features in modeling, which may not fully take the nonlinear and coupling relationships between features into account. Therefore, this article considers the input feature selection issue of silicon content prediction model from a new perspective and proposes a multiobjective evolutionary nonlinear ensemble learning model with evolutionary feature selection mechanism (MOENE-EFS), in which extreme learning machine is adopted as the base learner. MOENE-EFS takes the input feature scheme of each base learner as well as their network structure and parameters as decision variables and proposes a modified nondominated sorting differential evolution algorithm to optimize two conflicting objectives, i.e., accuracy and diversity of base learners, simultaneously. Through the optimization, a set of Pareto optimal base learners with high accuracy and strong diversity can be obtained. Moreover, different from the linear ensemble methods commonly used in classical evolutionary ensemble learning, this article proposes a nonlinear ensemble method to combine the obtained base learners based on differential evolution. Experimental results indicate that the two proposed strategies, i.e., evolutionary feature selection and nonlinear ensemble, are very effective in improving the accuracy and stability of the prediction model. MOENE-EFS also outperforms the other prediction models in both benchmark data and practical industrial data. Furthermore, analysis on the input features of all Pareto optimal base learners shows that the evolutionary feature selection is capable of selecting essential features and is consistent with human experience, which indicates it is a promising method to deal with the input feature selection issue in silicon content prediction.

Proposing a suitable slag composition by estimating the fusion behavior, viscosity and desulphurization ability for blast furnaces running with high alumina

The present work was focused on identifying a suitable quaternary CaO-MgO-SiO2-Al2O3 high alumina slag for blast furnaces running with high alumina ores as an input raw material. These high alumina slags have inferior properties like high liquidus temperature, high viscosity and poor sulphur absorption capacity. These properties will hamper the working condition of the blast furnace as well as quality of hot metal produced. To overcome these issues, it is necessary to focus on selecting an appropriate slag composition having superior properties like, minimum difference between softening and flow temperature (ST & FT) (<100 °C), low liquidus temperature, good fluidity (i.e. viscosity <0.6 pas. at 1773 K) & high sulphur absorption capacity at 1773 K. In the current work various slag compositions having Al2O3 25–30 %, CaO/SiO2 ratio (0.8–1.6), and MgO (8–16 %) has considered for determining fusion temperature, viscosity and desulphurization ability of slag using high temperature microscope, viscometer and slag-metal Desulphurization experiments. It was found that slag with Al2O3 (30 %) and C/S ratio (1.2) coupled with MgO (12 %) is showing superior properties like low FT-ST (i.e. short slag) having viscosity 0.35 pas and Desulphurization ability of approx. 80 % at 1773 K which fulfills the necessary conditions for better working condition of the blast furnace.

Dual ensemble online modeling for dynamic estimation of hot metal silicon content in blast furnace system

Hot metal silicon content (HMSC) is usually utilized to measure the quality of hot metal and reflect the thermal status of blast furnace (BF) system. However, most state-of-the-arts ignore the time-varying behavior of BF ironmaking process, which are impractical. Accordingly, a novel dual ensemble online sequential extreme learning machine (DE-OS-ELM) is proposed to establish the online estimation model of HMSC, which can update the data-driven model with the latest operation data. Specifically, an online learning method with recursive modification is first proposed based on OS-ELM (referred to as RM-OS-ELM) to address the modeling with uncertainty. To heel, a dynamic forgetting factor is presented for the dynamic tracking capability enhancement and convergence acceleration. Furthermore, a final updating rule for sequential implementation is constructed by combining the output weights of OS-ELM and RM-OS-ELM based on their corresponding contributions on modeling. Considering the modeling accuracy and curve trend consistency, multiobjective parameter optimization model is also implemented to achieve the satisfactory performance. By taking the proposed DE-OS-ELM, the estimation model of HMSC is established using industrial data. Comprehensive experiments demonstrate that DE-OS-ELM-based HMSC estimation model is more feasible and practical.

