Enhancing geothermal energy efficiency with Gen AI: Smarter energy solutions

Geothermal energy is a clean and reliable power source, but making it more efficient can be difficult. Systems like organic Rankine cycles (ORCs) are commonly used because they work well with moderate temperatures and are environmentally friendly.

However, improving their performance requires careful control of factors like temperature, pressure, and flow.

Traditional design and simulation tools can be slow and hard to use. That’s where Gen AI, Bayesian optimization, and large language models (LLMs) come in. These advanced technologies can make the process faster, smarter, and more user friendly.

• Gen AI can create useful data, suggest design improvements, and support decision-making.
• Bayesian optimization helps find the best settings to boost system efficiency.
• LLMs can explain complex data and offer clear, actionable insights.

By combining these tools with traditional engineering methods, we can build smarter, more efficient geothermal systems. This approach supports greener energy solutions that are easier to design, manage, and scale.

How can Gen4Geo help to optimize the geothermal energy process?

We partnered with one of India’s top institutes (IIT) to explore how geothermal power plants perform under different conditions. Our goal was to better understand and improve their efficiency.

• Simulation and modeling
  We built detailed models of geothermal systems using Python and REFPROP to get accurate data. We focused on key parts of the organic Rankine cycle (ORC) and calculated important values like energy output and efficiency. To ensure accuracy, we also recreated the model in Aspen HYSYS, a trusted industry tool.
• Smart predictions
  We used Gen AI to create a model that can predict how the system should operate to reach certain efficiency goals. This model was trained on real data and tested to make sure its predictions were reliable.
• System optimization
  To find the best setup for the system, we used Bayesian optimization with a fast-learning model (XGBoost). This helped us quickly identify the most efficient configurations without heavy computing.
• User friendly interface
  We developed a chatbot called Gen4Geo, powered by a large language model (LLM). It allows users – even those without technical backgrounds – to ask questions and get clear, helpful answers about the system.
• A smarter, closed loop system
  By combining simulation, AI generated data, optimization, and a natural language interface, we created a smart, self-improving system. It helps design and manage geothermal plants more easily and efficiently.

Bringing value to the geothermal extraction domain with AI and physical modeling

Traditional methods for designing geothermal power plants can be slow, expensive, and hard to use without deep technical knowledge. Our new approach solves these problems by combining the power of artificial intelligence (AI) with proven physical models.

• Faster, smarter design
  We use Gen AI to quickly create realistic data, which helps us test different design ideas much faster than before. This speeds up the entire process and leads to better, more efficient systems.
• Cost effective optimization
  With Bayesian optimization, we can find the best system settings using fewer tests. This saves time and money while still delivering high performance.
• Easy to use for everyone
  A breakthrough is our use of large language models (LLMs). These allow anyone from engineers to decision makers to ask questions and get clear, helpful answers. No need for deep technical skills.
• Always improving
  Our system learns and adapts over time. As new data comes in, it gets smarter, helping us stay ahead in geothermal technology and improve performance under changing conditions.
• A greener future
  By making plant design faster, cheaper, and more accurate, our method helps speed up the use of geothermal energy. It supports cleaner, more sustainable energy solutions that are also more profitable.

Key insights and learnings

We’re combining the power of thermodynamics and artificial intelligence (AI) to solve real world energy challenges. By using smart data models alongside traditional simulation and optimization tools, we can make geothermal power plants more efficient, faster to design, and more affordable. A key part of our approach is using Gen AI to create useful data for testing and improving system performance. Bayesian optimization helps us make smart choices quickly, saving time and money. We’ve also added a large language model (LLM) interface that lets users interact with the system using everyday language. This makes advanced tools easier to use, even for people without a technical background. This approach isn’t just for geothermal energy; it can also be used in other industries like oil and gas or hydrogen production. It opens the door to smarter, more sustainable, and more accessible energy solutions across the board.