Temperature Of Molten Steel Research Articles

Numerical Simulation on Multiple Physical Fields Behaviors in Billet Continuous Casting with Different Stirrer Positions

A 3D coupled model considering electromagnetic field, flow field, heat transfer, and particle transport is developed to predict the effect of stirrer position on the magnetic field distribution, fluid flow streamlines, temperature distribution, and inclusion removal in 180 mm × 220 mm billet continuous casting process, and the effect of stirrer position on the mold‐level fluctuation and slag entrapment behavior is also studied based on the homogeneous model. The casting temperature of molten steel is 1750 K, and the casting speed of billet is 0.9 m min−1. The results indicate that as the stirrer center position is lowered, the maximum value of the magnetic induction intensity has almost no change, whereas the maximum value of the tangential electromagnetic force initially decreases followed by an increase. With the downward movement of stirrer center position, the decrease rate of central molten steel velocity slows down, and the range of the upper circulation flow zone increases. A lower stirrer center position increases the cooling of the billet and inclusion removal. When the stirrer center position is moved from 515 to 815 mm, the wave height of steel/slag interface decreases from 11.6 to 3.4 mm, and slag entrapment behavior gradually weakens.

Predicting molten steel endpoint temperature using a feature-weighted model optimized by mutual learning cuckoo search

A feature-weighted neural network model for the prediction of the endpoint temperature of molten steel (MSET) in a ladle furnace (LF) is proposed in this paper. Accurate prediction of MSET is essential for promoting product quality, reducing production costs and enhancing productivity. Considering that different features have different impacts on the MSET during the process of LF refining, a weight is applied to each feature before feeding the feature to neural networks. A mutual learning cuckoo search (MLCS) algorithm is proposed to simultaneously determine the feature weights and network parameters of the proposed prediction model. The search of each cuckoo in the basic cuckoo search algorithm and many of its variants is performed independently, which may decrease the algorithms’ performance. The proposed MLCS algorithm introduces two new search strategies, the mutual learning-based search strategy and the bottom reinforcement learning-based search strategy. The superior performance of MLCS is first confirmed with 20 benchmark optimization problems. Then, MLCS is applied to optimize the feature weights and network parameters in the feature-weighted MSET prediction model. Application to modeling the production data from a 300 t LF in an iron & steel plant in China demonstrates the effectiveness of the proposed feature-weighted neural network model.

Case-based reasoning model based on attribute weights optimized by genetic algorithm for predicting end temperature of molten steel in RH

Temperature control is the key of Ruhrstahl-Heraeus (RH) process in steelmaking plant. The accuracy of RH control model greatly affects the molten steel temperature fluctuation. To obtain RH control model with higher accuracy, an improved case-based reasoning (CBR) model based on attribute weights optimized by genetic algorithm (GA) was proposed. The fitness function in GA was determined according to the prediction accuracy of end temperature of molten steel in RH; then, GA is used to optimize all the attribute weights based on known case base. An improved CBR model that contains the optimized attribute weights was applied to predict end temperature of molten steel in RH, and the prediction accuracy was calculated. Four methods, CBR based on attribute weights optimized by GA (CBR–GA), ordinary CBR, back propagation neural network (BPNN) and multiple linear regression (MLR) method were employed for comparison. The results show that in the error range of [− 3 °C, 3 °C], [− 5 °C, 5 °C], [− 7 °C, 7 °C] and [− 10 °C, 10 °C], the prediction accuracy of CBR–GA was improved by 19.99%, 28.19%, 27.11% and 16.3%, respectively, than that of MLR. Compared with BPNN, the prediction accuracy increased by 3.22%, 7.44%, 5.29% and 2.40%, respectively. Compared with ordinary CBR, the accuracy increased by 5.43%, 5.80%, 4.66% and 2.27%, respectively.

