Forskningsradar
← Tech & AI
Tech & AI 6.0 🇮🇩 🇸🇪 🇹🇭

Hidden Leaks Are Draining Billions From Waste-Heat Recovery Systems

Undetected heat and gas leaks in organic Rankine cycle systems—devices that recover waste heat from industrial processes and biomass plants—can slash efficiency by significant margins, new research shows. Real-time monitoring combined with AI could catch these problems before they cost operators millions in lost output.

Originaltitel: Methods for Leakage Monitoring for Safety and Efficiency of ORC System: A Review

Abstrakt

Abstract: Organic Rankine Cycles (ORCs) are widely used for recovering low-temperature waste heat, particularly in renewable energy systems like biomass. However, their performance is often reduced by undetected heat and gas leakage. This review aims to identify, classify, and assess current leakage de-tection methods specifically suited for ORC systems, focusing on their effectiveness under typical operat-ing conditions. The scope encompasses thermal and gas leakage detection techniques, including tempera-ture, pressure, and flow rate monitoring, as well as advanced diagnostic technologies. The main findings indicate that heat loss from components, such as the expander, and undetected vapor leakage can signifi-cantly degrade system efficiency and output. Continuous temperature, pressure, and flow rate monitoring are the most effective methods for ensuring safety and optimizing system performance, among the re-viewed options. Integrating these techniques with Internet of Things (IoT) devices and machine learning offers promising avenues for real-time diagnostics and predictive maintenance. Future research should fo-cus on developing cost-effective, robust sensors suitable for high-temperature and high-humidity envi-ronments common in ORCs. This review contributes to the broader discussion on improving ORC moni-toring and reliability while proposing practical pathways for technological innovation and sustainable en-ergy conversion.

Generera ett redaktionellt utkast på svenska