The ironmaking front-end processes , encompassing the raw material yard , sintering , pelleti- zing , coking , and blast furnace ironmaking , constitute the most energy-intensive and carbon-emitting segment of steel manufacturing. Consequently , the level of intelligence achieved within these processes directly determines the overall competitiveness and sustainability of the entire production chain. Taking industrial big data empowerment as the central narrative , this paper systematically reviews the nearly fifty_year evolution of digital and intelligent technologies in ironmaking front_end processes. This evolu_ tion is delineated into four distinct stages : automation enlightenment , digital transformation , intelligent upgrading , and whole_process collaborative breakthrough. From this historical analysis , three overarc_ hing developmental trajectories are distilled : the hierarchical advancement of control architecture , the paradigm shift in modeling approaches , and the progressive evolution of decision_making modes. Build_ ing upon this foundation , the paper provides an in_depth examination of the current applications of in_ dustrial big data and artificial intelligence technologies across the five core unit operations , elucidating the fusion mechanisms between data_driven methods and mechanistic models under diverse operational scenarios. Through a systematic comparison of domestic and international technological pathways , the paper identifies existing limitations regarding the depth of data_mechanism_scenario integration and the breadth of cross_process coordination. Looking forward ,  future innovation directions are envisioned from four critical dimensions : industrial big data technologies , research on large foundation models , smart manufacturing technologies , and the synergistic integration of green and low_carbon strategies. This review aims to provide a systematic reference for both theoretical research and engineering practice in the intelligent manufacturing of ironmaking front_end processes ,  thereby supporting the high_end , intelligent , and green transformation of the steel industry.

sadvantages in different The construction of standards in the steel industry, as a crucial support for industrial transformation, is of great significance in promoting high-quality industrial development and enhancing competitiveness. To improve the query efficiency and usage accuracy of steel industry standard knowledge and promote the in - depth integration of standard implementation and technological innovation, a research on the development of an intelligent question-answering platform based on retrieval augmented generation (RAG) technology was conducted. 60 representative steel industry standards are selected as the basic data source. The DeepSeek-R1 model was used to parse the standard texts and extract knowledge, and a knowledge graph of steel industry standards was constructed to provide structured knowledge support for intelligent question - answering. Under the LangChain development environment, two different architectures of question - answering system solutions, GraphRAG and LightRAG, were designed and implemented. The system performance was evaluated from multiple dimensions such as query accuracy, comprehensiveness of answer scope, and depth of technical analysis.The research results show that the two question-answering systems have their own advantages and dievaluation indicators. The GraphRAG system performs excellently in handling complex knowledge queries and associative reasoning, and can provide more in-depth and comprehensive answers. In contrast, the LightRAG system has certain advantages in query response speed and simple question processing.The intelligent question-answering platform developed effectively improves the query efficiency and usage accuracy of steel industry standard knowledge, and provides powerful tool support for the implementation of industry standards and technological innovation. In the future, the construction of the knowledge graph and the architecture of the question-answering system can be further optimized, and by integrating more domain data and advanced technologies, the intelligent development of the steel industry can be promoted.

At present, a new wave of technological revolution is emerging globally, represented by industrial technologies such as artificial intelligence, big data and industry 4.0. The traditional chemical plant control model can no longer meet the needs of digital manufacturing. Typical problems such as complex production processes and difficulty in collaborative operations in the steel industry have gradually emerged. Enterprises also have uneven information levels and serious information island phenomena. In this regard, the factory and production line based on emerging technologies such as digital twin technology, internet of things, machine learning and video intelligent recognition, accesses video images, sensor monitoring data, system control data, external drawing information and management systems, and the status of production line equipment and materials in real time were established, visual linkage of data information within the production line was displayed, and information islands were eliminated. At the same time, artificial intelligence algorithms were used to merge and splice the complex and scattered video images, and splice the production line monitoring images. For large-scale key areas, real-time monitoring was achieved in the form of "one picture".

