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An essential prerequisite for the expansion of deep geothermal energy, i.e. the use of geothermal energy from the earth's crust at depths of more than 400 m, especially considering the background of the planned geothermal and mine heat storage projects to convert the existing district heating systems in the Metropole Ruhr, is a reliable feed pump technology. New technical approaches for increasing the efficiency and service life of these pumps as well as prediction models for imminent pump failures during operation are of great interest. Due to the prevailing environmental conditions in which these types of feed pumps operate efficiency and service life are sometimes greatly reduced due to increased wear and deposits. Additionally, there are frequent sensor failures, which also leads to a direct reduction in efficiency, as the pumps are only operating with greatly reduced performance for safety reasons. Consequently, a scientific approach of computer-assisted optimization of maintenance intervals and an improvement of sensor technology in the field of conveying technology in deep geothermal energy are indispensable.

The goal of COMPRESS aims to significantly minimize the immense costs associated with frequent feed pump replacement and the associated long system downtimes. Therefore, the identification of error sources is an essential task possibly achieved by monitoring the running pump operation in combination with computer-assisted prediction models for planning optimized maintenance intervals. It is necessary to characterize the relevant operating conditions as well as the wear parts of the feed pumps. Furthermore, the operational status of specific feed pumps component must be tracked by sensors. Given the prevailing temperature levels, hydrochemical conditions and borehole and pump geometries, this poses increased challenges for the sensors, signal transmission and processing.