Smelting Reduction Characteristics and Mechanism of Vanadium–Titanium Magnetite Carbon-Bearing Pellet

Vanadium–titanium magnetite is one kind of important strategic resources. However, there exist some common problems in the current smelting process of vanadium–titanium magnetite. In the present paper, we propose the rotary kiln pre-reduction and bath smelting process reasonably for smelting vanadium–titanium magnetite. The smelting reduction test of vanadium–titanium magnetite carbon-bearing pellet in the bath was conducted under laboratory condition, and the effect of end slag basicity, the smelting temperature, the smelting time and the amount of pulverized coal on the separation of slag–iron and the quality of hot metal were mainly investigated. The results showed that the reasonable smelting parameters for vanadium–titanium magnetite smelting of the process introduced in the paper were above 1450 °C of smelting temperature, above 0.8 of basicity of slag, 30–40 min of smelting time and 20–22% of coal addition. Nevertheless, the sulfur content of the iron particle was higher than the demand of steelmaking and it needed further desulfurization. The process is economically feasible for smelting vanadium–titanium magnetite, which can recover the vanadium and titanium resources effectively.

Investigation of Molten Liquids Flow in the Blast Furnace Lower Zone: Numerical Modelling of Molten Slag Through Channels in a Packed Bed

Slag flow behaviour is critically important in the lower zone of the ironmaking blast furnace, and is closely related to the selection of charged raw materials, coke bed permeability, process stability and hot metal quality. To better understand the effect of slag properties on flow behaviour in the coke bed, a numerical approach was applied to characterize the slag flow through funnel analogues. These analogues were used to represent molten slag flow through the inter-particle voids of a coke packed bed. A critical funnel neck size, through which no slag flowed was experimentally established and confirmed by numerical modelling. The influence of slag wettability on the occurrence of blockage was also determined via numerical modelling. An increase in either contact angle or surface tension can make the occurrence of blockage easier. For a constant neck size, the relationship between surface tension and contact angle is non-linear. The status of the remaining slag in the funnel corresponding to different slag wettabilities was differentiated in terms of the blockage in the upper part and hanging in the lower part of the funnel. Modelling was also undertaken of slag flow through the inter-particle void between spherical particles to evaluate empirical correlations for predicting the remaining slag in the packed bed. These results show that the numerical approach is very useful in providing some level of guidance to help understand and predict the slag flow behaviour in the blast furnace ironmaking process.

Fundamental Insight into the Dripping Criteria of Slag and Hot Metal in a Coke Bed Using Energy Minimization Model

The dripping zone of the blast furnace is important in determining the productivity and quality of hot metal. The permeability of the bed affects both of these factors. Static holdup of hot metal and slag resulting from interfacial phenomena adversely affect the permeability of the bed. To characterize the holdup in the packed bed, we assumed the packed bed as an ensemble of two-sphere contacts at various contact axis inclinations and for contact angles. We developed mathematical models to determine the shapes of stable films at these contacts taking into account interfacial and gravitational energies. The film at higher contact angles shows an asymmetric shape even in between vertical spheres, which is investigated in detail. We also show the reason for the difficulty for dripping of the liquids at smaller particle sizes.

Data-Driven Bayesian-Based Takagi–Sugeno Fuzzy Modeling for Dynamic Prediction of Hot Metal Silicon Content in Blast Furnace

The main method of modern ironmaking is blast furnace ironmaking, which is a very complex nonlinear dynamic process with complex physical-chemical coupling. The hot metal is the final product of blast furnace, and its silicon content not only reflects the quality of hot metal but also characterizes the operation status of the blast furnace, so its accurate prediction is very important for the operation of the blast furnace. Given the bottleneck problem in the application of the existing prediction model of hot metal silicon content in the blast furnace, this article proposed a novel data-driven modeling method. First, a nonlinear Takagi–Sugeno (T–S) fuzzy model is constructed for the hot metal silicon content to completely capture the nonlinear dynamics of the blast furnace process. Then, considering the doubts of blast furnace operators about the predicted results of the model, the Bayesian method is used to identify the consequent parameters of the fuzzy model to obtain the probability output, to present the credibility of the predicted results. Furthermore, to improve the robustness of the fuzzy model to the initial fuzzy rules, the sparse priori is adopted to construct a compact fuzzy model with strong generalization performance, in which the key fuzzy rules were screened out. In addition, two optimization methods are derived for each of the above models. Finally, the validity of the proposed methods is verified by the test of actual blast furnace data.

Means to cope with the higher alumina burden in the blast furnace

ABSTRACTThe stability of the blast furnace (BF) process depends on the quality of raw materials charged into the BF. Apart from various process variables, slag viscosity and slag fluidity are key parameters that require due attention for the stable BF operation. The varying Al2O3 content in slag increases the viscosity of slag and thus hinders the separation of slag from hot metal during the casting process. This leads to the deterioration of hot metal quality because of the entrapment of slag during the slag–metal interfacial reaction. In this work, an attempt is made to utilize the high alumina content raw materials and to reduce the Al2O3 content in the final slag. The high alumina content in slag increases the liquidus temperature and viscosity of slag and causes an adverse effect on BF operations. To countermeasure the effect of alumina, slag volume is increased and basicity along with MgO was increased. It has been demonstrated that MgO addition as a flux is effective for compensating the effect of a high alumina burden. By increasing the MgO in slag, fluidity was maintained to ensure easy separation of slag from hot metal.

