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As for steelmaking process such as basic oxygen furnace (BOF) and electric arc furnace (EAF), slag foaming consists of introducing gas bubbles into molten metal and slag by chemical reaction. In the case of the BOF process, excessive foaming is over the converter capacity, a phenomenon called "slopping". Slopping reduces yield and equipment lifespan and increases production time. It is therefore important to control slag foaming properly. In previous studies by other investigators, the jet from top lance in BOF process effectively suppresses slag foaming. However, it is not obvious which mechanism of the jet from top lance is effective to suppress slag foaming, and its quantitative effect has not been reported. To clarify the relationship between slag foaming and the jet from top lance, the effects of the number of nozzle holes and lance height on the slag foaming were investigated by using a converter-shaped water-model device and test converter. The experimental results indicated that slag foaming height decreased as the number of nozzle holes increased. Also, slag foaming height changed instantly with the change in lance height, e.g., slag foaming height decreased as lance height increased, and vice versa. The foaming suppression mechanism of the jet from top lance is the entrainment of foaming slag into the jet. Consequently, slag foaming model that takes the effect of the jet from top lance into account is proposed. And it enables to predict the change in slag foaming height with time.

Total iron contents in iron ores have been accurately determined by JIS M 8212, in which iron ions in digested solutions of iron ores are reduced to divalent prior to redox titration. It is necessary for the iron reduction process that no reducing chemicals other than iron(II) in the decomposition solutions must not remain after the reduction with titanium(III). However, the redox reactions concerning the chemical species present in the decomposition solution has not been completely elucidated at the present time. In this paper, the redox reactions that occurred in the decomposition solution during the iron reduction in JIS M 8212 were studied by potentiometry and spectrophotometry under nitrogen atmosphere. The redox reaction of tin(II)/(IV) was very slow, causing significant effects on identifying the end point of the indicator for the iron reduction. The copper chloro-complexes were reduced with titanium(III) at a potential higher than that of indigo carmine used as a redox indicator, so that the reduced copper(I) gave a positive error to the potassium dichromate titration. The pentavalent vanadium was reduced with titanium (III) to form a complex with titanium, which also interfered with the potassium dichromate titration positively. To avoid these interferences, titanium(III) chloride was stoichiometrically added to the reaction mixture after addition of tin(II) chloride under nitrogen atmosphere so as to reduce only iron to divalent prior to the following redox titration. Combination of the proposed protocol with the potassium dichromate titration could successfully determine the iron content of certified reference materials of iron ores.

Sodium modification is an effective approach for enhancing the properties of bentonite and reducing its usage in pellets. However, due to limited research, the relationship between the physicochemical properties of bentonite and its green ball properties remains unclear, and the optimal degree of modification for bentonite has rarely been discussed. Therefore, this paper proposes a novel research idea: to exploring the correlation between the five most commonly used indexes for evaluating the physicochemical properties of bentonite (water absorption, methylene blue index, swell capacity, colloid index, and cation exchange capacity) and the most frequently used evaluation indexes for assessing green ball performance (drop strength), in order to determine the optimal degree of sodium modification of bentonite for pellets. The response surface methodology was employed in this paper to investigate the quantitative relationship between the five indexes and the green ball drop strength. The results demonstrate that when the drop strength of the green ball reaches its optimal level, the five commonly used indicators of bentonite are as follows: water absorption is 545.27%, methylene blue index is 22.94g/100g, swell capacity is 72.36ml/g, colloid index is 35.95ml/g, and cation exchange capacity is 68.93mmol/100g. Under these conditions, it has been the predicted value for green ball drop strength is determined to be 12.88, which exceeds the maximum value in the experimental conditions by 48.05%. The study determined the optimal degree of sodium modification for bentonite in pelletizing, providing valuable guidance for optimizing the properties of bentonite.