Flow Characteristics for Two-Strand Tundish in Continuous Slab Casting Using PIV

With the development of continuous casting technology, there has been an increase in the stringent requirements for the cleanliness and quality of steel being produced. The flow state of molten steel in tundish is the key to: Optimizing the residence time of molten steel in the tundish; homogenizing the temperature of molten steel; and removing inclusions by floatation. Hence, from theoretical and practical aspects, it is imperative to examine and analyze the flow field of molten steel in the tundish in order to ensure the desired molten steel flow. In this study, a two-strand tundish with 650 mm × 180 mm slab casting is considered as the subject for this research. According to the similarity theory, combined with the geometrical shape and dimension of the prototype tundish, a tundish model with a geometric similarity ratio of 2:3 is established in the laboratory. Digital particle image velocimetry (PIV) is employed to measure and examine the flow fields at different casting speeds for a tundish containing different flow control devices. The flow in the tundish is typically turbulent and also consists of a vortex motion; it exhibits both random and ordered characteristics. Results reveal that the presence of baffles with 15° holes can cause an upward-directed flow in the outlet section and give rise to a large circulation. When the casting speed is doubled, the overall velocity of the flow field and turbulent intensity increase, leading to an increase in the molten steel surface velocity.

Research and Development of Vacuum - Lined Ladle

Due to defect of poor heat preservation property, the common steel ladles can’t pour numbers of boxes by one bag for special steel, such as high manganese steel which is sensitive to casting temperature, casting grain size and mechanical properties. The common steel ladles cooling fast often cause cold ladle and misrun. For better insulation, a vacuum insulation layer was added to the ladle to maintain the temperature of the molten steel, reduce the heat absorption of molten steel by ladle and reduce heat exchange with the outside world. The test results showed that the vacuum-lined steel ladle had a desired insulation performance which was much better than ordinary ladle. It improved the properties of castings and also had distinct energy - saving effect. Its good insulation performance brought a series of advantages, such as heating up fast during preheating of ladle, pouring temperature of molten steel could be further reduced, refining the casting grain size, improving the mechanical properties of the casting and wear resistance. Good insulation performance also could extend pouring time. At the same time, the low temperature ladle wall brought good working environment to foundry workers.

Study on the Reblow Model for Medium-High Carbon Steel Melting by Converter

AbstractThe study on the behaviour of decarburization, temperature increase, change of phosphorus and oxygen content of liquid steel during reblow process was presented in this paper. On the basis of traditional three-stage decarburization theory and thermal equilibrium theory, models for the variation of carbon content and temperature were established. Moreover, with application of data mining method, models for change of phosphorus and oxygen content were built based on large amount of production data and thermodynamic theory. By considering the variation of composition and temperature of molten steel, the reblow model was obtained and the software for reblow operation was developed with the combination of production technology. The test results indicated that the reblow rate (reblow more than once) dropped from 3.99 % to 0, and the average tap to tap time was reduced from 37.43 min to 36.35 min. It could provide an effective solution for medium-high carbon steel melting.

The research on intelligent extraction of furnace mouth flame characteristics based on DNN

Deep neural networks are a focus of artificial intelligence and big data analysis in recent years. The monitor of the converter mouth is essential to the quality of the steel material production while the requirement of the steel material production is increasingly higher in China. The end-point control of converter blowing is the ultimate regulation of the carbon content and temperature. The severity of carbon-oxygen reaction and the temperature of molten steel can be reflected by the converter mouth flame. Operators judge the end of the steel by watching the converter mouth flame, the converter mouth spark and the time of oxygen supply. So, it is very important to offer a quantitative analysis to converter mouth flame characteristics. We quote the deep neural network into the intelligent extraction of the flame characteristics of the furnace mouth and construct a flame color recognition algorithm based on the deepness letter neural network. This paper belongs to the data science problem in the intelligent research of steel production. By observing the converter flame during the steel flame changes, this paper records the data of light intensity and end-point carbon content of each steel making furnace. When this paper then uses the temperature of flame emission spectrum to deduce and the absorption of the molten steel to judge the contents of the carbon during the converter steel blew process, it is more feasible and accurate than watching by operators. At the same time, by using deep learning algorithm, this paper makes the control process get automatic learning ability and achieve intelligent production so that we can provide a basis for solving the problem of predicting the end-point carbon content in molten steel during the blowing process.