Devolatilization Kinetics of Different Types of Bio-Coals Using Thermogravimetric Analysis

The interest of the steel industry in utilizing bio-coal (pre-treated biomass) as CO2-neutral carbon in iron-making is increasing due to the need to reduce fossil CO2 emission. In order to select a suitable bio-coal to be contained in agglomerates with iron oxide, the current study aims at investigating the thermal devolatilization of different bio-coals. A thermogravimetric analyzer (TGA) equipped with a quadrupole mass spectrometer (QMS) was used to monitor the weight loss and off-gases during non-isothermal tests with bio-coals having different contents of volatile matter. The samples were heated in an inert atmosphere to 1200 °C at three different heating rates: 5, 10, and 15 °C/min. H2, CO, and hydrocarbons that may contribute to the reduction of iron oxide if contained in the self-reducing composite were detected by QMS. To explore the devolatilization behavior for different materials, the thermogravimetric data were evaluated by using the Kissinger– Akahira–Sonuse (KAS) iso-conversional model. The activation energy was determined as a function of the conversion degree. Bio-coals with both low and high volatile content could produce reducing gases that can contribute to the reduction of iron oxide in bio-agglomerates and hot metal quality in the sustained blast furnace process. However, bio-coals containing significant amounts of CaO and K2O enhanced the devolatilization and released the volatiles at lower temperature.

Ensemble pattern trees for predicting hot metal temperature in blast furnace

In steel industry, it is crucial to predict hot metal temperature (HMT), which is strongly related to the product quality and the thermal state, to keep high productivity of the blast furnace. The present work proposes a novel ensemble pattern trees model to predict HMT. Ensemble pattern trees is a robust nonlinear modeling method, which aggregates a set of pattern trees models into a single predictive model via the bagging technique. Ensemble pattern trees overcomes the drawback of single pattern trees which may not be robust enough against the random variations such as process perturbations and noises in the blast furnace. In addition, a novel variable importance measure derived from the ensemble pattern trees is proposed to understand which process variables affect the final hot metal quality. The proposed method was validated through an industrial blast furnace ironmaking process, and the results have demonstrated its superiority to several conventional methods.

Static Thermochemical Model of COREX Melter Gasifier

COREX is one of the commercial smelting reduction processes. It uses the finer size ore and semi-soft coal instead of metallurgical coke to produce hot metal from iron ore. The use of top gas with high calorific value as a by-product export gas makes the process economical and green. The predictive thermochemical model of the COREX process presented here enables rapid computation of process parameters such as (1) required amount of ore, coal, and flux; (2) amount of slag and gas generated; and (3) gas compositions (based on the raw material and desired hot metal quality). The model helps in predicting the variations in process parameters with respect to the (1) degree of metallization and (2) post-combustion ratio for given raw material conditions. In general reduction in coal, flux, and oxygen, the requirement is concomitant with an increase in the degree of metallization and post-combustion ratio. The model reported here has been benchmarked using industrial data obtained from the JSW Steel Plant, India.

Study of the combustion behaviour and temperature of pulverised coal in a tuyere zone of blast furnace

ABSTRACTThe combustion behaviour and temperature of pulverised coal in a tuyere zone of the blast furnace could benefit to adjust the operation parameters, assure the stable state as well as maintaining high quality of hot metal of the blast furnace. However, there have been rare studies about this subject. In this paper, the digital imaging system based on the colour charge coupled device within gas-cooling and dustproof functions was established to capture the combustion radiation in the tuyere zone. For instance, images of tuyere zones of 2000 and 2500 m3 blast furnaces were detected through a peephole in front of the blowpipe by the application of digital imaging and image processing techniques, and the temperature distributions were also calculated. Some measurements were used to improve the accuracy of the results, and the combustion behaviour was also analysed.

