Steel scrap melting model for a dephosphorisation basic oxygen furnace

Dephosphorisation basic oxygen furnaces (deP-BOFs) greatly differ from conventional BOFs in the melting process, especially its many limits on adding scrap. A mathematical model of the steel scrap melting process was established in MATLAB to investigate the mechanism of scrap melting in deP-BOF in terms of coupling effects of the carbon content of the molten steel, temperature, scrap preheating and converter blowing time on the melting rate and size of the steel scraps. The scrap melting rate was influenced by both the heat and mass transfer during the melting process: at 1350℃, when the carbon content was increased from 4.5 to 5.0 mass%, the scrap melting rate increased by 43%;for the carbon content of 4.5 mass%, when the temperature was increased from 1350 to 1400℃, the scrap melting rate increased by 60%. The carbonisation was found to be the restrictive step of the scrap melting process in deP-BOFs with respect to conventional ones. The scrap heating from room temperature to 800℃ reduced the crusting thickness on the scrap surface but there was no obvious influence on the melting rate. The scrap melting size in the deP-BOF was rather limited by its low melting rate and short melting time.

Removal kinetics of phosphorus from synthetic wastewater using basic oxygen furnace slag

Removal kinetics of phosphorus through use of basic oxygen furnace slag（BOF-slag）was investigated through batch experiments. Effects of several parameters such as initial phosphorus concentration, temperature, BOF-slag size, initial p H, and BOF-slag dosage on phosphorus removal kinetics were measured in detail. It was demonstrated that the removal process of phosphorus through BOF-slag followed pseudo-first-order reaction kinetics. The apparent rate constant（kobs） significantly decreased with increasing initial phosphorus concentration, BOF-slag size, and initial p H, whereas it exhibited an opposite trend with increasing reaction temperature and BOF-slag dosage.A linear dependence of kobson total removed phosphorus（TRP） was established with kobs=（3.51 ± 0.11） × 10^-4× TRP. Finally, it was suggested that the Langmuir–Rideal（L–R）or Langmuir–Hinshelwood（L–H） mechanism may be used to describe the removal process of phosphorus using BOF-slag.

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 1min 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.

Kinetics and mechanism of hexavalent chromium removal by basic oxygen furnace slag

Basic oxygen furnace slag（BOFS） has the potential to remove hexavalent chromium（Cr（VI））from wastewater by a redox process due to the presence of minerals containing Fe2＋. The effects of the solution p H, initial Cr（VI） concentration, BOFS dosage, BOFS particle size, and temperature on the removal of Cr（VI） was investigated in detail through batch tests. The chemical and mineral compositions of fresh and reacted BOFS were characterized using scanning electron microscope（SEM） equipped with an energy dispersive spectrometer（EDS）system and X-ray diffractometer（XRD）. The results show that Cr（VI） in wastewater can be efficiently removed by Fe2＋released from BOFS under appropriate acidic conditions. The removal of Cr（VI） by BOFS significantly depended on the parameters mentioned above. The reaction of Cr（VI） with BOFS followed the pseudo-second-order kinetic model. Fe2＋responsible for Cr（VI） removal was primarily derived from the dissolution of Fe O and Fe3O4 in BOFS. When H2SO4 was used to adjust the solution acidity, gypsum（Ca SO4·2H2O）could be formed and become an armoring precipitate layer on the BOFS surface, hindering the release of Fe2＋and the removal of Cr（VI）. Finally, the main mechanism of Cr（VI） removal by BOFS was described using several consecutive reaction steps.

Strength Activity Index of Air Quenched Basic Oxygen Furnace Steel Slag

Air quenched basic oxygen furnace steel slag （BOF-SS） is processed at very high cooling rate, which is expected to have different cementitious properties from conventional slowly cooled BOF-SS. For this purpose, the strength activity indexes of air quenched and slowly cooled BOF-SS are investigated. The results reveal that, under the specific surface area （S） of 490 m^2/kg, the compressive strength activity index reaches 1.24 after 28 days with replacement of 15% air quenched BOF-SS and reaches 1.05 after 28 days with replacement of 20% air quenched BOF-SS and 30%granulated blast furnace slag （GBFS）. The cementitious activity of air quenched BOF-SS is obviously higher than that of slowly cooled BOF-SS, mainly because it contains more C3 S and glassy phases.

Memetic algorithms-based neural network learning for basic oxygen furnace endpoint prediction

Based on the critical position of the endpoint quality prediction for basic oxygen furnaces （BOFs） in steelmaking, and the latest results in computational intelligence （C1）, this paper deals with the development of a novel memetic algorithm （MA） for neural network （NN） lcarnmg. Included in this is the integration of extremal optimization （EO） and Levenberg-Marquardt （LM） pradicnt search, and its application in BOF endpoint quality prediction. The fundamental analysis reveals that the proposed EO-LM algorithm may provide superior performance in generalization, computation efficiency, and avoid local minima, compared to traditional NN learning methods. Experimental results with production-scale BOF data show that the proposed method can effectively improve the NN model for BOF endpoint quality prediction.

