Scope
Deadline Date: 31 December 2026
The growing complexity of modern metallurgical processes, encompassing everything from pyrometallurgical and hydrometallurgical extraction to advanced alloy design and thermomechanical processing, demands a paradigm shift beyond traditional empirical and numerical methods. Challenges persist in handling multiphase flow, multi-physics coupling, non-equilibrium phenomena, microstructural evolution across scales, and plant-wide real-time optimization—areas where artificial intelligence (AI) offer transformative potential.
This Special Issue seeks to compile pioneering research at simulation and modelling, and also the intersection of AI, simulation, and metallurgical engineering, addressing critical gaps between algorithmic advancements and their practical application in the metals industry. We highlight computational strategies where AI augments, accelerates, or replaces conventional methods to achieve:
Key Focus Areas:
1. Modelling and Optimization of Metallurgical Process
2. Hybrid Numerical-AI Frameworks
3. Industrial Applications and Digital Twins
4. Fundamental Advances and Data-Driven Discovery
This issue aims to establish best practices for integrating AI into the metallurgical engineering workflow while critically examining its limitations (e.g., data quality and scarcity, model interpretability, overfitting risks, and industrial scalability). Contributions should demonstrate rigorous validation against experimental results or high-fidelity numerical benchmarks, showcasing tangible advancements in the field of simulation and modeling for metallurgical engineering.