The iron and steelmaking industry must focus on neutralizing CO₂ emissions. One solution involves using hydrogen as a reducing agent for iron ore. However, carbon is an essential element as primary steel is produced by refining molten carbon-saturated iron (hot metal). Ironmaking processes applying CO₂ capture and utilization have been suggested; however, they are limited to the reduction process. To satisfy the demand for primary steel production with net-zero CO₂ emissions, a new carbon recycling ironmaking process capable of producing hot metal must be considered. This study proposes a carbon recycling ironmaking process using deposited carbon-iron ore composite (CRIP-D). In the CRIP-D process, hot metal is produced by using the solid carbon recovered by reforming exhaust gas as reducing and carburizing agents. Moreover, using the recovered solid carbon, iron oxides are reduced more rapidly, and reduced iron is melted at a lower temperature than that using fossil fuel-derived carbon. This means carbon-neutral steel can be produced more efficiently than conventional ironmaking processes. Using proven technologies, following hot metal production, primary steel can be produced while minimizing the burden on the steel mills for converting equipment. Thus, true carbon-neutral primary steel is feasible using the proposed CRIP-D process.

3D vision technologies have been widely used in metallurgy industry to measure particle size distribution (PSD) of green pellets on conveyor. However, 3D camera only captures the point clouds of surface pellets, and algorithms measure the surface PSD. To what extent the measured surface PSD can reflect whole PSD is a question that hasn't been answered yet. In the present work, a simulation method is proposed to analyze the PSD measurement error of green pellets. First, the motion process of green pellets on conveyor is simulated by discrete element method to obtain PSD of whole pellets; then, a transformation method is proposed to generate point clouds of simulated surface pellets, and region growing-based method is adopted to measure the PSD of surface pellets; finally, the PSD measuring error can be obtained by comparing surface PSD and whole PSD of pellets. Error analysis of green pellet size distribution measurement on conveyors is conducted, in aspects of camera location, patch number of point clouds, thickness as well as size distribution of pellet bed. Results illustrate that although the PSD measuring error (up to 12.3%) cannot be neglected when camera is installed above conveyor, it can be effectively reduced by increasing the patch number of captured point clouds (reduced by more than 7.4%) or installing camera near discharge of conveyor (reduced to less than 3.1%).

Accurately predicting the end temperature of molten steel is significant for controlling ladle furnace (LF) refining. This paper proposes an error correction method called EC-CBR based on case-based reasoning (CBR) to reduce errors in the prediction models caused by discrepancies between actual production data and training data. The proposed method combines the incremental learning advantage of CBR with the ability of other models to fit nonlinear relations. First, a prediction model is established, and historical heats similar to the new heat are retrieved by CBR. Then, the model error of the new heat is calculated by employing the errors of similar heats. The prediction result is calculated by subtracting the error from the predicted value. Testing and comparison are conducted on the models (support vector regression, backpropagation neural network, extreme learning machine and mechanism model) and general CBR using actual production data. Results show that the EC-CBR is effective for both data-driven and mechanism models, with an increase of approximately 5% in hit rate within the range of ±5°C for data-driven models and an increase of 21.73% for mechanism model. Moreover, the corrected data-driven models show higher accuracy than the general CBR, further proving the effectiveness of the proposed method.

Aiming at the problem of coadjustment of blast furnace raw materials and operation parameters, this paper proposes a cooptimization model of blast furnace batching that integrates Random Forest and NSGA-Ⅲ (Non-dominated Sorting Genetic Algorithm III) algorithm. First, blast furnace field data were collected for a two-year time span, and a predictive model for CO² emissions and blast furnace permeability was constructed using the Random Forest algorithm; taking the goodness of fit (R²), mean square error (MSE) and mean absolute error (MAE) as the evaluation indexes, the R² of the two prediction models obtained reached 0.93 and 0.96 respectively, and the MSE and MAE tended to be close to the zero value. Then, NSGA-Ⅲ was used to establish the blast furnace batching optimization model to optimally solve the batching scheme and the corresponding blast furnace operating parameters by taking the lowest batching cost, the lowest carbon dioxide emission and the maximum blast furnace permeability as the objective function, and the composition requirement of raw materials and the range limitation of operating parameters as the constraints; finally, the model was validated using the actual on-site data, and the application results showed that the output of the model conformed to the Finally, the results show that the model output meets the composition requirements and obtains a lower-cost dosage scheme than the original dosage ratio; moreover, this scheme corresponds to a blast furnace with less carbon dioxide emission, better blast furnace permeability and less slag. Therefore, the model can provide an effective reference for field operators to optimize blast furnace batching and operation.

