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300+ AI Applications Delivering Surges in Core Metrics and Annual Cost Reduction of Over CNY100 Million (Approx. US$15 Million)

NANJING, CHINA – Media OutReach Newswire – 26 October 2026 – 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.

As a traditional manufacturer navigating the challenges of transformation and cost efficiency, NISCO has built a robust digital and intelligent foundation by adopting industry-leading products including Ascend, Kunpeng, Huawei Cloud Stack, and Pangu models. By embedding AI into every core stage of production—ironmaking, steelmaking, rolling, and quality inspection—NISCO is forging green and high-end AI-powered super steel plants.

Unlike general-purpose monolithic models, YuanYe innovatively 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 CNY100 million (approx. US$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: YuanYe redefines traditional operations for significant 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 CNY600,000 (approx. US$90,000) in annual economic benefits—securing both environmental compliance and drastic cost reduction.

From automating labor to empowering people: 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.

As 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.

Hashtag: #Huawei #NISCO

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The company’s stock fell to HK$97.55 during morning trading, marking its steepest intraday decline in two months. Investors reacted to indications that Beijing could intensify scrutiny of online marketplaces, potentially increasing compliance costs for Alibaba and its competitors.

China’s State Administration for Market Regulation disclosed on Wednesday that it had convened a meeting in Beijing with representatives of leading digital commerce businesses to discuss improving product standards, strengthening consumer safeguards and addressing shortcomings in platform governance.

Shares of JD. com dropped approximately 4.7%, while Kuaishou declined more than 4%. Other technology companies, including Tencent, Meituan, Xiaomi and NetEase, also suffered losses as selling spread across the sector.

Hong Kong’s Hang Seng Index declined nearly 3%, touching a three-month low, while the Hang Seng Tech Index dropped more than 4%, reaching its weakest level since September 2024. The technology benchmark has lost more than 30% this year.

The regulatory announcement focused on persistent problems involving counterfeit merchandise, misleading advertising and products failing to match descriptions provided by online sellers. Authorities identified livestream shopping, instant retail services and social commerce as areas where product safety risks have become increasingly complex.

The regulator said platforms must strengthen their responsibilities as gatekeepers, establish comprehensive quality management systems and improve supervision throughout commercial supply chains. Companies were instructed to align their practices with existing regulatory guidance and the government’s 2026 quality improvement priorities.

Officials also emphasised greater transparency, improved aftersales services and more effective mechanisms for resolving consumer complaints. The measures seek to strengthen accountability among platform operators without necessarily introducing an entirely new regulatory framework.

Representatives from Alibaba’s Taobao and Tmall businesses attended the meeting alongside JD. com, Pinduoduo, Xiaohongshu, Kuaishou, Douyin, Vipshop and other online trading platforms.

The gathering followed guidance issued last year by ten government departments, including the market regulator, the Cyberspace Administration of China and the Ministry of Industry and Information Technology, concerning product and service standards across digital marketplaces.

Authorities acknowledged that companies had implemented improvements under those guidelines but said substantial challenges remained. Emerging business models were creating additional difficulties for regulators attempting to identify unsafe products, deceptive marketing and inadequate consumer safeguards.

For Alibaba, the renewed regulatory emphasis directly affects its domestic commerce operations, particularly Taobao and Tmall, which connect merchants with consumers across China’s vast online retail market.

Stricter monitoring requirements could oblige marketplace operators to devote additional resources to merchant verification, product inspections, complaint handling and supply chain oversight. The financial implications will depend on how existing requirements are implemented and enforced.

The announcement did not disclose fresh financial penalties against Alibaba or establish a timetable for additional enforcement measures. The market reaction therefore reflected concerns about possible regulatory consequences rather than confirmation of sanctions against the company.

Broader investor caution compounded the selling pressure. Mainland investors had reduced their exposure to Hong Kong equities during the preceding trading session, while technology valuations remained under pressure amid questions surrounding substantial expenditure on artificial intelligence infrastructure.

Alibaba has committed significant capital to expanding its computing capacity and artificial intelligence operations, making investor assessments of profitability and spending particularly important alongside regulatory developments.

The company completed an HK$80 billion share placement in August, allocating approximately 60% of net proceeds towards expanding global computing infrastructure and the remainder towards hyperscale artificial intelligence data centres and upgrades to cloud systems.

That financing underlined Alibaba’s commitment to technological investment even as its established ecommerce businesses face heightened expectations concerning operational standards and consumer safeguards.

The regulator also encouraged companies to use commercial data to improve product quality among suppliers, linking information gathered through online transactions with manufacturing improvements and better supply chain coordination.

Another proposal involved developing and publishing platform quality governance indices, intended to provide consumers with clearer information about standards maintained by different marketplaces.

Officials urged participating companies to strengthen cooperation with regulators and other stakeholders while making information about products and services more transparent.

The meeting included discussions of the ISO 9001 quality management framework and methods for businesses to assess their own governance arrangements. Platform representatives exchanged views on implementing quality improvements and addressing weaknesses identified by the authorities across online trading activities.