Conventional process controlProcess control methods based on traditional modelingModelling 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 modelingModeling techniques in digital platforms offer a transformative solution. This work examines how advanced techniques—including thermochemical and solution chemistry calculations, artificial intelligenceArtificial Intelligence, physics-based surrogate functions, and data reconciliationReconciliation—supported by empirical data, thermodynamicThermodynamics, and kineticKinetics data, can overcome these limitations. Implementing these techniques leads to robust metalMetal production processes and consistent product quality, minimized metalMetal losses, reduced refiningRefining time, detection of uncertainties or measuring errors in materials’ composition, minimized samplingSampling requirements, and lower energy consumptionEnergy consumption. A case study on copperCopper recyclingRecycling demonstrates these benefits, showcasing the advanced process controlAdvanced Process Control method and linking it to online higher hierarchical process analysisAnalysis, such as environmental and economic assessment.