According to the Zhitong Finance APP, China Galaxy Securities has released a research report stating that it believes macro policies continue to take effect, which is beneficial for enhancing the concentration of the steel industry and improving the long-imbalanced supply-demand relationship. Leading players in the rebar sector are expected to benefit. Meanwhile, with the transformation and upgrading of the manufacturing industry, the demand for medium and high-end special steel is expected to see rapid growth. DeepSeek's rise further promotes the evolution of IDC towards AIDC, amplifying the advantages of steel enterprises. It is recommended to pay attention to: AI actively empowering, large models accelerating the refined implementation or initial results in multiple links and scenarios of steel production and supply chains, as well as existing related external operations or involvement in telecommunications business, orderly expanding IDC, and other targets.
<h2>The main points of China Galaxy Securities are as follows:</h2>
DeepSeek creates ripples, AI + steel is imperative.
Under the condition that DeepSeek's performance is on par with similar models, the deployment and operating costs have been significantly reduced, increasing the possibility of AI applications flourishing and empowering various industries. Currently, the steel industry is facing a turning point in &#34;quantity&#34; structural adjustment and challenges in &#34;price&#34; leading to profit losses. Among many industries, steel is a typical &#34;black box&#34; scenario due to the complex and variable physical and chemical reactions involved in production and the strongly coupled process flows with multiple variables, as well as the existence of information silos between equipment, making it difficult to accurately grasp the key factors and interrelated mechanisms in each link. Therefore, AI + steel is imperative.
AI reshapes the steel production process, building a bridge for supply-demand matching.
For the ironmaking process, AI focuses on optimizing blast furnace ironmaking technology, diagnosing and warning of blast furnace faults, managing blast furnace gas energy efficiency, guiding blast furnace operations, digital twins, and virtual blast furnaces. For the steelmaking process, AI focuses on scheduling and command of cranes, predicting and optimizing steel temperature and composition, controlling the continuous casting process, monitoring equipment status and fault warnings, managing and optimizing energy, predicting and tracing quality, and guiding steelmaking operations. For the rolling process, AI focuses on optimizing the rolling process, controlling plate shape, maintaining equipment and predicting faults, monitoring and predicting quality, and planning and scheduling production. In summary, through case studies, it is expected that after introducing AI technology, the production cycle of steel enterprises will be shortened by about 30%, output will increase by about 25%, defect rates will decrease by about 3%, the fluctuation range of performance indicators such as strength and toughness will be reduced by about 30%, energy consumption will decrease by about 15%, raw material utilization will increase by about 7%, equipment downtime will be reduced by about 33%, and equipment maintenance costs will decrease by about 30%. AI can optimize the steel supply chain mechanism from aspects such as demand forecasting and inventory management, supplier selection and procurement decisions, logistics distribution and transportation routes, building a bridge for supply-demand matching. In the research and development of new steel grades, AI can shorten the R&amp;D cycle by about 30% and reduce R&amp;D costs by about 25%, with significant AI + effectiveness.
AI empowers steel cases emerge, quality and efficiency improvement effects are significant.
In December 2024, the Ministry of Industry and Information Technology released the &#34;List of Typical Application Cases of Artificial Intelligence Empowering New Industrialization,&#34; with 14 cases from the steel industry listed, accounting for 9.3% of the entire industry. Among them, there is 1 case in the technology foundation direction, 11 cases in the industry application direction, and 2 cases in the equipment product direction. For example, Baosteel's Meigang has built a 4,070 cubic meter smart blast furnace, leading a new model of low-carbon intelligent ironmaking. Baotou Steel's smart hot rolling is in full swing, and the group has partnered with Huawei to open a new chapter in digital transformation. Valin Steel's Xianggang has implemented the world's first steel AI large model, steadily achieving full-link upgrades
Nangang Steel Co., Ltd. is building &#34;One Brain and Three Centers,&#34; focusing on agile operations + green low-carbon + data value + key scenarios + ecological development. Angang Steel's &#34;One Furnace, One Strategy&#34; visualization and comprehensive evaluation of group furnace conditions are emerging in abundance, with significant improvements in production efficiency.
Risk Warning: Risks of cyclical fluctuations in the macroeconomic environment; risks of AI + steel collaboration falling short of expectations; risks of rising upstream raw material prices; risks of slow recovery in downstream demand, etc

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China Galaxy Securities released a research report indicating that AI technology is reshaping the steel production process, enhancing production efficiency and quality. The continued effects of macro policies are helping to improve the supply-demand relationship in the steel industry, and it is expected that leading players in the rebar sector will benefit. The application of AI in processes such as ironmaking, steelmaking, and rolling can optimize processes, improve energy efficiency, and enhance quality forecasting, driving the transformation and upgrading of steel enterprises. It is recommended to pay attention to investment targets related to AI and telecommunications businesses

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China Galaxy Securities: AI reshapes the steel production process, significantly improving quality and efficiency