A New Data Preprocessing Technique Based on Feature Extraction and Clustering for Complex Discrete Temperature Data

LF(LADLE FURNACE) refining technology is the key process to regulate the temperature in steelmaking process. To predict the end temperature of molten steel in LF, this paper proposes a new data preprocessing technique based on feature extraction and clustering. Firstly, random forest algorithm was used to predict the temperature, the predictive hit rate of error within ± 10°C was 73.18%. The Lasso algorithm and K-means algorithm was used for feature extraction and clustering. After improvement, the prediction accuracy of the LF end temperature of error within ± 10°C was about 88.16%. The results show that this improvement has high prediction accuracy in the prediction about the end temperature of molten steel in LF refining.

Numerical analysis for the heat transfer behavior of steel ladle as the thermoelectric waste-heat source

Heat transfer behavior of a steel ladle filled with molten steel is comprehensively analyzed for subsequent design of the matched thermoelectric devices. Considering the influence of the flow and thermal stratification of molten steel on heat dissipation, a mathematical model that describes both the inner transient flow and heat transfer of molten steel and the outer heat dissipation of ladle wall and slag is established. An innovative two-step method is applied to make the simulation accord with actual industrial process. The results show the non-uniform temperature and natural convection of molten steel has a significant effect on the heat transfer of wall and slag but a weak effect on temperature variation and heat dissipation of the surfaces. The steel ladle can be considered as a steady heat source. Moreover, the heat loss of sidewall is the main path of ladle’s and accounts for more than half of the total. Besides, radiation is the main form of heat dissipation. The surface temperature distribution in the vertical direction of the sidewall has a large difference. However, the region of sidewall surface under slag line is with a high and relatively uniform temperature and is conducive to the stability of thermoelectric conversion. Finally, a primary solution of waste-heat utilization of steel ladle is proposed.

Dephosphorization stability of hot metal by double slag operation in basic oxygen furnace

Double slag process was adopted to produce low-phosphorus steel from middle-phosphorus hot metal. To achieve a stable dephosphorization operation, conventional process was modified as follows: the blowing time was extended by approximately 1 min by reducing the oxygen supply flow rate; calcium ferrite pellets were added to adjust the slag composition and viscosity; the dumping temperature was lowered by 30–50 °C by the addition of calcium ferrite pellets during the double slag process to prevent phosphorus in the slag from returning to the molten steel; and the bottom-blown gas flow was increased during the blowing process. For 40 heats of comparative experiments, the rate of dephosphorization reached 91% and ranged between 87% and 95%; the phosphorus, sulfur, manganese, and oxygen contents calculated according to the compositions of molten steel and slag as well as the temperature of molten steel at the end-point of the basic oxygen furnace process were similar to the equilibrium values for the reaction between the slag and the steel. Less free calcium oxide and metallic iron were present in the final slag, and the surface of the slag mineral phase was smooth, clear, and well developed, which showed that the slag exhibited better melting effects than that produced using the conventional slag process. A steady phosphorus capacity in the slag and stable dephosphorization effects were achieved.

Thermal analysis of walls in a new-type billet caster tundish with a vacuum shell

To reduce thermal loss from molten steel in a tundish during continuous casting production, a new tundish fabricated by welding radiation-proof steel plates onto the steel plates of the exterior walls of a billet caster tundish was proposed. This new tundish was used to investigate the effect of pressures inside the vacuum chamber on the uniformity of the temperature of molten steel and the thermal conditions of the vacuum layer. The results show that the conversion radiation coefficient is not sensitive to pressure and its value at high temperatures is merely 1.5 times greater than that at low temperatures. Pressure is the key factor affecting additional factor of conversion convection. This factor is more than 100 times greater at 105 Pa than at 102 Pa, and the temperature at inner points at 102 Pa is, on average, 4 K higher than that at 105 Pa. Meanwhile, the local temperature difference of the inlet at 102 Pa is 1 K higher than that at 105 Pa. Thus, the proposed vacuum billet caster tundish can achieve low superheat teeming of steel because of the thermal preservation capability of the vacuum, which helps to reduce the tapping temperature and improve the uniformity of the temperature of steel.

