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.