Numerical investigation of sinusoidal pulsating gas intake to intensify the gas-slag momentum transfer in the top-blown smelting furnace

The variation characteristics of bubble morphology and the thermal-physical properties of bubble boundary in the top-blown smelting furnace were explored by means of the computational fluid dynamics method.The essential aspects of the fluid phase(e.g.,splashing volume,dead zone of copper slag,and gas penetration depth)were explored together with the effect of sinusoidal pulsating gas intake on the momentum-transfer performance between phases.The results illustrated that two relatively larger vortices and two smaller vortices appear in the bubble waist and below the lance,respectively.The expansion of larger ones as well as the shrinking of smaller ones combine to cause the contraction of the bubble waist.Compared to the results of the case with a fixed gas injection velocity(V_(g)=58 m/s),the splashing volume and dead zone volume of the slag under the V_(g)=58+10sin(2πt)condition are reduced by 24.9%and 23.5%,respectively,where t represents the instant time.Gas penetration depth and slag motion velocity of the latter are 1.03 and 1.31 times high-er than those of the former,respectively.

CFD modeling of gas−liquid flow phenomenon in lead smelting oxygen-enriched side-blown furnace

A validated numerical model was established to simulate gas−liquid flow behaviors in the oxygen-enriched side-blown bath furnace.This model included the slip velocity between phases and the gas thermal expansion effect.Its modeling results were verified with theoretical correlations and experiments,and the nozzle-eroded states in practice were also involved in the analysis.Through comparison,it is confirmed that the thermal expansion effect influences the flow pattern significantly,which may lead to the backward motion of airflow and create a potential risk to production safety.Consequently,the influences of air injection velocity and furnace width on airflow behavior were investigated to provide operating and design guidance.It is found that the thin layer melt,which avoids high-rate oxygen airflow eroding nozzles,shrinks as the injection velocity increases,but safety can be guaranteed when the velocity ranges from 175 to 275 m/s.Moreover,the isoline patterns and heights of thin layers change slightly when the furnace width increases from 2.2 to 2.8 m,indicating that the furnace width shows a limited influence on production safety.

Research progress and future prospects in the service security of key blast furnace equipment

The safety and longevity of key blast furnace(BF)equipment determine the stable and low-carbon production of iron.This pa-per presents an analysis of the heat transfer characteristics of these components and the uneven distribution of cooling water in parallel pipes based on hydrodynamic principles,discusses the feasible methods for the improvement of BF cooling intensity,and reviews the pre-paration process,performance,and damage characteristics of three key equipment pieces:coolers,tuyeres,and hearth refractories.Fur-thermoere,to attain better control of these critical components under high-temperature working conditions,we propose the application of optimized technologies,such as BF operation and maintenance technology,self-repair technology,and full-lifecycle management techno-logy.Finally,we propose further researches on safety assessments and predictions for key BF equipment under new operating conditions.

Process metallurgy and data-driven prediction and feedback of blast furnace heat indicators

The prediction and control of furnace heat indicators are of great importance for improving the heat levels and conditions of the complex and difficult-to-operate hour-class delay blast furnace(BF)system.In this work,a prediction and feedback model of furnace heat indicators based on the fusion of data-driven and BF ironmaking processes was proposed.The data on raw and fuel materials,process op-eration,smelting state,and slag and iron discharge during the whole BF process comprised 171 variables with 9223 groups of data and were comprehensively analyzed.A novel method for the delay analysis of furnace heat indicators was established.The extracted delay variables were found to play an important role in modeling.The method that combined the genetic algorithm and stacking efficiently im-proved performance compared with the traditional machine learning algorithm in improving the hit ratio of the furnace heat prediction model.The hit ratio for predicting the temperature of hot metal in the error range of±10℃ was 92.4%,and that for the chemical heat of hot metal in the error range of±0.1wt%was 93.3%.On the basis of the furnace heat prediction model and expert experience,a feedback model of furnace heat operation was established to obtain quantitative operation suggestions for stabilizing BF heat levels.These sugges-tions were highly accepted by BF operators.Finally,the comprehensive and dynamic model proposed in this work was successfully ap-plied in a practical BF system.It improved the BF temperature level remarkably,increasing the furnace temperature stability rate from 54.9%to 84.9%.This improvement achieved considerable economic benefits.

