From "relying on experience to find minerals" to "AI-precise modeling," and from "relying on touch to smelt" to "intelligent closed-loop control"—the Chinese non-ferrous metals industry is undergoing a deep transformation driven by artificial intelligence. On December 26, the China Non-Ferrous Metals Industry Association and Aluminum Corporation of China (Chinalco) jointly launched the industry's first large model, "Kun'an" 2.0, marking a new phase in the digital and intelligent transformation of China's mineral resources sector, shifting from pilot exploration to large-scale implementation.

 "Kun'an" 2.0: More than an upgrade—it's a full-chain intelligent restructuring

As the first AI large model introduced in the industry in 2024, "Kun'an" 2.0 has achieved a comprehensive enhancement in technical capabilities, scenario coverage, and ecological collaboration. According to Duandongxiang, Chairman of Chinalco, "This is not just an update but also a deeper understanding and expansion of the ecosystem." Currently, AI has been deeply integrated into four core stages of the non-ferrous metals industry: geological exploration, mining, smelting, and recycling, driving traditional processes toward data-driven, intelligent decision-making, and self-optimization.

Since 2024, Chinalco has advanced over 100 AI application scenarios, and this time officially released 52 high-value scenarios, while building 8 industry-grade data sets covering key areas such as ore body identification, energy consumption optimization, equipment predictive maintenance, and improved metal recovery rates.

 From "single-point breakthroughs" to "systematic transformation": New productive forces are accelerating formation

Honglin Ge, President of the China Non-Ferrous Metals Industry Association, emphasized at the launch event: "The empowerment of digital and intelligent technologies in the non-ferrous metals industry holds great potential and opportunities." Driven by policy guidance and internal industry demand, the sector has formed a development pattern characterized by "technological leadership, scenario breakthroughs, and ecological collaboration." Typical examples include:

- Smart Exploration: AI integrates remote sensing, geological, and drilling data, increasing the accuracy of ore body prediction by 30%;

- Intelligent Smelting: An AI control system based on real-time furnace conditions reduces energy consumption by 8% and improves metal recovery rate by 2.5%;

- Full-Chain Visibility: A big data platform connects the entire supply chain from raw ore to finished product, achieving dynamic optimization of quality and cost.

 Association-led, Building the "Five Supports" for Industry AI Transformation

To accelerate the promotion of the "Kun'an" model, the China Non-Ferrous Metals Industry Association will play a role as a bridge between the government, the industry, and enterprises, focusing on promoting:

- Cultivating a transformation ecosystem: Establishing a collaborative platform for industry-academia-research-use;

- Targeted measures: Customizing AI paths for specific sectors like copper, aluminum, and lithium;

- Standard leadership: Developing industry-wide specifications for training, evaluation, and security of large models;

- Technology breakthroughs: Overcoming key technologies such as multi-modal perception and industrial knowledge graphs;

- Talent cultivation: Collaborating with universities to train "AI + Metallurgy" composite talents.

 AIbase Observation: Industrial Large Models Are Moving from "Concepts" to "Production Lines"

The release of "Kun'an" 2.0 marks another milestone in the practical application of vertical industry large models in China. Unlike general models that pursue parameter scale, the value of industrial large models lies in solving real production line pain points and creating quantifiable economic benefits. When AI can directly increase metal recovery rates, reduce carbon emissions, and ensure resource security, it ceases to be a technological ornament and becomes the core engine of new productive forces.