Design and analysis on fume exhaust system of blackbody cavity sensor for continuously measuring molten steel temperature

Fume exhaust system is the main component of the novel blackbody cavity sensor with a single layer tube, which removes the fume by gas flow along the exhaust pipe to keep the light path clean. However, the gas flow may break the conditions of blackbody cavity and results in the poor measurement accuracy. In this paper, we analyzed the influence of the gas flow on the temperature distribution of the measuring cavity, and then calculated the integrated effective emissivity of the non-isothermal cavity based on Monte-Carlo method, accordingly evaluated the sensor measurement accuracy, finally obtained the maximum allowable flow rate for various length of the exhaust pipe to meet the measurement accuracy. These results will help optimize the novel blackbody cavity sensor design and use it better for measuring the temperature of molten steel.

Review of Our Investigations on Ladle Furnace

The LD-2 and slab caster shop of Tata Steel, Jamshedpur manufactures slabs. Tata Steel desired some studies on the ladle furnace (LF) of the shop. It sponsored project to Tata Research Development and Design Centre (TRDDC), Pune. It was undertaken as a collaborative work by the two organizations with team of engineers and scientists participating. The present author was then at IIT Kanpur and acted as advisor. The study was concerned with plain carbon aluminum-killed steel. The entire work was undertaken in three phases. Phase 1 involved flow and thermal modeling. Phase 2 was concerned with development of on-line reckoner (LFOR) for prediction and control of temperature and composition of molten steel in ladle furnace. Phase 3 was a short project concerned with modification of LFOR, when Tata Steel adopted Al-wire feeding at the OLP station. The present paper is a brief review of the entire work.

New insight into investigation of thermal transfer of molten steel inside a ladle with vacuum shell

In order to reduce the amount of thermal loss of steel inside a ladle during the industrial production, one new type ladle constructed using radiation-proof steel plates welded on the exterior walls of conventional ladle and its lid was investigated. Thermal insulation principle of vacuum is applied to study the mechanism of thermal transfer, and a novel approach is proposed for calculation of comprehensive thermal conductivity inside the vacuum chamber. The main idea of this paper was to explore the effects of pressures inside vacuum chamber on the temperature of molten steel. The conclusions were made. When the absolute pressure inside the chamber of the ladle is set to 50 Pa, as with its lid, the temperature drop of molten steel is a mere 9 K; while the chamber of the ladle is set to 105 Pa, as with its lid, the temperature drop is 37 K and heat flux occupies the minimum proportion value, 15.38 %. Thermal conduction ability of air molecule is almost not affected by pressure. The additional conversion convection factor value at 104 Pa is 200 times larger than that at 50 Pa. The ability of comprehensive thermal conduction is poor due to low pressures and small amount of air molecules. Thermal conductivity of refractory layer is 11 times larger than that of vacuum layer at 50 Pa. Thus, vacuum layer has strong thermal insulation at lower vacuum pressures.

Spectral and total effective emissivity of a nonisothermal blackbody cavity formed by two coaxial tubes

A blackbody cavity is developed for continuously measuring the temperature of molten steel, which consists of a cylindrical outer tube with a flat bottom, a coaxial inner tube, and an aperture diaphragm. The ray-tracing approach based on the Monte Carlo method was applied to calculate the effective emissivity for the nonisothermal cavity with a diffuse wall. The influence of the wall temperature distribution on the spectral and total effective emissivity was studied for various cavity geometrical parameters. For a practical blackbody cavity of the temperature sensor used in the tundish, the average spectral effective emissivity was calculated and verified. Our results will help optimize the novel sensor design and use it better for measuring the temperature of molten steel.

