In a significant advancement for the renewable energy sector, researchers led by Hatem Gasmi from the Department of Civil Engineering at the University of Hail have unveiled a hybrid geothermal system that leverages machine learning to optimize the production of both heat and electricity. This innovative approach, detailed in their recent study published in “Case Studies in Thermal Engineering,” promises to enhance the efficiency of geothermal energy, a resource often underutilized in the global energy mix.
The research combines a double-flash geothermal system with a transcritical carbon dioxide Rankine cycle, a method that not only improves energy output but also aligns with the growing demand for sustainable energy solutions. Gasmi emphasizes the potential impact of this system, stating, “By integrating advanced thermodynamic systems with machine learning, we can significantly improve the efficiency and effectiveness of renewable energy sources, paving the way for a more sustainable future.”
The study showcases remarkable accuracy in predicting system performance, achieving R-squared values of 98.86% for heating output and 99.89% for power output. Such precision is crucial for commercial applications, as it allows energy producers to optimize operations and maximize returns on investment. The findings suggest that optimal operating pressures between 840 and 870 kPa, along with specific pressure ratios, can yield power outputs of approximately 2583.97 kW and heating outputs nearing 12279.3 kW.
This dual-output capability is particularly appealing in a market increasingly focused on energy efficiency and sustainability. As industries and municipalities seek to reduce their carbon footprints, systems like the one developed by Gasmi and his team could play a critical role in meeting those objectives. “The commercial viability of our system lies in its ability to provide both heating and electricity simultaneously, which is a game changer for energy providers,” Gasmi explains.
The implications of this research extend beyond mere numbers; it represents a shift towards more integrated renewable energy systems that can adapt to varying demands. As energy markets evolve, the adoption of such innovative technologies may be crucial in maintaining energy security while also addressing environmental concerns.
With the potential to reshape the landscape of renewable energy, this groundbreaking work not only highlights the role of machine learning in optimizing energy systems but also sets the stage for future developments in geothermal energy utilization. The integration of advanced analytics could lead to smarter, more responsive energy systems that benefit both providers and consumers alike.
For more information about Hatem Gasmi’s work, you can visit the University of Hail.
