Under CopyrightKraft, ThomasThomasKraft2025-12-192025-12-192025https://publica.fraunhofer.de/handle/publica/502226https://doi.org/10.24406/publica-689610.24406/publica-6896For the efficient operation of concentrating solar systems, knowledge of the mass flow distribution is of central importance. While the temperature distribution within a solar field is generally well known due to the presence of numerous temperature sensors, the mass flow distribution in commercial plants often remains unknown. This publication demonstrates how operational power plant data can be automatically processed using the Mondas Internet of Things (IoT) platform to determine the mass flow distribution within the solar field. The Time of-Flight (ToF) method is employed, wherein thermal gradients in the heat transfer fluid (HTF) are measured at several locations over time. Based on these time offsets, fluid velocity and mass flow are derived, considering the relevant pipeline geometries. As a result, mass flows can be determined in a completely non-invasive manner without financial or structural risks, using only existing temperature sensors. Suitable operating points are identified automatically through integrated peak-finder algorithms and intelligent data preprocessing. Rapid evaluation of operating data allows operators to respond promptly to anomalies, for instance by adjusting valve positions or repairing leaks. By combining the derived mass flow distribution with the corresponding outlet temperatures, inefficient areas of the solar field can be pinpointed, thus substantially improving the operation and maintenance of concentrating solar systems. The results presented in this paper offer significant potential to enhance the competitiveness of linear concentrating solar thermal systems.enAutomationConcentrated Solar PowerMass Flow MeasurementSolar FieldThermal Time of Flight (TToF)presentation