Lithium Extraction from Geothermal Brines in the Salton Sea Region; The Ethical Implications of Tesla’s Opticaster in AI-Driven Energy Optimization

Lithium Extraction from Geothermal Brines in the Salton Sea Region:

This report presents the design and feasibility of converting a geothermal plant in California’s Salton Sea region into a brine-based direct lithium extraction (DLE) facility. The goal is to produce 99.5 wt% battery-grade lithium carbonate to meet rising demand driven by electric vehicles, renewable energy storage, and electronics. Unlike conventional methods that rely on evaporation ponds, DLE offers a more sustainable approach, minimizing environmental harm while supporting lithium supply growth. The plant will process geothermal brine at 6,000 gal/min, 110 °C, and 1 atm. Silica pretreatment precedes DLE to ensure compatibility with downstream units. Major stages of the process include brine cooling, citrate addition, adsorption and regeneration, lithium precipitation, peroxide removal, and separatory operations. Each step is optimized for thermodynamics, kinetics, and material balances to maximize efficiency. Advanced adsorption methods, such as simulated moving beds, are employed to enhance lithium recovery.

Although technically promising, the project is currently economically unfeasible due to high operating costs. Future improvements in citrate recycling and calcium removal are recommended to boost profitability. A Layer of Protection Analysis (LOPA) ensures safety considerations are incorporated into the design.

In conclusion, the proposed DLE facility offers a promising pathway for environmentally and socially responsible lithium production. While further optimization is needed—particularly in reagent recovery, pretreatment, and integration with existing geothermal systems—this approach holds strong potential for scalable, sustainable lithium extraction in support of clean energy transitions.

The Ethical Implications of Tesla’s Opticaster in AI-Driven Energy Optimization:

This paper explores the ethical implication of Tesla’s Opticaster, an AI-powered energy optimization system that enhances efficiency in distributed energy resources. While this technology offers significant advancements in renewable energy integration, it also raises concerns regarding privacy, equity, and environmental sustainability. The Opticaster’s reliance on real-time data collection to optimize battery storage and energy usage introduces risks around user surveillance and data ownership. Without clear policies, consumers lack control over how their energy usage data is collected, shared, and used. Additionally, the benefits of the Opticaster – such as energy cost savings and improved grid services- are currently accessible primarily to those who can afford complementary technologies like Tesla’s Powerwall. This raises concerns about deepening energy access inequities, particularly in underserved or low-income communities. From an environmental perspective, while the system supports sustainability goals, its reliance on data-intensive infrastructure and resource-heavy hardware poses challenges that may offset its ecological benefits.

Using Actor-Network Theory (ANT), this research examines the complex relationships between human and non-human actors – such as consumers, regulators, and the Opticaster itself – highlighting the power dynamics and ethical tensions embedded in AI-driven energy systems. This study emphasizes the need for transparent data practices, inclusive system design, and sustainable hardware development. Without addressing these ethical concerns, technologies like the Opticaster risk reinforcing existing social inequalities and environmental harms, despite their technical promise.

School of Engineering and Applied Science

Bachelor of Science in Chemical Engineering

Technical Advisor: Eric Anderson

STS Advisor: Joshua Earle

Technical Team Members: Jasper Bennett, Patrick Boyd, Ian Forrer, Nicholas Goldstein