End Temperature Prediction of Molten Steel in LF based on CBR–BBN

To improve the control level about the end temperature of molten steel in ladle furnace (LF) refining, a combined method of case‐based reasoning (CBR) and Bayesian belief network (BBN) has been proposed to predict the end temperature of molten steel in LF. The evaluation of the reliability of cases is conducted by applying BBN for the assessment which the CBR method lacks in. A BBN is established based on the actual production process of LF refining. Statistical interval is then determined for each node, and finally the probability of each case in the case base is computed to evaluate the reliability. On such basis, the case retrieval algorithm in CBR is revised using the reliability of cases and the prediction accuracy of the revised algorithm is compared with ordinary CBR and back propagation neural network (BPNN). The results show that CBR–BBN has higher prediction accuracy than ordinary CBR and BPNN in the prediction about the end temperature of molten steel in LF refining.

Effect of Parapet Hole on Tundish Flow Field

According to the actual operating parameters of tundish in a steel mill, the flow pattern and temperature distribution of molten steel in tundish were studied by CFD and heat transfer calculation method. Changing the height of parapet hole, the effect of parapet hole on flow field and temperature field were simulated. The simulation results show that: the parapet hole height has large influence on flow recirculation position, which can impact surface temperature and outflow temperature of molten steel. When parapet hole height increases, the recirculation vortex moves towards the surface of molten steel in outflow zone, surface temperature rises, but outflow temperature decreases near the narrow wall. Comprehensive analysis show that, when the parapet hole height is 480mm, surface temperature is high, molten steel will not solidify at corner; the reduction of molten steel temperature is small at outflow hole.

Effect of Turbulence Inhibitor on the Molten Steel Flow in Tundish

The flow pattern and temperature distribution of molten steel in tundish were calculated by CFD and heat transfer method. Comparing the tundish flow field and the temperature field of empty packet and turbulence inhibitor installed, the role of turbulence inhibitor in tundish was analyzed, and the advantages and disadvantages of turbulence inhibiter were illuminated. The results show that: the turbulence inhibitor can eliminate the short circuit flow at tundish bottom, stabilize flow, and homogenize molten steel composition; but the turbulence inhibitor can consume energy of molten steel, and reduce the temperature of molten steel in the tundish. Without the turbulence inhibitor, the surface minimum temperature is far from the narrow corner, it is not easy to knot cold steel; installing turbulence inhibitor can raise the molten steel temperature near inlet, steel flow will scour inlet refractory intensively, service life of which shortens.

Effective emissivity of a blackbody cavity formed by two coaxial tubes.

A blackbody cavity is developed for continuously measuring the temperature of molten steel, which consists of a cylindrical outer tube with a flat bottom, a coaxial inner tube, and an aperture diaphragm. The ray-tracing approach based on the Monte Carlo method was applied to calculate the effective emissivity for the isothermal cavity with the diffuse walls. And the dependences of the effective emissivity on the inner tube relative length were calculated for various inner tube radii, outer tube lengths, and wall emissivities. Results indicate that the effective emissivity usually has a maximum corresponding to the inner tube relative length, which can be explained by the impact of the inner tube relative length on the probability of the rays absorbed after two reflections. Thus, these results are helpful to the optimal design of the blackbody cavity.

A Homotopic Algorithm of NN and its Application for the End-Point Prediction Model of BOF Process

A Homotopic BI neural network model is developed by combining the homotopy theory and the BI neural network model, to improve the defects of the steepest gradient descent algorithm itself, such as low speed converging and liable to be trapped in local minimum. The end-point carbon content and temperature of molten steel in BOF smelting process is predicted by the proposed model and the original. Result shows that the precision of new model is improved significantly. The hit rates are increased by about 5% and 10%, and the forecasting residuals have decreased 16.31% and 8.67% than the conventional ones, respectively. Also, the calculation time of the new model is 10% shorter than BI model.