The silicon content of hot metal is a key index for the determination of blast furnace status, and accurate prediction of the silicon content of hot metal is crucial for blast furnace ironmaking. First, 10992 sets of blast furnace data obtained from the site of an iron and steel enterprise were preprocessed. Then, 22 important feature parameters related to the silicon content of hot metal were screened by feature engineering. Finally, the hyperparameters of the Gradient Boosting Decision Tree (GBDT) algorithm model were optimized with the help of the Optuna framework, and the Optuna-GBDT model was established to predict the silicon content of hot metal. The experimental results show that compared with the Bayesian algorithm and the traditional stochastic search method, the Optuna framework can achieve better hyperparameter optimization with fewer iterations and smaller errors.The Optuna-GBDT model performs better in predicting the silicon content of hot metal compared with the optimized Random Forest (RF), Decision Tree and AdaBoost models, and the prediction results are basically in line with the actual values, with the mean absolute error (MAE) of 0.0094, the root mean square error (RMSE) of 0.0152, and the coefficient of determination (R2) of 0.975.The experimental results verified the validity and feasibility of establishing the Optuna-GBDT model to predict the silicon content of hot metal, which provides a reliable tool for iron and steel enterprises and helps to optimize the ironmaking process, improve production efficiency and product quality.

Austenitic Stainless Steel (ASS) and Duplex Stainless Steel (DSS) are joined to optimize the Resistance Spot Welding (RSW) process parameters and to predict the parametric influence on the response of Tensile Shear Fracture Load (TSFL). The Response Surface Methodology (RSM) is an optimization technique is used in this research to develop the satisfactory quadratic mathematical model and to predict the response. The optimal parameters and their levels are found and reported as follows: welding current = 9 kA, welding time = 0.18 seconds and electrode tip radius = 3 mm. The actual and predicted values of TSFL for the optimized parameters are 17.6 kN and 17.9 kN respectively. The developed quadratic model is efficiently predicted the response with an average error percentage of 2.18. The significant and insignificant terms in the models has been identified by 95% confidence level using ‘p' test. The insignificant terms are removed from the model and the ANOVA table is formulated only with the significant terms. Significance or effect of each term in the ANOVA table is identified by calculating the percentage of contribution and noticed that welding current has the highest significance (46%) on TSFL. The macroscopic examination confirmed that the larger nugget is observed during the maximum welding current due to the high heat generation. Also, the variation in TSFL against the process parameters are observed as same as nugget length, because, TSFL and nugget length are perfectly correlated.

In the current study, a novel laboratory experiment and a kinetic calculation were proposed to analyze the modification behavior of alumina inclusions in the molten steel. To obtain the shape and composition of a single Al₂O₃ inclusion at different times during the modification process, confocal scanning laser microscopy experiments were conducted to track the evolution of the Al₂O₃ inclusion particle on the surface of Ca-treated steel. Then, the composition evolution of the Al₂O₃ inclusion particle during the modification process was predicted using a kinetic model. It was assumed the product layer was homogeneous. The diffusion of dissolved [Ca], [Al], and [O] crosses through the inclusion-steel interface was considered. Experimental results agreed well with kinetic calculated results. Meanwhile, the kinetic model was used to analyze the modification behavior of Al₂O₃ inclusions in steel with various influence factors including the [Ca] content in steel, the [Al] content in steel, and the initial size of inclusions.