The connection and scheduling of the casting-rolling interface is an important factor affecting the production efficiency and economic benefits of iron and steel enterprises. For the continuous casting and hot rolling production site of a steel enterprise, this paper established a multi-agent simulation model of the casting-rolling interface, and simulated the complete production and transportation process on the production site by designing the roles and tasks of various agents and the cooperative interaction mechanism between agents. This paper simulated and analyzed a variety of different production rhythms by multi-agent model, and studied the precise matching between different production rhythms of continuous casting and different production rhythms of hot rolling on the production line. The simulation results show that the model can accurately reflect the actual production process, which verifies the effectiveness of the simulation model. According to the simulation model, as the interval time between the different streams of the casting machine increases, the conveying time of the hot slab in the process of traversing trolley and the total conveying time of the hot slab showed a trend of first decreasing and then rising. and reached their lowest when the interval time between the different streams of the casting machine was 2 minutes. When only producing hot billets, with the increasing of the casting speed, the average total conveying time increases, the total production time decreases, and the average reheating time increases first and then decreases after the casting speed above 1.9m/min. As a result of the optimization, the average reheating time was reduced to 95.7% and the average total conveying time was reduced to 68.2% of the pre-optimization level.

Blast furnace burden distribution is one of the crucial aspects of blast furnace ironmaking. Due to the airtight nature of the top loading equipment, it is not possible to intuitively and accurately observe the actual distribution of furnace burden in the furnace throat, such as the distribution of burden layers and ore-to-coke ratio. During production, blast furnace operators typically rely on the distribution of CO₂, coal gas temperature, or gas flow rate in the coal gas at the furnace throat for upper adjustments. With the advancement of detection technology, operators can utilize infrared cameras, thermal imaging, laser technology, and radar technology to perform more precise upper adjustments. However, these technologies are only capable of capturing information pertaining to the surface of the furnace material, and they fail to provide a detailed understanding of the distribution information regarding the material layer and ore-to-coke ratio. This article is based on the mathematical model of blast furnace burden distribution, and comprehensively applies cutting-edge technologies such as numerical simulation, supercomputing, and artificial intelligence. Through big data cloud platforms and intelligent neural networks, an intelligent monitoring system for the distribution of the entire blast furnace burden layer is constructed. This system can achieve online tracking and simulation of the material distribution process, assisting blast furnace operators in understanding the distribution of furnace materials. After the application of the system, various economic and technical indicators of the blast furnace have been significantly improved. Compared with the past, the monthly output has increased by an average of 4.7%, the fuel ratio has decreased by 2.37kg/tFe, and the vanadium recovery rate is all above 80%.

The prediction of the endpoint carbon content and molten steel temperature in converter steelmaking is of great significance for the precise control of molten steel composition and temperature and the improvement of product quality. Due to the high dimension, high noise and strong nonlinearity of the converter steelmaking process data, it is difficult to obtain high-quality data. Direct modeling not only has a low hit rate but also is prone to overfitting. To address this problem, this paper proposes a random forest prediction model based on adaptive SMOTE data enhancement technology was proposed. Firstly, the feature selection of the endpoint carbon content and molten steel temperature iswas performed by recursive elimination method. Secondly, the adaptive SMOTE algorithm iswas used to enhance the original data. Finally, random forest iswas used to predict the endpoint carbon content and molten steel temperature respectively. The actual industrial data shows that the prediction hit rate of the end-point carbon content within the target error value ±0.02% is 88.9%, and the prediction hit rate of the end-point molten steel temperature within the target error value ±20°C is 92.3%, which is significantly improved, and provides a reference for end point prediction and control of converter steelmaking.

In the sintering process, accurate prediction of the sintering burn-through point is essential to control the operation of the sintering machine, as it determines the quality of the sintered material and the efficiency of energy use. However, the current common sintering burn-through point prediction models mainly focus on single-step prediction, and they tend to ignore the spatio-temporal characteristics of the sintering process data. In view of the multi-step prediction task requirements of the sintering burn-through point and the complex spatio-temporal characteristics of process data, a multi-step prediction model of the sintering burn-through point based on encoder-decoder architecture was proposed. The spatio-temporal encoder-task decoder architecture effectively extracts the spatio-temporal features of sintering process data by stacking temporal convolutional networks and spatial attention mechanism, and adopts a task-specific guidance decoder to correspond to each step of prediction in the form of independent units. The effectiveness of the proposed method was verified by an actual sintering industrial process case. This study can assist operators to obtain information about the future state of the sintering process in advance, so as to adjust the operating parameters more accurately, reduce the quality fluctuation and energy waste caused by the lag, and improve the quality stability of the sinter.