NISCO And Huawei Scale Industrial AI For Predictive Maintenance, Quality Control And Energy Efficiency
Key Highlights
- YuanYe combines multiple AI models, metallurgical mechanisms, and operational data to support precise, real-time steelmaking decisions.
- Blast-furnace models predict temperatures and identify issues up to two hours ahead with over 90% accuracy, while improving prime-grade molten iron yield by more than 10%.
- Rolling-force prediction responds in milliseconds, raises the hit rate from 88% to 98%, and helps achieve thickness tolerance of ±0.2 mm.
- Automated metallographic inspection exceeds 95% accuracy and cuts inspection time from 10 minutes to 3–5 minutes per sample.
- A gas and flue-gas sulfur-control agent predicts risks with over 95% accuracy, nearly halves natural-gas co-firing, and reduces emissions-related downtime by more than 25%.
Nanjing Iron & Steel Co., Ltd. (NISCO) has worked with Huawei and ecosystem partners to commercially deploy the world's first steel model powered by swarm intelligence—the YuanYe Steel Large Model (YuanYe)—across multiple scenarios at scale. This helps NISCO transition its core operations from relying on experience to being powered by data and intelligence collaboration.
Unlike general-purpose monolithic models, YuanYe fuses multiple AI models with the steel industry's metallurgical mechanism models to create a dual-drive, multi-model cluster architecture. This architecture features a "right brain" for probabilistic computing and a "left brain" for precise and scientific decision-making. Powered by massive amounts of sensing data aggregated from millions of operational technology (OT) data points across 26 business domains, NISCO has achieved real-time digital twin mapping within its intelligent operations center, laying solid data and scenario foundations for YuanYe. This enables the entire steelmaking process to become not only visible, but also capable of precise control and intelligent assessment. Now 100% of NISCO's key equipment is connected and digitally controlled and its new product R&D cycles have been shortened by 50%, reducing annual costs by over $15 million.
As the model continues to iterate, NISCO had launched 25 business agents by October 2026. These agents cover a wide range of scenarios, from visual quality inspection and process optimization to production scheduling and operations management, setting a new benchmark for digital and intelligent steel transformation.
Breakthroughs in four core scenarios: safety, efficiency and quality gains
In the ironmaking stage, AI makes traditionally invisible blast furnaces transparent. YuanYe leverages blast furnaces' internal sensor data and metallurgical mechanism analysis to perform real-time dynamic simulations of the ironmaking process. Assisted by furnace temperature prediction and health diagnosis models, furnace operators can predict temperatures and identify issues two hours in advance, with over 90% accuracy. This shifts furnace operations from reactive post-event remediation to proactive pre-control, increasing prime-grade molten iron yield by over 10% while significantly cutting unit energy consumption.
In the rolling stage, AI drives precise predictions and codifies expert experience to enhance the operational stability of steel rolling and secure leadership in product quality. To tackle the highly dynamic and complex operating conditions during the rolling process, YuanYe's rolling-force prediction model achieves millisecond-level response, narrowing the thickness tolerance to ±0.2 mm and reducing the steel thickness deviation to 1.9% to reach a world-leading standard. The rolling-force hit rate has jumped from 88% to 98%, supporting a steady annual output of over 2.5 million tons of steel plates and increasing the overall yield rate by 0.5%. Enhanced dimensional precision and physical-chemical stability allow NISCO's products to meet demanding steel specifications across diverse applications, ranging from mega stadiums and oil pipelines to 10,000-ton cruise ships and LNG storage tanks.
In quality inspection, the synergy between robots and AI models serves as a discerning "eagle eye," doubling metallographic inspection efficiency. During metallographic inspection for finished steel, the integrated smart metallographic inspection system built on YuanYe has achieved full-process automation, covering sample grinding and polishing, image acquisition, and smart grading. Inspection accuracy has risen to over 95%, while per-sample inspection time has been shortened from 10 minutes down to 3–5 minutes. This 80% increase in overall inspection efficiency frees human operators to focus on anomaly analysis and high-level decision-making.
In the energy and environmental control stage, NISCO leverages the YuanYe architecture to develop the S-Link Intelligent Agent for Gas and Flue Gas Sulfur Control. Integrating 602 process parameters alongside multi-modal image and knowledge capabilities, the agent achieves a smart closed loop across source prediction, process optimization, and end-of-pipe compliance. By combining mechanism inversion with time-series forecasting, it issues precise sulfur-risk warnings one hour in advance, achieving a sulfur prediction accuracy of over 95%. Operational optimization algorithms then calculate the optimal gas blending scheme within seconds. Finally, game-theoretic models dynamically allocate emission quotas, slashing natural gas co-firing consumption by nearly 50%. This has reduced downtime caused by emission exceedance by over 25%, and delivered more than $90,000 in annual economic benefits—securing both environmental compliance and drastic cost reduction.
Defining a new paradigm of inclusive AI
Li Jinyan, general manager of the Digital and Intelligent Integrated Development Department and Director of the AI Research Institute at NISCO, said: "Digital and intelligent transformation is inherently human-centric. Its purpose is not to replace people, but to unleash human potential. Our goal is to lift employees out of the endless sea of data, empowering them to direct their creative energy toward work of higher value."
To date, NISCO has deployed over 300 AI models across its plants, alongside more than 1,000 low-code agents and lightweight applications built independently by frontline employees.
Cheng Yongfu, senior industrial solution expert at Huawei's Steel & Non-ferrous BU, noted "The real value of industrial AI can only be realized when it expands from single-point applications to comprehensive integration with production, operations, and management, which can unlock sustained, scalable empowerment."
Looking ahead, as model deployment expands and computing demands continue to grow, NISCO will team up with Huawei and other partners to explore the construction of dedicated data centers for smart steel plants. Centered on the AI-native three-tier architecture (AI-native OS, AI-native factory, and AI-native enterprise) and powered by the YuanYe model, NISCO is accelerating high-quality steel development, aiming to provide a replicable and scalable benchmark for the digital and intelligent transformation of the global steel and manufacturing industries.

