Explore the vast range of titles available.

During high-speed continuous casting, the effect of secondary cooling on the quality of the billet is more obvious, and the possibility of quality problems is higher. This study takes the continuous casting machine producing 165 mm × 165 mm billet as the research object. The six different secondary cooling schemes are designed with the 4.5 m/min casting speed. By comparing the shell deformation, surface stress, surface temperature recovery, and metallurgical lengths at different secondary cooling schemes, the secondary cooling scheme applied in the high-speed continuous casting process is selected. The results show that the surface temperature recovery and stress of the billet in Scheme 1 are higher than those of other cooling schemes. The metallurgical length and shell deformation of the billet in Scheme 4 are higher than those of other cooling schemes. In Schemes 2, 3, 5, and 6, the shell deformation of Schemes 3 and 5 is higher than that of Schemes 2 and 6. In Schemes 2 and 6, the maximum surface central stress of the continuous casting billet in the secondary cooling zone is 36.3 MPa and 31 MPa, respectively. Scheme 6 is used as the secondary cooling scheme of high-speed continuous casting in this study. The water quantity in secondary cooling zone 1 ~ 5 segments of Scheme 6 is 28 m3/h, 31 m3/h, 16 m3/h, 10 m3/h, and 6 m3/h, respectively. Finally, the industrial trial is carried out, which proves that Scheme 6 can be applied to high-speed continuous casting.

To address challenges such as the high variability of variables and difficulties in feature extraction during the casting process of the continuous casting machine’s sector segment, a fault diagnosis method based on SG-PCA-LSTM is proposed. This method aims to overcome the issue of fault features obscured by noise in the total tension time series by employing the SG smoothing algorithm for filtering and denoising. By leveraging the inter-segment data correlation and the advantage of PCA in extracting fault feature information, combined with the powerful learning capability of LSTM in modeling, a Principal Component Analysis - Long Short-Term Memory (PCA-LSTM) fault diagnosis model is established. Through comparative analysis against different diagnostic methods in terms of recognition rate, false positive rate, etc., the results obtained by this method are compared against those obtained by using other algorithms. Experimental results demonstrate that the proposed method exhibits good overall performance in terms of accuracy and training time.

Frequent delays will be experienced in the start-up of molten steel on the converter equipment during the steelmaking–continuous casting (SCC) production process due to the untimely supply of molten iron or scrap, which may cause conflicts between adjacent heat on the same equipment or in the same casting. The casting machine is cut off, resulting in the failure of the static scheduling plan. SCC production ladle re-scheduling is based on the premise that the production process path remains unchanged, the operation of adjacent heat on the converter and refining furnace does not conflict, and the casting of adjacent heat within the same casting is continuous. The ladle re-scheduling of steelmaking and continuous casting production aims at continuously casting many charges with the same cast and avoiding conflicts of adjacent charges on the same machine. This mechanism proposes a method of ladle re-scheduling in the production process of steelmaking–refining–continuous casting, which is divided into two parts: plan re-scheduling and ladle optimisation scheduling. Firstly, a re-scheduling optimisation model of the steelmaking and continuous casting production is built. This model aims at minimising the waiting time of all charges. The re-scheduling strategy of steelmaking and continuous casting production is proposed by interval processing time of charges and scheduling expert experience. This strategy is composed of two parts: re-scheduling charge decision and charge processing machine decision. Then, the first-order rule learning is used to select the optimisation target to establish the ladle optimal scheduling model. The ladle matching rules are extracted on the basis of the rule reasoning of the minimum general generalisation. The ladle optimisation scheduling method that consists of the optimal selection of the ladle and the preparation of the optimal path of the ladle is proposed. Ladle selection is based on the production process and adopts rule-based reasoning to select decarburised ladle after choosing dephosphorised ladle. Ladle path preparation, which is a multi-priority heuristic method, is designed to decide the path of the ladle from the converter to the refining furnace to the continuous casting machine. Finally, this mechanism was actually verified based on the large-scale data of a steel company in Shanghai, China. Results showed that the production efficiency of steelmaking-refining-continuous casting was improved.

Results of computational experiments on studying multiphase flow and interphase interaction in the volume of an industrial 50-ton tundish of a continuous caster, including when blowing with argon through bottom blowing blocks, are presented. A mathematical model has been developed that makes it possible to predict the dynamics of melt mixing at a given casting speed. The pattern of influence of the layout of the ladle’s working space on the area of “stagnant zones” in the ladle volume has been found.

The iron and steel industry is energy-intensive due to the large volume of steel produced and its high-temperature and high-weight characteristics, sensors such as high-temperature application sensors can be utilized to collect production data and support the process control and optimization. Steelmaking-refining-continuous casting (SRCC) is a bottleneck in the iron and steel production process. SRCC scheduling problems are worldwide problems and NP-hard. The problems are not only important for iron and steel enterprises to enhance production efficiency, but also play a significant role in saving energy and reducing resource consumption. SRCC scheduling problems can be modeled as hybrid flowshop scheduling problems with batch production at the last stage. In this paper, a Discrete Brain Storm Optimization (DBSO) algorithm is proposed to handle SRCC scheduling problems. In the proposed DBSO, population initialization and cluster center replacement are specially designed to enhance the intensification abilities. Moreover, a perturbation operator is devised to enhance its diversification abilities. Furthermore, a new individual generation operator is devised to improve the intensification and diversification abilities simultaneously. Experimental results have demonstrated that the proposed DBSO is an efficient method for solving SRCC scheduling problems.

