Italian Study: Ultrasound Revolutionizes Molten Salt Management in CSP

A groundbreaking study led by Raffaele Dragonetti from the Department of Industrial Engineering at the University of Naples “Federico II” in Italy, published in Applied Sciences, is set to revolutionize the management of molten salt systems in concentrated solar power (CSP) plants. The research delves into the application of ultrasonic technology, a method traditionally used in medical diagnostics and materials science, to detect solid inclusions in molten salt piping. This innovation promises to enhance the efficiency and safety of CSP systems, addressing a critical challenge in the renewable energy sector.

Dragonetti’s work focuses on the use of non-invasive ultrasonic techniques to monitor and manage the solidification of molten salts, such as the widely used NaNO3–KNO3 mixture, which solidify when temperatures drop below 240°C. This solidification can lead to the formation of inclusions within piping and storage sections, posing operational risks and compromising efficiency. Traditional temperature-based control mechanisms, while effective in monitoring bulk thermal conditions, lack the precision to provide detailed information on the extent or location of solidification. This often results in excessive and energy-intensive activation of heating systems, reducing overall efficiency.

Dragonetti explains, “Ultrasound offers a promising alternative due to its ability to detect phase changes, density shifts, and variations in viscosity within fluid mixtures. By analyzing the interaction between ultrasonic waves and solid inclusions, we can gather detailed information about the inclusions’ spatial distribution and their impact on the system.”

The study employed finite element method (FEM) simulations in the time domain to investigate the interaction of ultrasonic signals with solidified inclusions. This approach allowed for a detailed understanding of wave propagation and attenuation in the presence of these inclusions. Unlike previous studies, which primarily focused on basic detection techniques or limited material systems, this work integrated statistical analysis to interpret the ultrasonic signals. This allows for real-time detection and assessment of the internal state of the molten salt, offering significant advantages in terms of operational monitoring and efficiency.

The key innovation lies in the ability to combine numerical simulations with probabilistic modeling, providing a more accurate and reliable framework for assessing the degree of solidification and the spatial distribution of inclusions. This approach addresses the limitations of traditional methods, which are often limited to temperature-based controls or qualitative visual inspections. By integrating statistical analysis, the research established a probabilistic framework that links measured attenuation levels to likely section porosity ranges, forming the basis for operational decision-making.

This research has far-reaching implications for the energy sector, particularly in CSP plants. The proposed ultrasonic-based monitoring approach offers a non-invasive, continuous, and high-resolution alternative for detecting and characterizing solid inclusions. This could lead to more energy-efficient and precise management of solidification in CSP molten salt piping, reducing energy consumption and minimizing unnecessary intervention. Dragonetti’s work highlights the versatility and transformative potential of ultrasonic technology in industrial fluids management, paving the way for more robust, efficient, and adaptive systems capable of autonomously managing the challenges of solidification in CSP piping networks.

The study was published in Applied Sciences, underscoring the importance of this research in the broader scientific community. As the energy sector continues to evolve, innovations like this will be crucial in enhancing the efficiency and reliability of renewable energy systems. Future work could focus on experimental validation of the proposed methods and the integration of supervised machine learning techniques to improve predictive capabilities, further advancing the field and its applications.

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