(c) Uriel SC

Project description

The research project AI4CO2Opt addresses the minimization of energy consumption and consequently CO2 emissions while at the same time optimizing production control. Through the combination of energetic and event-discrete simulation models as well as deeper reinforcement learning processes, different production scenarios can be derived at runtime for multivariate and dynamic optimization goals and imported into production. Furthermore, as part of a holistic approach to energy consumption, the simulation and machine learning models developed are used to enable production-related detection and classification of abnormal energy consumption at process and plant level. In order to meet current and future economic and legislative requirements for the traceability of CO2 emissions, a consistent CO2 traceability system for all system components is being designed and implemented as a prototype.

The AI4CO2Opt project will start on April 1st, 2022 and will be funded by the Bavarian State Ministry for Science and Art (stmwk) for a period of 3 years. It can be assigned to the “Digitalization in the Energy Sector” track in the “R&D Program Information and Communication Technology Bavaria” program.