The φ16 mm single-crystal copper rod billet was prepared by the heated mold horizontal continuous casting process. After cold drawing + 600 °C× 5 s annealing to φ1 mm, the annealed φ1 mm single-crystal copper processing wire was cold drawn to φ0.2 mm (φ1 mm → φ0.2 mm), and the electrical conductivity, tensile strength and microstructure evolution of single-crystal copper wire were compared and analyzed. The research shows that the conductivity of the as-cast single-crystal copper rod is 102.1% IACS, the tensile strength is 141 MPa, the conductivity is as high as 97.26% IACS, after cold drawing to φ0.2 mm, and the tensile strength is greatly increased to 506 MPa. Compared with the as-cast properties, the electrical conductivity of the as-drawn wire is only reduced by 4.7%, while the tensile strength is increased by 258.9%. The as-cast rod exhibits typical characteristics of single-crystal copper; with the increasing amount of deformation, the microstructure evolves in the following form: dislocations generated by slip entanglement into dislocation cells → microstrip structure → layered structure → twin structure. A prediction model for the strength and electrical conductivity of single-crystal copper wire was constructed. The results show that grain refinement strengthening and dislocation strengthening are the key factors affecting the strength and conductivity of single-crystal copper wire, when deformed to φ0.2 mm, and twinning strengthening is superimposed in the above strengthening mechanism.

The influence of the electromagnetic mixing (EMM) of liquid metal in the mold of the continuous casting machine on its thermal work, the formation of the structure and properties of cast billets are investigated. The influence of the current and frequency of EMM on the mechanical properties, macro-and microstructure of round and square sorted billets was established.

To achieve the desired superheat of molten steel during the continuous casting process, optimization of process parameters such as molten steel temperature in ladle furnace, casting speed, and baking temperature is necessary. Therefore, obtaining the superheat corresponding to these process parameters in advance is particularly important. To address this issue, a model for predicting the temperature of molten steel in the tundish during continuous casting is designed. The model adopts a combined modeling approach of mechanistic model and data model. To address the issue of the mechanism model’s inability to capture the variation of the lining’s thermal parameters, this article improves the traditional physics-informed neural network (PINN) algorithm. It combines the constraints from both the forward and inverse problems, allowing for obtaining solutions to the equations while capturing the variation of equation parameters. Actual data from multiple casting sequences at a steel plant are collected to validate the accuracy and interpretability of the model. The results show that the error of the model is about 2.1k which has better accuracy compared to pure mechanistic model and pure data model. Additionally, it can capture the variation patterns of tundish lining thermal parameters under different operating conditions. Therefore, the model designed in this article can provide both profound physical interpretation ability and more practical predictions of molten steel temperature.

Multi-position refining furnaces are a critical strategy for energy-intensive industries to meet its demands of fast-paced production. In most literature, however, they serve only as a buffer, holding up to at most two ladles to maintain the proper temperature of ladles. These studies do not take full advantage of them, nor do they study the production scheduling of energy-intensive enterprises with multi-position refining furnaces under time-of-use (TOU) tariffs. Therefore, this paper presents a steelmaking-continuous casting (SCC) scheduling problem with multi-position refining furnaces under TOU tariffs. We firstly develop a mixed integer nonlinear programming (MINLP) model with the goals of minimizing jobs completion time, machines idle time, and total electricity costs, which subjects to the double-position characteristics and other process constraints. Owing to the complexity between the time-slots of TOU tariffs and the processing cycles of jobs, we design an intermediate function to calculate objectives efficiently. Furthermore, a Lagrangian relaxation (LR) algorithm based on a subgradient algorithm is utilized to solve the proposed model, and an interior point algorithm is adopted to solve sub-problems obtained by job-level and batch-level decomposition, whose solution approximates optimality comparing to GUROBI solver. The computational results demonstrate that the solution of job-level decomposition algorithm approximates the optimal scheduling scheme in an acceptable time and is superior to that of GUROBI solver. In addition, double-position instance can find a better scheduling scheme than nondouble-position one.

In the casting and rolling production process, surface longitudinal cracks are a typical casting defect. Tracing the causes of longitudinal cracks online and controlling the key parameters leading to their formation in a timely manner can enhance the stability of casting and rolling production. To this end, the influencing factors of longitudinal cracks were analyzed, a data integration storage platform was constructed, and a tracing model was established using empirical rule analysis, statistical analysis, and intelligent analysis methods. During the initial production phase of a casting machine, longitudinal cracks occurred frequently. The tracing results using the LightGBM-SHAP method showed that the relative influence of the narrow left wide inner heat flow ratio of the mold was significant, followed by the heat flow difference on the wide symmetrical face of the mold and the superheat of the molten steel, with weights of 0.135, 0.066, and 0.048, respectively. Based on the tracing results, we implemented online emergency measures. By controlling the cooling intensity of the mold, we effectively reduced the recurrence rate of longitudinal cracks. Root cause analysis revealed that the total hardness of the mold-cooling water exceeded the standard, reaching 24 mg/L, which caused scaling on the mold copper plates and uneven cooling, leading to the frequent occurrence of longitudinal cracks. After strictly controlling the water quality, the issue of longitudinal cracks was brought under control. The online application of the tracing method for the causes of longitudinal cracks has effectively improved efficiency in resolving longitudinal crack problems.