A Process-Based Tool for Estimating Water Use in Geothermal-Lithium Co-Production Systems

The transition to a renewable energy grid requires the expansion of clean energy production and advancements in energy storage technologies, the latter of which relies directly on critical minerals such as lithium. However, the extraction of such minerals can impose significant environmental demands, particularly on freshwater resources. The Salton Sea Known Geothermal Resource Area (SS-KGRA) in California offers a unique opportunity to co-produce geothermal energy and lithium from highly saline geothermal brines. Although geothermal plants generally consume less freshwater than conventional energy-generation technologies, such as fossil-fuel-based and nuclear plants, their water use in the arid SS-KGRA still intensifies local water scarcity. Therefore, quantifying the freshwater requirements of expanded geothermal and direct lithium extraction (DLE) operations is critical to ensuring sustainable development. In this study, we address a key gap in the current state of knowledge: the high uncertainty and lack of transparency in freshwater consumption estimates for geothermal and DLE processes. We introduce a unit process-based modeling framework designed to quantify water use and assess environmental trade-offs under different development and water allocation scenarios. Our Python-based tool currently estimates water consmnption for flash-steam geothermal power generation, drawing on process trains built from peer-reviewed manuscripts, patent data, and industry reports, and presents these results in detail. Detailed DLE modules (impurity removal, lithium recovery, conversion, and by-product management) are under development and will be integrated into the framework in future work. Such a model will provide transparent information on water use from geothermal energy and DLE processes for stakeholders. The model includes three modules: (1) an Input Dashboard that allows users to select and configure facilities and operational parameters, (2) a Computational Engine that calculates water use using thermodynamic, mass balance, and empirical models, and (3) a Visualization & Reporting Module that outputs results, including total water consumption, sensitivity analyses, and scenario comparisons.