Online optimization of biomass high-temperature energy conversion processes

Variations of operational parameters are one of the most critical uncertainties in practical thermochemical conversion processes. Diagnostics and feedback control of these variations can not only improve the performance of existing processes but also pave the way for novel biomass conversion methods and increase the use of challenging biomass fuels.

We aim to develop and apply new software that integrates diagnostic sensors based on tunable diode laser absorption spectroscopy, direct imaging techniques, and machine learning. The software will provide online real-time data on conversion efficiency, biomass moisture content, fuel feeding variation, and emissions in Swedish bio-based pilot and full-scale plants. The data will be used to perform feedback control and propose optimized operating practices for these plants. The expected outcome is the improvement of process efficiency and flexibility, reduction of pollutant emissions, along with increased digitalization in industry.