Numerical investigation of basic oxygen furnace slag modification with gas bottom-blowing and SiO_(2) modifier

To avoid the volume expansion of basic oxygen furnace (BOF) slag for use in building materials, a hot slag modification process was proposed to reduce free CaO (f-CaO) in the molten slag. A transient 3D numerical model of BOF molten slag modification by SiO_(2) particles was established. The flow and heat transfer of molten slag, movement and dissolution of the modifier, and concentration distribution of f-CaO in slag during the modification of BOF were studied. The distribution of f-CaO concentration is inhomogeneous all over the molten slag. The mixing effect at the slag surface is weaker than that at the half-height plane of the slag. To consume the f-CaO below 2.0 wt.% in the slag, the optimum quantity of the SiO_(2) modifier is 10.0% of the mass of the slag. The fine SiO_(2) particles help attain a lower final mass fraction of f-CaO and a higher SiO_(2) utilization ratio.

Thermal and chemical analysis of massive use of hot briquetted iron inside basic oxygen furnace

The integrated steelmaking cycle based on the blast furnace-basic oxygen furnace（BOF）route plays an important role in the production of plain and ultra-low carbon steel,especially for deep drawing operations.BOF steelmaking is based on the conversion of cast iron in steel by impinging oxygen on the metal bath at supersonic speed.In order to avoid the addition of detrimental chemical elements owing to the introduction of uncontrolled scrap and in order to decrease environmental impact caused by the intensive use of coke for the production of cast iron,HBI（hot briquetted iron）can be used as a source of metal and a fraction of cast iron.Forty industrial experimental tests were performed to evaluate the viability of the use of HBI in BOF.The experimental campaign was supported by a thermal prediction model and realized through the estimation of the oxidation enthalpy.Furthermore,the process was thermodynamically analyzed based on oxygen potentials using the off-gas composition and the bath temperature evolution during the conversion as reference data.

Hybrid model for BOF oxygen blowing time prediction based on oxygen balance mechanism and deep neural network

The amount of oxygen blown into the converter is one of the key parameters for the control of the converter blowing process,which directly affects the tap-to-tap time of converter. In this study, a hybrid model based on oxygen balance mechanism (OBM) and deep neural network (DNN) was established for predicting oxygen blowing time in converter. A three-step method was utilized in the hybrid model. First, the oxygen consumption volume was predicted by the OBM model and DNN model, respectively. Second, a more accurate oxygen consumption volume was obtained by integrating the OBM model and DNN model. Finally, the converter oxygen blowing time was calculated according to the oxygen consumption volume and the oxygen supply intensity of each heat. The proposed hybrid model was verified using the actual data collected from an integrated steel plant in China, and compared with multiple linear regression model, OBM model, and neural network model including extreme learning machine, back propagation neural network, and DNN. The test results indicate that the hybrid model with a network structure of 3 hidden layer layers, 32-16-8 neurons per hidden layer, and 0.1 learning rate has the best prediction accuracy and stronger generalization ability compared with other models. The predicted hit ratio of oxygen consumption volume within the error±300 m^(3)is 96.67%;determination coefficient (R^(2)) and root mean square error (RMSE) are0.6984 and 150.03 m^(3), respectively. The oxygen blow time prediction hit ratio within the error±0.6 min is 89.50%;R2and RMSE are0.9486 and 0.3592 min, respectively. As a result, the proposed model can effectively predict the oxygen consumption volume and oxygen blowing time in the converter.

Prediction of lime utilization ratio of dephosphorization in BOF steelmaking based on online sequential extreme learning machine with forgetting mechanism

The machine learning models of multiple linear regression(MLR),support vector regression(SVR),and extreme learning ma-chine(ELM)and the proposed ELM models of online sequential ELM(OS-ELM)and OS-ELM with forgetting mechanism(FOS-ELM)are applied in the prediction of the lime utilization ratio of dephosphorization in the basic oxygen furnace steelmaking process.The ELM model exhibites the best performance compared with the models of MLR and SVR.OS-ELM and FOS-ELM are applied for sequential learning and model updating.The optimal number of samples in validity term of the FOS-ELM model is determined to be 1500,with the smallest population mean absolute relative error(MARE)value of 0.058226 for the population.The variable importance analysis reveals lime weight,initial P content,and hot metal weight as the most important variables for the lime utilization ratio.The lime utilization ratio increases with the decrease in lime weight and the increases in the initial P content and hot metal weight.A prediction system based on FOS-ELM is applied in actual industrial production for one month.The hit ratios of the predicted lime utilization ratio in the error ranges of±1%,±3%,and±5%are 61.16%,90.63%,and 94.11%,respectively.The coefficient of determination,MARE,and root mean square error are 0.8670,0.06823,and 1.4265,respectively.The system exhibits desirable performance for applications in actual industrial pro-duction.