Coke behavior with H_(2)O in a hydrogen-enriched blast furnace:A review

Hydrogen-enriched blast furnace ironmaking has become an essential route to reduce CO_(2)emissions in the ironmaking process.However,hydrogen-enriched reduction produces large amounts of H_(2)O,which places new demands on coke quality in a blast furnace.In a hydrogen-rich blast furnace,the presence of H_(2)O promotes the solution loss reaction.This result improves the reactivity of coke,which is 20%-30%higher in a pure H_(2)O atmosphere than in a pure CO_(2)atmosphere.The activation energy range is 110-300 kJ/mol between coke and CO_(2)and 80-170 kJ/mol between coke and H_(2)O.CO_(2)and H_(2)O are shown to have different effects on coke degradation mechanisms.This review provides a comprehensive overview of the effect of H_(2)O on the structure and properties of coke.By exploring the interactions between H_(2)O and coke,several unresolved issues in the field requiring further research were identified.This review aims to provide valuable insights into coke behavior in hydrogen-rich environments and promote the further development of hydrogen-rich blast furnace ironmaking processes.

Oxygen-assisted zinc recovery from electric arc furnace dust using magnesium chloride

Electric arc furnace(EAF)dust is an important secondary resource containing metals,such as zinc(Zn)and iron(Fe).Recover-ing Zn from EAF dust can contribute to resource recycling and reduce environmental impacts.However,the high chemical stability of ZnFe_(2)O_(4)in EAF dust poses challenges to Zn recovery.To address this issue,a facile approach that involves oxygen-assisted chlorination using molten MgCl_(2)is proposed.This work focused on elucidating the role of O2 in the reaction between ZnFe_(2)O_(4)and molten MgCl_(2).The results demonstrate that MgCl_(2)effectively broke down the ZnFe_(2)O_(4)structure,and the high O2 atmosphere considerably promoted the sep-aration of Zn from other components in the form of ZnCl_(2).The presence of O2 facilitated the formation of MgFe_(2)O_(4),which stabilized Fe and prevented its chlorination.Furthermore,the excessive use of MgCl_(2)resulted in increased evaporation loss,and high temperatures pro-moted the rapid separation of Zn.Building on these findings,we successfully extracted ZnCl_(2)-enriched volatiles from practical EAF dust through oxygen-assisted chlorination.Under optimized conditions,this method achieved exceptional Zn chlorination percentage of over 97%within a short period,while Fe chlorination remained below 1%.The resulting volatiles contained 85wt%of ZnCl_(2),which can be further processed to produce metallic Zn.The findings offer guidance for the selective recovery of valuable metals,particularly from solid wastes such as EAF dust.

Extraction of Valuable Metals from Titanium-bearing Blast Furnace Slag by Acid Leaching

To realize the resource utilization of the valuable metals in the titanium-containing blast furnace slag,the process route of “hydrochloric acid leaching-electrolysis-carbonization and carbon dioxide capture-preparation of calcium carbonate” was proposed.In this study,the influences of process conditions on the leaching rates of calcium,magnesium,aluminum,and iron and the phases of the leaching residue were investigated for the leaching process.The experimental results show that the HCl solution could selectively leach the elements from the titanium-containing blast furnace slag.The better leaching conditions are the HCl solution concentration of 4 mol/L,the leaching time of 30 min,the ratio of liquid volume to solid gas of 10 mL/g,and the stirring paddle speed of 300 r/min.Under the conditions,the leaching rates of calcium,magnesium,aluminum,and iron can reach 85.87%,73.41%,81.35%,and 59.08%,and the leaching rate of titanium is 10.71%.The iron and the aluminum are removed from the leachate to obtain iron-aluminum water purification agents,and the magnesium is removed from the leachate to obtain magnesium hydroxide.The leaching residue phase is dominated by perovskite,followed by magnesium silicate and tricalcium aluminate,and the titaniumrich material could be obtained from the leaching residue by desiliconization.

