Biomass CHP plants are able to provide carbon-neutral electricity and heat, independent of the weather conditions. They are therefore an important pillar of the future climate-neutral power supply. In order to be able to make this process as cost-effective as possible, low-cost and problematic biogenous materials must also be taken into consideration as sources of energy.

With this in mind, a consortium of research institutions and companies is currently developing new processes with which low-cost biomass residues and waste materials can be utilised as cleanly and efficiently as possible. The aim of the FuelBand2 project is thus to reduce the huge amounts of slagging and ash deposits that are often produced in the combustion of residual materials in order to avoid damage to the plant and malfunctions. Within the framework of the previous project FuelBand, possibilities for predicting the slagging tendency of conventional and alternative biofuels have already been identified.

The self-learning early-warning system being used for the first time in FuelBand2 builds on these findings. Applying the machine-learning approach, the system can detect slagging immediately and suggest countermeasures, e.g. adjusting the combustion parameters. The project is also interested in identifying measures for the environmentally compatible pre-treatment of waste and residual materials. Different approaches will be showcased and evaluated in order to identify possibilities for the optimisation of plant operators with problematic fuels.

The project is being funded by the Federal Ministry for Economic Affairs and Energy (BMWi) and will run until mid-2021.