A process model for BOF process based on bath mixing degree

The process model for BOF process can be applied to predict the liquid steel composition and bath temperature during the whole steelmaking process. On the basis of the traditional three-stage decarburization theory, the concept of mixing degree was put forward, which was used to indicate the effect of oxygen jet on decarburization. Furthermore, a more practical process model for BOF steelmaking was developed by analyzing the effect of silicon, manganese, oxygen injection rate, oxygen lance height, and bath temperature on decarburization. Process verification and end-point verification for the process model have been carried out, and the verification results show that the predic- tion accuracy of carbon content reaches 82.6% （the range of carbon content at the end-point is less than 0. 1wt%） and 85.7% （the range of carbon content at end-point is 0. 1wt% -0.7wt%） when the absolute error is less than 0.02wt% and 0.05wt%, respectively.

Technological progress in the production of pipeline steel at Baosteel

Baosteel started the production of pipeline steel in the early 1990s. The company has so far produced more than 4 Mt of API X42-X80 and hydrogen sulfide resistant X52-X65 steel. In 2006, it succeeded in producing X100 and X120 pipeline steels,which have been used in many important projects, such as the oil transporting project in the Tarim Basin, the pipeline project in Sudan,the West to East gas pipeline project,the Sino-Russian oil transporting project,etc. Baosteel has made great progress in the purity control of pipeline steel, the homogeneity control of slab composition and surface quality over the last ten years. The company has achieved this by improving its steelmaking equipment and operation management technology,and by integrating its process technology. The phosphorus and sulfur levels of the pipeline steel have been decreased to 50 ppm and 12 ppm respectively （1 ppm = 10-6）.

HMPT-BOF-RH-CSP process for SPHE substrate of cold rolled deep drawing steel

Based on hot metal pretreatment （HMPT）-basic oxygen furnace （BOF）-Rheinstahl Heraeus （RH）-compact strip production （CSP） process, parameters controlling on cold rolling deep drawing substrate SPHE were investigated during smelting and rolling process by composition design and technology control. The influence of parameters on chemical compositions, mechanical properties and microstructure was revealed by scanning electron microscope （SEM）. The results show that, 1） main chemical components in SPHE are w（C）_〈40×10^-6, w（Si）_〈 0.01%, w（S）_〈0.009%, w（N）〈20×10^-6, w（O）〈_ 25×10^-6; 2） main mechanical properties of the SPHE are Crs=274 MPa, 00=334 MPa, A=48.9%; 3） main performances of deep drawing quality （DDQ） grade steel produced by SPHE are as follows, transversely crs=167 MPa, 00=298 MPa, n=0.219, r=2.46; vertically σs=166 MPa, 00=298 MPa, n=0.226, r=2.39; in 45° direction σ=171 MPa, 00=308 MPa, n=0.214, t=2.26; 4） microstrueture of DDQ is ferrite, average grain size is Grade 7.5, and inclusion size is 3-10μm.

Application of variable-filtrating technique on fuzzy-reasoning neural network system predicting BOF end-point carbon content

Artificial intelligence techniques have been used to predict basic oxygen furnace(BOF) end-points. However,the main challenge is to effectively reduce the input nodes as too many input nodes in neural network increase complexity,decrease accuracy and slow down the training speed of the network.Simply picking-up variables as input usually influence validity of model.It is quite necessary to develop an effective method to reduce the number of input nodes whereby to simplify the network and improve model performance.In this study,a variable-filtrating technique combining both metallurgical mechanism model and partial least-squares(PLS ) regression method has been proposed by taking the advantages of both of them,i.e.qualitive and quantative relationships between variables respectively.Accordingly,a fuzzy-reasoning neural network(FNN) prediction model for basic oxygen furnace(BOF) end-point carbon content based on this technique has been developed.The prediction results showed that this model can effectively improve the hit rate of end-point carbon content and increase network training speed.The successful hit rate of the model can reach up to 94.12%with about 0.02% error range.

Numerical Analysis of Slag Splashing in a Steelmaking Converter

Some variables that influence the slag splashing phenomenon in an oxygen steelmaking converter are numerically analyzed in this work. The effect of lance height, jet velocity, jet exit angle and slag viscosity on the washing and ejection mechanisms of slag splashing is studied employing transient two-dimensional computational fluid dynamics simulations. A parameter here called average slag volume fraction is proposed for the quantitative evaluation of the slag splashing efficiency. Besides, a qualitative comparison is made between the computational fluid dynamics results and physical model results from literature.

