Critical minerals, such as magnesium, potassium, lithium, and sodium, among others, are crucial for clean energy technologies, and electronics, and play key roles in advanced manufacturing. Recently, their recovery from secondary sources is increasingly being prioritized due to rising mineral demand, supply chain risks, and growing policy emphasis on resource sustainability. This work presents a mathematical modeling approach coupled with a superstructure-based optimization framework for the separation and recovery of critical marine-derived minerals from bittern, a concentrated brine waste stream resulting from sea salt production. A mixed-integer nonlinear programming (MINLP) model was developed to identify viable process configurations and recovery pathways and evaluate their economic and environmental trade-offs. Initial cost-driven optimization revealed energy consumption as the dominant economic driver, with optimal pathway achieving a 17% lower production cost than the traditional pathway. To enable a holistic sustainability assessment, the framework was extended to a multi-objective formulation integrating techno-economic analysis with life cycle indicators, solved as a bi-objective problem using the epsilon constraint method. The approach generated a spectrum of candidate solutions spanning cost-optimal, environmentally favorable and trade-off configurations. For the representative cost-climate change case, an 8.4% cost reduction coincided with up to 55% improvement in climate change impact across the solution space. These findings highlight the importance of integrated economic and sustainability optimization in process design and demonstrate the potential of bittern valorization as a pathway for sustainable critical mineral recovery and circular resource utilization. • Integrated framework for sustainable mineral recovery from bittern. • Superstructure optimization reveals viable integrated recovery routes. • Economic feasibility tests conducted via cost-optimality analysis. • Coupled techno-economic and LCA evaluations for process design decisions. • Multi-objective optimization reveals cost and environment trade-offs.
Gao et al. (2026) studied this question.