followed by fine-tuning of process parameters such as treatment time and gas flow. As a result, unnecessary experiments under thermodynamically unfavourable conditions were avoided. This targeted approach offers substantial practical benefits. By reducing the number of exploratory experiments required, the modelling minimizes the consumption of energy, materials, and laboratory resources. It also accelerates the development cycle by enabling faster convergence toward optimal processing conditions. This is particularly important for industrial-scale applications, where energy efficiency and process optimization are critical for economic and environmental sustainability. Furthermore, the use of freely available thermodynamic modelling tools such as Perple_X democratizes access to advanced process design capabilities, enabling research laboratories with limited experimental infrastructure to efficiently design and optimize reduction processes. Overall, the agreement between thermodynamic predictions and experimental results confirms the reliability of the modelling approach and highlights its value as an effective tool for accelerating the development of sustainable recovery processes for iron and titanium from BR and other complex industrial residues. 5. CONCLUSIONS This study demonstrates that thermodynamic modelling using Perple_X provides a powerful, efficient and freely available computational tool to accelerate the responsible recovery of critical raw materials from industrial residues such as BR. The calculated phase stability diagrams identified a clear reduction window in which iron-bearing oxides are transformed into metallic iron, while titanium is preferentially concentrated into perovskite, a phase favourable for downstream recovery. Experimental validation confirmed these predictions, demonstrating high iron metallization and effective titanium partitioning. The strong agreement between modelling and experimental results confirms that thermodynamic tools can reliably guide process design and material valorization strategies. Beyond the specific system studied, this work highlights how modelling can significantly reduce the experimental design space, enabling targeted process optimization rather than relying on resource-intensive trial-and-error approaches. By focusing directly on thermodynamically favourable conditions, the development cycle is accelerated while reducing energy consumption, material use, and associated environmental impact. This smarter and more efficient approach directly supports the global need to deliver essential minerals faster and more responsibly. In the broader context of the global energy transition and increasing demand for critical materials, the integration of thermodynamic modelling into process development represents a key enabler for transforming industrial residues into valuable secondary resources. Such approaches help reduce reliance on primary extraction, lower environmental footprints, and strengthen resource security. By combining predictive modelling with targeted experimentation, this methodology contributes to building more sustainable, transparent, and responsible mineral supply chains, aligned with the urgent call for innovation and trust in the mining sector.
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