Cross-upgrading of biomass hydrothermal carbonization and pyrolysis for high quality blast furnace injection fuel production:Physicochemical characteristics and gasification kinetics analysis

The paper proposes a biomass cross-upgrading process that combines hydrothermal carbonization and pyrolysis to produce high-quality blast furnace injection fuel.The results showed that after upgrading,the volatile content of biochar ranged from 16.19%to 45.35%,and the alkali metal content,ash content,and specific surface area were significantly reduced.The optimal route for biochar pro-duction is hydrothermal carbonization-pyrolysis(P-HC),resulting in biochar with a higher calorific value,C=C structure,and increased graphitization degree.The apparent activation energy(E)of the sample ranges from 199.1 to 324.8 kJ/mol,with P-HC having an E of 277.8 kJ/mol,lower than that of raw biomass,primary biochar,and anthracite.This makes P-HC more suitable for blast furnace injection fuel.Additionally,the paper proposes a path for P-HC injection in blast furnaces and calculates potential environmental benefits.P-HC of-fers the highest potential for carbon emission reduction,capable of reducing emissions by 96.04 kg/t when replacing 40wt%coal injec-tion.

A Novel Sensing Imaging Equipment Under Extremely Dim Light for Blast Furnace Burden Surface:Starlight High-Temperature Industrial Endoscope

Blast furnace(BF)burden surface contains the most abundant,intuitive and credible smelting information and acquiring high-definition and high-brightness optical images of which is essential to realize precise material charging control,optimize gas flow distribution and improve ironmaking efficiency.It has been challengeable to obtain high-quality optical burden surface images under high-temperature,high-dust,and extremelydim(less than 0.001 Lux)environment.Based on a novel endoscopic sensing detection idea,a reverse telephoto structure starlight imaging system with large field of view and large aperture is designed.Combined with a water-air dual cooling intelligent self-maintenance protection device and the imaging system,a starlight high-temperature industrial endoscope is developed to obtain clear optical burden surface images stably under the harsh environment.Based on an endoscope imaging area model,a material flow trajectory model and a gas-dust coupling distribution model,an optimal installation position and posture configuration method for the endoscope is proposed,which maximizes the effective imaging area and ensures large-area,safe and stable imaging of the device in a confined space.Industrial experiments and applications indicate that the proposed method obtains clear and reliable large-area optical burden surface images and reveals new BF conditions,providing key data support for green iron smelting.

Furnace Temperature Curve Optimization Model Based on Differential Evolution Algorithm

When soldering electronic components onto circuit boards,the temperature curves of the reflow ovens across different zones and the conveyor belt speed significantly influence the product quality.This study focuses on optimizing the furnace temperature curve under varying settings of reflow oven zone temperatures and conveyor belt speeds.To address this,the research sequentially develops a heat transfer model for reflow soldering,an optimization model for reflow furnace conditions using the differential evolution algorithm,and an evaluation and decision model combining the differential evolution algorithm with the Technique for Order Preference by Similarity to Ideal Solution(TOPSIS)method.This approach aims to determine the optimal furnace temperature curve,zone temperatures of the reflow oven,and the conveyor belt speed.

Production Practice of Solid Waste Desulfurizer Disposal in European Metallurgical Furnace

In order to realize the OY Smelting Furnace disposal of solid waste,municipal waste,form a model of steel enterprises and urban integration,through the production practice of smelting furnace,analyzes the mechanism of solid waste disposal,and proposed the disposal of solid waste desulfurization agent and disposal method:According to the theory to guide the actual production process,the test ton of iron with 10-15kg of desulfurizer in the production process does not affect the quality of molten iron and slag quality.

Analysis and Prospect of Waste Heat Utilization from Blast Furnace Slag Flushing

Estimating the residual heat of blast furnace slag flushing in China,classifying and introducing the current proposed methods of slag flushing waste heat utilization,and listing existing cases.In order to better save energy and water in the slag flushing process of blast furnaces,an ideal comprehensive cascade utilization system scheme for annual recovery of waste heat is proposed.Based on the measured waste heat data of a steel plant,design calculations are carried out to further analyze the economic feasibility of the new scheme and provide reference for its promotion and application.

Predicting the alloying element yield in a ladle furnace using principal component analysis and deep neural network

The composition control of molten steel is one of the main functions in the ladle furnace(LF)refining process.In this study,a feasible model was established to predict the alloying element yield using principal component analysis(PCA)and deep neural network(DNN).The PCA was used to eliminate collinearity and reduce the dimension of the input variables,and then the data processed by PCA were used to establish the DNN model.The prediction hit ratios for the Si element yield in the error ranges of±1%,±3%,and±5%are 54.0%,93.8%,and98.8%,respectively,whereas those of the Mn element yield in the error ranges of±1%,±2%,and±3%are 77.0%,96.3%,and 99.5%,respectively,in the PCA-DNN model.The results demonstrate that the PCA-DNN model performs better than the known models,such as the reference heat method,multiple linear regression,modified backpropagation,and DNN model.Meanwhile,the accurate prediction of the alloying element yield can greatly contribute to realizing a“narrow window”control of composition in molten steel.The construction of the prediction model for the element yield can also provide a reference for the development of an alloying control model in LF intelligent refining in the modern iron and steel industry.

