The FurnAIce project develops an AI-driven control system for tunnel kilns to optimize energy-intensive brick production. The goal is to significantly enhance energy efficiency, reduce CO2 emissions, and improve operational sustainability.
At the core of the project is the development of hybrid models that combine physical process models with data-driven AI techniques. These enable precise prediction and optimization of kiln operations. By using model predictive control (MPC) and reinforcement learning (RL), energy consumption is expected to be reduced by up to 20%. An interactive training framework supports human operators in safely managing the production process and its AI-assisted control.
The consortium includes TU Wien (AI and control systems), Wienerberger (industrial expertise, test environment), and DrS3 (computational fluid dynamics). Together, they aim to create a scalable solution for the brick industry and related sectors.