Flow Phenomena in the Dripping Zone of Blast Furnace − A Review

Dripping zone plays a crucial role in modern high productivity blast furnaces; it affects production rate, hot metal quality, and process efficiency. This is a four phase region, where gas, solid, liquid, and powder coexist with each other. Solid and liquid flow downward driven by gravity, whereas gas and powder suspended in the gas flow upward due to the pressure force. The flow of four phases in this region is considerably different from that in the upper shaft region due to the geometry and the presence of deadman zone at the center and discrete raceways at the periphery. This paper presents the review of the current status of the understanding of this flow phenomena of different phases namely, granular solids, dripping liquids, powder, and gas. Discussion on the fundamental aspects of the flow of each phase are followed by review of the current status of experimental investigations and modeling efforts. Gaps in understanding and challenges ahead for future investigations are suggested.

A novel method to detect the water leakage from tuyere nose cooling circuit in blast furnace

The water cooled tuyere noses, through which hot blast is blown into the furnace, are exposed to very high temperature region of raceway inside the furnace. As a result the chances of rupture of cooling pipes within the tuyere nose are significantly high. The rupture causes water dripping into the furnace and if it continues and unnoticed for a prolonged period it lowers down the local raceway temperature and thus adversely affects the product hot metal and slag quality. Moreover, with heavy water leakage, there is a danger of explosion; monitoring of the cooling water is therefore essential. Despite the availability of water flow metre at each tuyere it is difficult to identify the leaking tuyere at the early stage unless the rupture size grows bigger and so the leakage. In several cases the furnace is forced to shut down to manually inspect the leakage occurring without prior knowledge of the exact tuyere number. Identification of water leakage at an early stage is therefore necessary to prevent process disturbances due to chilling of the furnace and avoid the unscheduled downtime for tuyere replacement. This paper presents a method to identify the water leakage from tuyere nose cooling circuit in blast furnace and the adverse effect of water leakage on the performance of the blast furnace. A system called water leak detection system is developed for different blast furnaces in Tata Steel Jamshedpur to monitor the water leakage through tuyere nose and identify the exact leaking tuyere based on a dimensionless number called leak detection factor.

Sulphide Capacity of CaO–SiO&lt;sub&gt;2&lt;/sub&gt;–Al&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;3&lt;/sub&gt;–MgO System Relevant to Low MgO Blast Furnace Slags

Desulphurization has been of more importance to control the quality of hot metal in blast furnace (BF) with increasing utilization of low-grade iron ores and poor-quality fuels as well as reducing the slag volume by decreasing MgO concentration. To improve performance with better understanding the desulphurization in BFs, sulphide capacities of CaO-SiO -Al O -MgO system relevant to BF slags were experimentally determined using gas-slag equilibration technique. The results show that sulphide capacity increases with the increase in temperature, CaO/SiO weight ratio and MgO at fixed Al O concentration. The experimental results were further compared with sulphide capacity obtained from available models in open literature. In addition, the equilibrium sulphur partition ratio between slag and hot metal was calculated from sulphide capacity with oxygen activity in the melt, which is also compared with industrial data and the laboratory experiments of kinetic desulphurization of hot metal.

Estimation of ore mineralogy from analytical analysis of iron ore

A pressing issue that has often been the topic of discussion in iron-making operations in India is the high content of alumina in the country’s iron ore deposits. The presence of alumina has hampered production by affecting slag fluidity, and thereby the hot metal quality. Iron ores from Indian mines are generally rich in hematite, with traces or small amounts of goethite/limonite, kaolinite, gibbsite and quartz reported from time to time. In day-to-day production, emphasis is placed on checking the chemical analysis of product samples, but the true solution to the alumina problem lies in estimating the mineralogy of the ore samples. We developed a mathematical model, based on singular value decomposition, to predict mineralogy from classical chemical analysis. We then used X-ray diffraction patterns, scanning electron microscope analysis, and grain counting with an optical microscope to determine actual mineral compositions and to test the values predicted by the model. The results indicate that with regular calibration, the model can be used as a simple, fast and effective tool to predict on a regular basis the mineralogy of multiple samples.

Optimizing Steelmaking System for Quality Steel Mass Production for Sustainable Future of Steel Industry

Demands for the infrastructure and transportation in the world keep increasing with matching production of steels of higher strength and better ductility. They are produced largely with BF‐BOF/EAF‐LF/RH‐CC systems as the main stay. Optimization of the system is mandatory to stably maximize premium yield of steels at higher productivity, while keeping the CO2 emission, waste evolution, raw material/energy consumption, and production cost at their minimum. Problems inherent to the unit processes involved are addressed. Integration of the processes into an optimized system is discussed with emphasis placed on hot metal pretreatment, productivity, and process versus quality issues for the sustainable near future of steel industry.