№ 75-3 (том 1): ОБРАЗОВАНИЕ И НАУКА В XXI ВЕКЕ, Июнь, 2026
Научно-образовательные статьи

ARTIFICIAL INTELLIGENCE–DRIVEN METALLURGY: PROCESS MODELING, OPTIMIZATION AND INTELLIGENT PRODUCTION SYSTEMS

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  • Khudoymuratov Sh.J.
Khudoymuratov Sh.J.
Almalyk State Technical Institute

Опубликован 29.06.2026

Ключевые слова

  • Artificial Intelligence, Digital Twin, Machine Learning, Hydrometallurgy, Pyrometallurgy, Selective Metal Recovery, Computational Thermodynamics.

Как цитировать

Sh.J. Khudoymuratov. (2026). ARTIFICIAL INTELLIGENCE–DRIVEN METALLURGY: PROCESS MODELING, OPTIMIZATION AND INTELLIGENT PRODUCTION SYSTEMS. ОБРАЗОВАНИЕ И НАУКА В XXI ВЕКЕ, 75-3 (том 1). https://mpcareer-google.ru/index.php/journal/article/view/5890

Аннотация

The metallurgical industry faces intense pressure to optimize energy efficiency, reduce carbon emissions, and enhance critical metal circularity. This study establishes a unified intelligent framework that integrates data-driven artificial intelligence (AI) with computational thermodynamics across the metallurgical value chain. In pyrometallurgical environments, machine learning architectures—primarily artificial neural networks and reinforcement learning—transition steelmaking operations from empirical forecasting to dynamic, adaptive control, significantly improving endpoint temperature precision and emission reduction. Concurrently, in secondary hydrometallurgical circuits, embedding optimized thermodynamic speciation windows derived from FactSage modeling into predictive frameworks enables highly selective separation flowsheets. This hybrid approach achieves recovery efficiencies exceeding 98% for rare and strategic metals (Mo, Re, Dy, Te) from complex technogenic solutions and spent catalysts, while dynamically stabilizing sulfide and carbonate precipitation for high-purity base metals (Ni, Co). Digital Twin architectures provide the structural backbone for these multi-layered systems, advancing from live monitoring toward bidirectional closed-loop optimization. Ultimately, this integration demonstrates that physics-informed AI ecosystems amplify domain expertise, driving the transition toward sustainable, resilient, and fully autonomous intelligent metallurgical production plants.

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