The Application of Industrial Automation in the Metallurgical Industry

As the basic pillar industry of the national economy, the metallurgical industry undertakes the heavy responsibility of metal raw material production and processing. However, the problems of traditional metallurgical processes relying on manual experience, high energy consumption and low efficiency are becoming increasingly prominent. With the rapid development of industrial automation technology, the deep integration of technologies such as artificial intelligence, the Internet of Things, and big data with metallurgical production is driving the industry towards intelligence, greenness, and efficiency. This article will explore the core application scenarios, technical advantages, and future trends of industrial automation in the field of metallurgy.





1. Current status of automation transformation in the metallurgical industry

Modern metallurgical processes cover complex links such as ore crushing, roasting, smelting, refining, continuous casting, and rolling. In the past decade, more than 70% of large steel mills worldwide have introduced automated control systems, and the average automation rate of Chinese steel companies has increased from 35% in 2015 to 68% in 2022 (data from the China Iron and Steel Association). This transformation not only reduces manual dependence, but also increases production efficiency by 20%-40%, becoming a key driving force for the sustainable development of the industry.



II. Core applications of industrial automation in metallurgical production

1. Intelligent smelting: from "experience-driven" to "data decision-making"

- Process optimization: By deploying distributed control systems (DCS) and IoT sensors, real-time monitoring of parameters such as temperature, pressure, and composition in the blast furnace, combined with AI algorithms to dynamically adjust the fuel ratio and blast volume, the fluctuation range of silicon content in molten iron is reduced from ±0.5% to ±0.1%.

- Environmental protection upgrade: The exhaust emission monitoring system is linked to the dust removal equipment to achieve real-time early warning and automatic regulation of pollutant emissions. A Baosteel case shows that the concentration of nitrogen oxide emissions has decreased by 35% year-on-year.

2. Continuous casting and rolling: unmanned and precise control of the entire process

- Continuous casting: The machine vision system automatically detects surface defects of steel billets with an accuracy of microns and a missed detection rate of less than 0.01%; the vibration frequency of the crystallizer is automatically matched according to the type of steel, and the qualified rate of the casting billet is increased to 99.8%.



- Rolling process: AI model predicts the rolling force and width of plate and strip materials, reduces roller wear by 20%, and controls the product thickness tolerance within ±3μm, reaching the international advanced level.



3. Intelligent quality inspection: Say goodbye to "manual visual inspection"

- Nondestructive testing: The X-ray flaw detection system based on deep learning can identify 0.1mm internal defects, and the detection speed is 5 times faster than the traditional method; the laser scanner measures the three-dimensional morphology of steel in real time, and the size deviation automatically alarms.



- Traceability management: Blockchain technology opens up the production data chain and realizes the full life cycle traceability from raw materials to finished products. A case of Handan Iron and Steel shows that the quality complaint rate has dropped by 60%.



4. Energy management and green production

- Smart Energy Network: Through the industrial Internet platform, the energy data of electricity, gas, steam and other energy in the factory area are integrated to optimize the scheduling plan. A Shougang Park saves 120,000 tons of standard coal per year.



- Carbon emission reduction: The capture rate of AI-driven carbon capture system exceeds 90%. With the waste heat recovery technology, carbon emissions per ton of steel decreased by 15% year-on-year.



III. Industry transformation under technology empowerment



Double leap in efficiency and quality



Automated production lines shorten the switching time of production lines to less than 15 minutes (traditional processes require 2 hours). A case of Zhanjiang Steel shows that its hot rolling rhythm has been accelerated to 120 meters per minute, setting a world record. At the same time, the product qualification rate has increased from 96% to 99.5%, and the proportion of high-value-added products such as high-end automotive plates and aviation aluminum has increased by 18%.





Significant optimization of cost and safety

After a private steel plant introduced a predictive maintenance system, the downtime of equipment failures was reduced by 70%, and the cost of spare parts consumption was reduced by 40%. Automated remote control technology has reduced the density of personnel in high-temperature and high-risk positions by 60%, and the safety accident rate has decreased by 85% year-on-year.



IV. Challenges and Countermeasures

Despite significant results, metallurgical automation still faces three major bottlenecks:

1. Technical integration problems: Insufficient fusion of multi-source heterogeneous data. A steel plant once delayed production by 3 hours/day due to poor interface between MES and ERP systems.



- Countermeasures: Formulate industry standard data interface protocols to promote the construction of industrial Internet platforms.



2. Shortage of compound talents: Engineers with both metallurgical knowledge and AI programming capabilities account for less than 5%.



- Countermeasures: Schools and enterprises jointly build "intelligent manufacturing training bases" and carry out targeted training programs for "digital craftsmen".



3. High initial investment pressure: The average payback period for automation transformation is as long as 5-7 years.



- Countermeasures: The government provides a 20% special subsidy, and banks launch low-interest financial products such as "technical transformation loans".





V. Future Outlook: Towards a New Era of "Digital Twin + Metaverse"



1. Digital Twin Factory: Build virtual mirrors of blast furnaces and converters, optimize process parameters through simulation, and a study shows that the commissioning cycle of new production lines can be shortened by 40%.



2. AR remote collaboration: Experts guide on-site operations through 5G+AR glasses, and the response time for solving complex equipment failures is compressed from several hours to 15 minutes.



3. AI autonomous decision-making: The intelligent scheduling system based on reinforcement learning realizes autonomous optimization of the entire process, and a certain experimental steel plant has achieved an accuracy rate of 98.7% in predicting the carbon content at the end of steelmaking.



Conclusion

Industrial automation is reshaping the DNA of the metallurgical industry. From "steel behemoths" to "smart factories", technological innovation not only brings a leap in production efficiency, but also promotes the deep integration of green manufacturing and high-end manufacturing. In the future, with the continuous breakthroughs in technologies such as AI, blockchain, and digital twins, the metallurgical industry will surely move towards a new chapter of greater intelligence, low carbon, and sustainability, injecting strong momentum into China's strategy of building a strong manufacturing country.