(136a) Optimizing Green Hydrogen Production with Operational Digital Twins

Green hydrogen is typically produced via water electrolysis using renewable electricity, with various electrolyzer technologies already in use at scale. However, plant owners face high production costs and operational complexities due to the variability in renewable power supplies, limited operator experience, and challenges in efficiently utilizing expensive storage capacity. Additionally, operators must navigate dynamic factors such as hydrogen pricing and demand to maintain optimal operations. Developing and implementing optimized production plans in near real-time is essential to address these challenges and ensure cost-effective operation.

Digital twins offer a promising solution to these challenges. During the design phase of hydrogen production plants, digital twins can simulate various design scenarios, enabling engineers to optimize plant configuration, select appropriate technologies, and predict performance under different operating conditions. This proactive approach helps in identifying potential issues early, reducing design errors, and ensuring that the plant is built to operate efficiently from the start.

In the operational phase, innovative digital twins combine a high-fidelity model of an asset with real-time information from a plant’s data framework, creating a highly accurate replica of the plant state that is updated continuously. When deployed in an online application, this replica provides operators with detailed insights into asset performance and powerful decision-support tools for optimization.

A modular digital twin application designed to enhance green hydrogen production can offer performance monitoring, production planning, and dynamic real-time optimization, in addition to the benefits seen during the design phase. This enables plant operators to quickly understand their operations and efficiently meet hydrogen demand. Utilizing a physics-based digital twin framework, the application can conduct plant-wide simulations and state estimation, accurately modeling key physical phenomena, including electrochemistry, heat exchange, and reaction. The application provides a predictive representation of production scenarios by combining physics-based simulations with demand and power supply forecasts.

In this presentation, attendees will learn how a transformative approach to digitalization in the hydrogen industry can overcome some of the most pressing challenges in creating renewable hydrogen. We will showcase a practical application of a plant digital twin that optimizes hydrogen production and reduces energy consumption, delivering a step-change in both the sustainability and economic viability of hydrogen plants at all scales.