MALEG – Maschinelles Lernen zur Verbesserung der geothermischen Energienutzung
Dr. Tobias Kluge, Dr. Fabian Nitschke, Prof. Dr. Thomas Kohl
BMWI, Grant number, subproject KIT: 020E‐100554430;
Funded as part of the Smart Energy Systems ERA Net - Geothermica call
The key target of this project is the development of a new AI-based tool (MALEG) to study and quantify the impact of enhanced heat extraction from thermal waters on geothermal plants in terms of their two most significant aspects, the geochemical and economic characteristics. At the same time, the MALEG simulation tool will be developed for onsite operation and finally act as a “digital twin - controller” for geothermal plants. This development will be accompanied by comprehensive geochemical sampling campaigns, data collection and analysis of plant operation parameters. The AI-based tool and the digital twin will be part of the “MALEG demonstration system” shown in schematic sketch and will be complemented by a hardware system that consists of a “hardware twin” that is able to emulate a geothermal plant and process technology for geothermal brine treatment and mineral extraction. The objective is to implement the MALEG demonstrator in in a broad variety of sites in Turkey, Austria and Germany for the training of machine learning by the digital twin and for the validation of the entire system operations in different real environments. The final objective is that the MALEG system will be a validated, unique and highly innovative software and hardware tool for the assistance of advanced geothermal energy planning and plant operations and will help to increase the technical and economic capacity of existing and new geothermal plants.