Prediction of Endpoint Phosphorus Content of Molten Steel in BOF Using Weighted K-Means and GMDH Neural Network

The hybrid method composed of clustering and predicting stages is proposed to predict the endpoint phos- phorus content of molten steel in BOF （Basic Oxygen Furnace）. At the clustering stage, the weighted K-means is performed to generate some clusters with homogeneous data. The weights of factors influencing the target are calcu- lated using EWM （Entropy Weight Method）. At the predicting stage, one GMDH （Group Method of Data Handling） polynomial neural network is built for each cluster. And the predictive results from all the GMDH polynomial neural networks are integrated into a whole to be the result for the hybrid method. The hybrid method, GMDH polnomial neural network and BP neural network are employed for a comparison. The results show that the proposed hybrid method is effective in predicting the endpoint phosphorus content of molten steel in BOF. Furthermore, the hybrid method outperforms BP neural network and GMDH polynomial neural network.

Water quality improvement of a lagoon containing mixed chemical industrial wastewater by micro-electrolysis-contact oxidization

A lagoon in the New Binhai District, a high-speed developing area, Tianjin, China, has long been receiving the mixed chemical industrial wastewater from a chemical industrial park. This lagoon contained complex hazardous substances such as heavy metals and accumulative pollutants which stayed over time with a poor biodegradability. According to the characteristics of wastewater in the lagoon, the micro-electrolysis process was applied to improve the biodegradability before the bioprocess treatment. By the orthogonal experimental study of main factors influencing the efficiency of the treatment method, the best control parameters were obtained, including pH=2.0, a volume ratio of Fe and reaction wastewater of 0.03750, a volume ratio of Fe and the granular activated carbon (GAC) of 2.0, a mixing speed of 200 r/min, and a hydraulic retention time (HRT) of 1.5 h. In the meantime, the removal rate of chemical oxygen demand (COD) was up to 64.6%, and NH4+-N and Pb in the influent were partly removed. After the micro-electrolysis process, the ratio of biochemical oxygen demand (BOD) to COD (B/C ratio) was greater than 0.6, thus providing a favorable basis for bioprocess treatment.

An online BOF terminal temperature control model based on big data learning

The development of basic oxygen furnace(BOF)intelligent steelmaking model based on artificial intelligence and big data is the focus of international research and development.In the view of the current situation that the BOF cannot continuously detect the composition and molten steel temperature,combined with the monitoring results of the high-definition and high-brightness camera at the converter mouth,an online BOF terminal temperature control model is established based on big data learning case-based reasoning model and expert system model.The on-site online operation shows that the model can effectively improve the"flying lance"phenomenon and the splashing condition,the stability and safety of smelting process are better than that of artificial smelting,the"flying lance"rate decreases from 39.2% to 0,the early splashing rate decreases from 21.4% to 13.3% and the late splashing rate decreases from 81.25% to 56.7%.When the temperature fluctuation is controlled at±15 oC,the hit rate of the terminal temperature under the automatic control of the model is 90.91%.

Control Model of Multifunctional Hot Metal Ladles

For further research on the control model of muhifunctional hot metal ladles between the ironmaking and steelmaking interface, the hot metal ladies of K steel plant were taken as the object to analyze the operation process. The factors of blast furnace supply and basic oxygen furnace demand were proposed. According to the principle of supply and demand balance, the control model of hot metal was researched under the following factor conditions： equal to, greater than, and less than 1, respectively. The distribution model of the blast furnace, sleelmaking works, and online buffering was proposed. When the supply and demand factor is equal to ） , the turnover number of hot metal ladles equals 16 and the turnover cycle of hot metal ladles equals 512 min. When the factor is greater than 1, the total number of hot metal ladles is equal to the normal turnover number plus the turnover number of the cast iron machine. When the factor is less than 1, the total number of hot metal ladles is equal tO the normal turnover number plus the accumulating number. Satisfactory effects were obtained by applying the control model in produc tion. The numbers of turnover ladles and accumulating ladles were reduced.

Study on Cleanliness of High Quality Steels in Shouqin

Study on cleanliness of high quality steels in 100t basic oxygen furnace was carried out by "Hot Metal Desulphurization-Basic Oxygen Furnace-Ladle Furnace Refining-RH Refining-Continuous Casting" process. High-quality low-carbon ultra thick slabs were produced with 0.04%≤wC≤0.08%, wS≤0.002%, wP≤0.008%, wN≤0.004%, wH≤ 0.0002%, wT[O] ≤0.0015%. A series of key technologies for content control of carbon, phosphorus, sulfur, nitrogen, hydrogen and inclusions were determined, which has laid a foundation for the development of the high-degree steel.