In the quest for sustainable energy solutions, a groundbreaking study has emerged that could revolutionize how we harness low-grade thermal energy. Led by Diki Ismail Permana, a researcher affiliated with the Doctoral School of Mechanical Engineering at the Hungarian University of Agriculture and Life Science, the University of Florence, and the Institut Teknologi Nasional Bandung, this innovative work focuses on the Organic Rankine Cycle (ORC) system. The research, published in Energy Nexus, which translates to Energy Crossroads, promises to enhance the efficiency and predictability of ORC systems, paving the way for more effective waste heat recovery and renewable energy integration.
The ORC system is already known for its efficiency in converting low-grade thermal energy into usable power. Unlike traditional Rankine cycles that rely on water, ORC systems use refrigerants or mixed fluids with lower boiling points, making them ideal for smaller-scale and lower-temperature applications. These systems can be adapted to various heat sources, including solar energy, geothermal, biomass, and waste heat recovery. However, predicting the performance of ORC systems and identifying optimal operating conditions have remained significant challenges.
Permana’s research addresses these issues head-on by leveraging the power of artificial neural networks (ANNs). ANNs are self-learning, nonlinear methods capable of approximating complex functions, making them perfect for developing accurate prediction models for ORC systems. “The use of ANNs allows us to reduce the experimental resource requirements and enhance the accuracy of our models,” Permana explains. “This approach not only saves time and resources but also provides a more reliable framework for optimizing ORC operations.”
The study involved developing a 2 kW ORC prototype and applying ANN to predict and optimize performance using 102 experimental data sets. This method significantly reduces the need for extensive experimental trials, making the process more efficient and cost-effective. Moreover, the research employs a multi-objective optimization approach to simultaneously maximize net output work and thermal efficiency, setting a new benchmark for sustainable and efficient ORC system designs.
The implications of this research are far-reaching for the energy sector. By improving the predictability and efficiency of ORC systems, Permana’s work could lead to more widespread adoption of these systems in industrial settings, where waste heat recovery is a critical concern. “This research provides insights into optimized ORC operations for real-world applications,” Permana notes. “It advances predictive modeling for ORC systems, improving resource efficiency and contributing to more sustainable energy practices.”
As the energy sector continues to evolve, the integration of machine learning and advanced optimization techniques will play a crucial role in driving innovation. Permana’s study, published in Energy Nexus, offers a glimpse into the future of ORC technology, where predictive modeling and optimization work hand in hand to create more efficient and sustainable energy solutions. The commercial impacts could be substantial, with industries benefiting from reduced energy costs and improved operational efficiency. As we move towards a more sustainable future, research like this will be instrumental in shaping the next generation of energy technologies.