The Potential of Multidimensional Modeling Techniques for Advanced Process Control in Metallurgical Recycling Processes

Conventional process control methods based on traditional modeling strategies are limited by their capacity to incorporate multiple objective functions and can be time-demanding during decision-making. In metallurgical processes, these limitations often result in inefficiencies and suboptimal outcomes. Advanced modeling techniques in digital platforms offer a transformative solution. This work examines how advanced techniques—including thermochemical and solution chemistry calculations, artificial intelligence, physics-based surrogate functions, and data reconciliation—supported by empirical data, thermodynamic, and kinetic data, can overcome these limitations. Implementing these techniques leads to robust metal production processes and consistent product quality, minimized metal losses, reduced refining time, detection of uncertainties or measuring errors in materials’ composition, minimized sampling requirements, and lower energy consumption. A case study on copper recycling demonstrates these benefits, showcasing the advanced process control method and linking it to online higher hierarchical process analysis, such as environmental and economic assessment.