Corrosion Mechanism of Alumina-magnesia Dry Materials for Smelting Manganese/chromium Steel in Coreless Medium Frequency Induction Furnaces

Alumina-magnesia dry materials are widely used in induction furnace linings, but they show different kinds of damage when melting different kinds of alloy steel. In this paper, the chemical composition, phase composition, and microstructure of the post-use dry materials for the working liners melting different kinds of steel were evaluated. Furthermore, the corrosion mechanism of the steel on the furnace lining materials was comprehensively analyzed. The findings reveal a significant ability of the Mn element in the molten steel to diffuse and penetrate into the refractories. Mn oxidizes to form MnO at the steel-refractory interface, and then forms a liquid phase with Al_(2)O_(3). The Cr element is dissolved into corundum and spinel of the refractories, resulting in lattice defects and structural damage of the materials. TiO2reacts with Al_(2)O_(3) to form Al_(2)TiO_(5), which plays a crucial role in preventing crack formation and propagation. Part of Ti4+dissolves into magnesia-alumina(MA), densifying the materials. TiO2also slows down the reaction between the Cr element and refractory components, further improving the corrosion resistance of the materials.

Experiment and numerical simulation of two-phase flow in oxygen enriched side-blown furnace

Taking an oxygen enriched side-blown furnace as the prototype,a hydraulic model was established according to the similarity principle.The influence of three factors on the gas-liquid two-phase flow was analyzed,i.e.the airflow speed,the submerged depth and the downward angle of the nozzle.A numerical simulation of the hydraulic model was carried out trying to find the suitable turbulence model which can describe the side-blown two-phase flow correctly by comparing the simulation results with the experimental data.The experiment shows that the airflow speed has a great influence on the flow of the water.The submerged depth of the nozzle has a relatively smaller influence on the penetration depth and the surface fluctuation height in the liquid phase.When the nozzle is at a downward angle of 15°,the penetration depth and the surface fluctuation height are reduced.It is concluded that the numerical results with the realizable k-εturbulence model are the closest to the experiment for the penetration depth,the surface fluctuation height and the bubble scale.

Synthesis,Characterization of NaA Zeolite from Blast Furnace Slag(BFS)via Alkaline Fusion and Hydrothermal Treatment

A blast furnace slag zeolite(BFSZ)material was successfully synthesized from BFS by alkaline fusion and hydrothermal treatment.Via the analyses of XRD,FT-IR,FE-SEM,XRF,CEC and BET surface area measurement,when zeolite was synthesized at a crystallization temperature of 100℃with initial Si/Al ratio of 1:1,the main composition in the product is Na-A zeolite.Under the above conditions,the BFSZ was synthesized with CEC of 3.06 meq/g and maximum BET surface area of 37.55 m^(2)·g^(-1).Moreover,the incorporating of BFS-derived minor metals(such as Mg,Fe,and Ca)are found to be of little importance for the synthesis of BFSZ.Thus the obtained BFSZ material has a great adsorption performance for removing Mn^(2+),Cu^(2+),and NH_(4)^(+)ions diluted in water,owing to the higher CEC.

Minimax entropy-based co-training for fault diagnosis of blast furnace

Due to the problems of few fault samples and large data fluctuations in the blast furnace(BF)ironmaking process,some transfer learning-based fault diagnosis methods are proposed.The vast majority of such methods perform distribution adaptation by reducing the distance between data distributions and applying a classifier to generate pseudo-labels for self-training.However,since the training data is dominated by labeled source domain data,such classifiers tend to be weak classifiers in the target domain.In addition,the features generated after domain adaptation are likely to be at the decision boundary,resulting in a loss of classification performance.Hence,we propose a novel method called minimax entropy-based co-training(MMEC)that adversarially optimizes a transferable fault diagnosis model for the BF.The structure of MMEC includes a dual-view feature extractor,followed by two classifiers that compute the feature's cosine similarity to representative vector of each class.Knowledge transfer is achieved by alternately increasing and decreasing the entropy of unlabeled target samples with the classifier and the feature extractor,respectively.Transfer BF fault diagnosis experiments show that our method improves accuracy by about 5%over state-of-the-art methods.

