(11d) Multi-Period Recovery of Critical Minerals Using Robust Membrane Diafiltration Systems

Critical Minerals (CMs) are key materials necessary for economic and national security that are extensively used in essential energy technologies and products, but they suffer from vulnerable supply chains with high geographical concentration of both resources and processing capabilities [1]. Novel technologies and rising energy needs are expected to lead to a sharp increase in CM demand [2], and diversified processes for producing and recovering CMs are needed.

Membrane systems are promising processes for CM recovery that have seen success in wastewater treatment applications [3] and do not generate the large amounts of waste that traditional hydrometallurgical separations processes like solvent extraction produce. Recent work has explored the use of membrane diafiltration processes for CM recovery and proposed their use as modular and flexible technologies that can respond quickly to operational disturbances [4-6].

Although the need for diversified CM supply chains and benefits of membrane technologies are clear, the process of designing and deploying recovery systems that remain robust under many possible scenarios is needed. CM pricing, for example, may vary significantly on a month-to-month basis and depend on geopolitical circumstances [7,8]. Similarly, process feedstock conditions may change due to decreasing quality of mining sources and changing materials of recycled electronics [5,9]. Membranes, in particular, may also suffer decreases in performance from inevitable fouling over time [10-12]. We handle these challenges from a process systems perspective and contribute methods for technical risk reduction for these processes using robust optimization.

We look toward utilizing the advantages of membrane system adaptability for applications in recovering CMs. To this end, we have developed membrane diafiltration cascade flowsheet models using the IDAES Integrated Platform (IDAES-IP) [13]. The flowsheet contains a cascade of membrane stages for fractionation of an inlet mixture into multiple products and downstream recovery through precipitation, with solvent recycle returning to the membrane cascade as a diafiltrate flow. The model is formulated with multiple periods to study the time-dependent performance of the membrane system and utilizes advanced costing capabilities built within the IDAES costing framework.

We use robust optimization methods, enabled by the Pyomo Robust Optimization Solver (PyROS) [14,15], to study optimization under uncertainty of these multi-period models in the context of economic variabilities and fluctuations in process environments. We partition our decisions into first-stage design choices, such as membrane area and precipitator sizing, and later-stage operational choices for recourse against uncertainty, such as inlet/outlet flow rates of the membrane cascade. By incorporating non-anticipativity into our decision rules, we ensure that the decisions obtained for each period of our model are based only on information available at the time. Using PyROS, we can generate and study robust designs that perform well in spite of a range of economic and process uncertainties.

Disclaimer

This project was funded by the Department of Energy, National Energy Technology Laboratory an agency of the United States Government, in part, through a site support contract. Neither the United States Government nor any agency thereof, nor any of its employees, nor the support contractor, nor any of their employees, makes any warranty, expressor implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof, or any of their contractors.

Acknowledgments

This effort was funded by the U.S. Department of Energy’s Process Optimization and Modeling for Minerals Sustainability (PrOMMiS) Initiative, supported by the Office of Fossil Energy and Carbon Management’s Office of Resource Sustainability.

References

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