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Differences in the densities of bed material and—especially biogenic—solid fuels prevent an ideal mixture within bubbling fluidised bed (BFB) combustors. So, the presence of fuel particles is usually observed mainly near the surface of the fluidised bed. During their thermal conversion, this leads to a release of unburnt pyrolysis products to the freeboard of the combustion chamber. Within the further oxidation, these species will not transfer their heat-of-reaction to the inert bed material in the way of a convective heat transfer, but rather increase the gas phase temperature providing probably some additional radiative heat transfer to the dense bed. In this case, the so-called heat release efficiency to the fluidised bed, being the ratio of transferred heat to the fuel input, will be reduced. This paper presents a methodology to quantify this heat release efficiency with lab-scale experiments and the observed effects of common operating parameters like bed temperature, fluidisation ratio and fuel-to-air ratio. Experimental results show that the air-to-fuel ratio dominates the heat release efficiency, while bed temperature and fluidisation ratio have minor influences.

This work focuses on the contribution of fuel particles with diameters smaller than 1 mm on the ash deposition behavior of different kinds of biomass feedstock. Special emphasis lies on their impact on industrial-scale boilers. For this purpose, we conduct lab-scale experiments and numerical simulations of biomass boilers in the megawatt range. First of all, we present experimental results from combustion and deposition experiments derived from a 100 kWth lab-scale fluidized bed furnace featuring a vertical deposition probe in the flue gas duct. To assess the slagging propensity of fine particles, different amounts of ground fuels were introduced into the combustion chamber. The experiments show a significantly higher amount of deposits (by a factor of 3.9–5.7) in the presence of fine particles in comparison to pelletized feedstock. Scanning electron microscopy with energy-dispersive X-ray spectroscopy and laser diffraction measurements were carried out, identifying the prevailing deposition mechanisms with respect to the respective feedstock. Subsequently, we draft a fast and scalable computational-fluid-dynamics-based model for the combustion of these fuel particles and ash deposition in megawatt-range biomass boilers. The main focus lies on collision-induced deposition of molten ash particles carried away from the fuel bed, thus burning in the flue gas channel. This modeling approach is capable of identifying the principal sections suffering from ash deposits in the combustion chamber and can be used to evaluate potential mitigation and avoidance strategies.

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This article focuses on fuel quality in biomass power plants and describes an online prediction method based on image analysis and regression modeling. The main goal is to determine the mixture fraction from blends of two wood chip species with different qualities and properties. Starting from images of both fuels and different mixtures, we used two different approaches to deduce feature vectors. The first one relied on integral brightness values while the latter used spatial texture information. The features were used as input data for linear and non-linear regression models in nine training classes. This permitted the subsequent prediction of mixture ratios from prior unknown similar images. We extensively discuss the influence of model and image selection, parametrization, the application of boosting algorithms and training strategies. We obtained models featuring predictive accuracies of R2 > 0.9 for the brightness-based model and R2 > 0.8 for the texture based one during the validation tests. Even when reducing the data used for model training down to two or three mixture classes—which could be necessary or beneficial for the industrial application of our approach—sampling rates of n < 5 were sufficient in order to obtain significant predictions.