Efficient Use of Steel Slag in Alkali-Activated Blast Furnace Slag Based Geopolymer

Energy shortage and the emission of greenhouse gases have become a global problem of urgent concern.Therefore,there is an urgent need to develop a low carbon building material.Geopolymers have become a hot topic due to their environmental sustainability and the feasibility of immobilizing industrial waste.In this paper,steel slag(SS)fines were investigated as auxiliary materials of blast furnace slag(BFS)based geopolymer.The hydration heat properties,flowability,compressive strength,sorptivity coefficient,X-ray diffraction(XRD),and scanning electron microscopy(SEM)of the geopolymer pastes were determined.The results showed that the incorporation of SS weakened the reactivity of the BFS-based geopolymer paste and improved the flow values of the paste.The compressive strength of the geopolymer with 20%SS content reached 117 MPa at 28 d.The geopolymer specimens with high compressive strength showed a low sorptivity coefficient.The microscopic results showed that the addition of the appropriate amount of SS reduced the cracks,improved the density of the geopolymer,and produced a geopolymer composite with excellent mechanical properties.

Post-mortem Microstructural Study of Aluminous Refractory Brick Used in Channels of Blast Furnaces

Aluminous refractory materials with high alumina contents are widely used in the steel industry,and the higher the alumina content,the higher the working temperature.Properties such as high refractoriness and thermal shock resistance lead these refractory materials to be used as channel linings of blast furnaces,where they are exposed to the attack by slag,molten steel,working cycles and sudden temperature changes between 25℃(room temperature)and 1520℃(the temperature of molten pig iron).In this work,microstructural changes in post-mortem aluminous refractory bricks were investigated by apparent porosity,X-ray diffraction analysis(XRD),Fourier transform infrared spectroscopy(FTIR),scanning electron microscopy,and X-ray dispersion energy spectrometry(SEM/EDS).The results showed an increase in the apparent porosity and the bulk density and the presence of the phases mullite,sillimanite,alumina,and quartz in the post-mortem brick.Calcium and magnesium were not detected in the microstructure of the post-mortem brick,indicating that slags did not corrode these refractory materials.Therefore,the microstructural changes that occurred in the post-mortem bricks must be due to thermal cycling.In the X-ray diffraction(XRD)test,mullite,sillimanite,quartz,andα-alumina phases were identified.These results indicate that the aluminous refractory was obtained from sillimanite.In infrared spectroscopy(FTIR)it was possible to identify the vibration bands referring to the Si-O and Al-O bonds.The increase in the porosity is a result of cracks caused by work cycles at high temperatures and the temperature gradient to which the refractory was subjected during use.Through the micrograph it was possible to identify the presence of acicular mullite.The absence of magnesium and calcium in the microanalysis results by energy dispersed X-ray spectrometry(EDS)indicates that there was no infiltration by slag or liquid iron.These results indicate that the microstructural changes that occurred in the post-mortem aluminous refractory were of a thermal nature.

CLOF Based Outlier Detection Algorithm of Temperature Data for Ethylene Cracking Furnace

The flue temperature is one of the important indicators to characterize the combustion state of an ethylene cracker furnace,the outliers of temperature data can lead to the false alarm.Conventional outlier detection algorithms such as the Isolation Forest algorithm and 3-sigma principle cannot detect the outliers accurately.In order to improve the detection accuracy and reduce the computational complexity,an outlier detection algorithm for flue temperature data based on the CLOF(Clipping Local Outlier Factor,CLOF)algorithm is proposed.The algorithm preprocesses the normalized data using the cluster pruning algorithm,and realizes the high accuracy and high efficiency outlier detection in the outliers candidate set.Using the flue temperature data of an ethylene cracking furnace in a petrochemical plant,the main parameters of the CLOF algorithm are selected according to the experimental results,and the outlier detection effect of the Isolation Forest algorithm,the 3-sigma principle,the conventional LOF algorithm and the CLOF algorithm are compared and analyzed.The results show that the appropriate clipping coefficient in the CLOF algorithm can significantly improve the detection efficiency and detection accuracy.Compared with the outlier detection results of the Isolation Forest algorithm and 3-sigma principle,the accuracy of the CLOF detection results is increased,and the amount of data calculation is significantly reduced.