Overview of technology evolution and application of artificial intelligence in aluminum industry

The aluminum industry is at a critical juncture of transitioning from experience-driven to cognitive intelligence, and it is confronted with common technical challenges such as the unpredictability of ″black boxes″ under extreme working conditions, strong coupling of multiple physical fields, and time-varying raw materials. This article reviews the technological iteration and development paths of artificial intelligence technology in the three core processes of alumina production, electrolytic aluminum smelting and casting processing. Studies indicate that in the alumina process, artificial intelligence-related technologies have made a leap from optimization regression to mechanism-constrained soft measurement and edge-cloud collaborative architecture, effectively alleviating the physical inconsistency and control lag problems of pure data models. In the electrolytic aluminum process, in response to challenges such as uneven spatial distribution of large pre-baked cells and cell control black boxes, the diagnostic paradigm has evolved from single-time series analysis to high-dimensional spatio-temporal topology perception. The related technologies have achieved transparent mapping of unmeasurable states such as the shape inside the furnace. In the aluminum casting processing stage, the machine vision based on the YOLO algorithm and the physically guided AI model have effectively improved the efficiency of surface quality inspection and process optimization. Finally, this paper looks forward to the new era of cognitive intelligence centered on industrial large models, and analyzes deep-seated challenges such as ″semantic islands″, ″physical consistency″ of models, and the crisis of interpretability, providing theoretical references and technical guidance for building a new green, low-carbon, and intelligent aluminum industry ecosystem.