Surrogate-based optimization analysis and scale-up of an ultra-concentrated brine pilot treatment chain for sustainable mineral recovery

This work presents the analysis and optimization of a treatment chain for bitterns, aiming at full recovery of magnesium, while generating all required chemicals directly on-site enabling a fully circular and self-sustaining treatment scheme. Based on a pilot-scale experimental campaign, machine learning surrogate models were developed and embedded into an optimization framework aiming to identify the operating conditions minimizing the production cost of the target product: magnesium hydroxide. This poses the basis for the development of the so-called industrial-scale “MareMag” plant. A sensitivity analysis and whole optimization were conducted to evaluate the best model performance and to provide insights into the key parameters influencing the system. Optimization led to a production rate exceeding 50 tons/year, with a final product cost of 1.58 €/kg and a specific energy consumption of 3.83 kWh/kg. Scaling up the process to an industrial level (5000 tons/year), the production cost was dramatically reduced to values ranging between 0.40 and 0.45 €/kg, thereby demonstrating the economic feasibility of the process and its large implementation potential. The specific water consumption in all scenarios was found below 0.1 m3/kgMg(OH)2 highlighting the lower water footprint of the scheme. The outcomes confirm the viability of the proposed treatment chain, highlighting its novelty and potential as a sustainable solution for waste brine valorisation within a circular economy framework.