BMWi-Projekt: FuelBand2

BMWi-Project: FuelBand2 – Plant and feedstock optimisation for higher fuel flexibility of biomass furnaces

The project FuelBand2 develops a machine learning guard for slagging in biomass furnaces, that can be integrated in the system controll, to increase the flexibility of usable fuels.

Support Code: 03KB145

Term: 01.07.2018 – 30.06.2021


Projektträger Jülich


Bundesministerium für Wirtschaft und Energie


Förderprogramm Energetische Biomassenutzung

The project addresses ash deposition („slagging and fouling“) in biomass-fired heat and power plants. The principle goal is to achieve a reduction of ash and slag contaminations and the development of an on-line detection system in order to facilitate suitable countermeasures. This potentially allows the utilization of higher shares of low-rank feedstock like forest residues.

The project aims at the evaluation of different fuel pre-treatment and plant operation strategies in order to reduce the ash deposition propensity in megawatt-scale combustion plants. These strategies will be implemented into an “online ash deposition system”, a framework based on realtime process data and additional sensors. Using a machine learning approach and regression modelling, empirical and experience-based plant control strategies will be applied and demonstrated for ash deposition mitigation in industrial-scale biomass combustion.


Dr.-Ing. Thomas Plankenbühler

Department of Chemical and Biological Engineering
Lehrstuhl für Energieverfahrenstechnik