• Energy Research

Particle properties such as size, shape and density play significant roles on particle flow and flame propagationin pulverized fuel combustion and gasification. A drop tube furnace allows for exper ...

Particle properties such as size, shape and density play significant roles on particle flow and flame propagationin pulverized fuel combustion and gasification. A drop tube furnace allows for exper ...

The effect of mechanical screening of severely contaminated forest fuel chips was investigated, focusing on main ashforming elements and slagging tendency and other properties with relevance for thermal conversion. In this study, screening operations were performed according to practice on an industrial scale by combining a star screen and a supplementary windshifter in six different settings and combinations. Mechanical screening reduced the amount of ash and fine particles in the accept fraction. However, the mass losses for the different screening operations were substantial (20−50 wt %). Fuel analyses of the non-screened and the screened fuels showed that the most significant screening effect was a reduction of Si and Al, indicating an effective removal of sand and soil contaminations. However, the tested fuel’s main ash-forming element’s relative concentration did not indicate any improved combustion characteristics and ash-melting behavior. Samples of the accept fractions and non-screened material were combusted in a single-pellet thermogravimetric reactor, and the resulting ashes’ morphology and elemental composition were analyzed by scanning electron microscopy−energy dispersive X-ray spectrometry and the crystalline phases by powder X-ray diffraction. Results from both these analyses confirmed that screening operations had no, or minor, effects on the fuels’ ash chemistry and slagging tendencies, i.e., the fuels’ proneness to ash melting was not improved. However, the reduction of ash and fine particles can reduce slagging and other operational problems in smaller and more sensitive combustion units.

The effect of mechanical screening of severely contaminated forest fuel chips was investigated, focusing on main ashforming elements and slagging tendency and other properties with relevance for thermal conversion. In this study, screening operations were performed according to practice on an industrial scale by combining a star screen and a supplementary windshifter in six different settings and combinations. Mechanical screening reduced the amount of ash and fine particles in the accept fraction. However, the mass losses for the different screening operations were substantial (20−50 wt %). Fuel analyses of the non-screened and the screened fuels showed that the most significant screening effect was a reduction of Si and Al, indicating an effective removal of sand and soil contaminations. However, the tested fuel’s main ash-forming element’s relative concentration did not indicate any improved combustion characteristics and ash-melting behavior. Samples of the accept fractions and non-screened material were combusted in a single-pellet thermogravimetric reactor, and the resulting ashes’ morphology and elemental composition were analyzed by scanning electron microscopy−energy dispersive X-ray spectrometry and the crystalline phases by powder X-ray diffraction. Results from both these analyses confirmed that screening operations had no, or minor, effects on the fuels’ ash chemistry and slagging tendencies, i.e., the fuels’ proneness to ash melting was not improved. However, the reduction of ash and fine particles can reduce slagging and other operational problems in smaller and more sensitive combustion units.

Phosphorus has been identified as a critical element by the European Union and recycling efforts are increasingly common. An important phosphorus-containing waste stream for recycling is municipal sewage sludge (MSS), which is used directly as fertilizer to farmland. However, it contains pollutants such as heavy metals, pharmaceutical residues, polychlorinated bi-phenyls (PCBs) and nano-plastics. The interest in combustion of MSS is continuously growing, as it both reduces the volume as well as destroys the organic materials and could separate certain heavy metals from the produced ashes. This results in ashes with a potential for either direct use as fertilizer or as a suitable feedstock for upgrading processes. The aim of this study was to investigate co-combustion of MSS and biomass to create a phosphorus-rich bottom ash with a low heavy metal content. A laboratory-scale fixed-bed reactor in addition to an 8 MWth grate-boiler was used for the experimental work. The concentration of phosphorus and selected heavy metals in the bottom ashes were compared to European Union regulation on fertilizers, ash application to Swedish forests and Swedish regulations on sewage sludge application to farmland. Element concentrations were determined by ICP-AES complemented by analysis of spatial distribution with SEM-EDS and XRD analysis to determine crystalline compounds. The results show that most of the phosphorus was retained in the bottom ash, corresponding to 9–16 wt.% P2O5, while the concentration of cadmium, mercury, lead and zinc was below the limits of the regulations. However, copper, chromium and nickel concentrations exceeded these standards.

Phosphorus has been identified as a critical element by the European Union and recycling efforts are increasingly common. An important phosphorus-containing waste stream for recycling is municipal sewage sludge (MSS), which is used directly as fertilizer to farmland. However, it contains pollutants such as heavy metals, pharmaceutical residues, polychlorinated bi-phenyls (PCBs) and nano-plastics. The interest in combustion of MSS is continuously growing, as it both reduces the volume as well as destroys the organic materials and could separate certain heavy metals from the produced ashes. This results in ashes with a potential for either direct use as fertilizer or as a suitable feedstock for upgrading processes. The aim of this study was to investigate co-combustion of MSS and biomass to create a phosphorus-rich bottom ash with a low heavy metal content. A laboratory-scale fixed-bed reactor in addition to an 8 MWth grate-boiler was used for the experimental work. The concentration of phosphorus and selected heavy metals in the bottom ashes were compared to European Union regulation on fertilizers, ash application to Swedish forests and Swedish regulations on sewage sludge application to farmland. Element concentrations were determined by ICP-AES complemented by analysis of spatial distribution with SEM-EDS and XRD analysis to determine crystalline compounds. The results show that most of the phosphorus was retained in the bottom ash, corresponding to 9–16 wt.% P2O5, while the concentration of cadmium, mercury, lead and zinc was below the limits of the regulations. However, copper, chromium and nickel concentrations exceeded these standards.

X-ray microtomography is a non-destructive method that allows for detailed three-dimensional visualisation of the internal microstructure of materials. In the context of using phosphorus-rich residual streams in combustion for further ash recycling, physical properties of ash particles can play a crucial role in ensuring effective nutrient return and sustainable practices. In previous work, parameters such as surface area, porosity, and pore size distribution, were determined for ash particles. However, the image analysis involved binary segmentation followed by time-consuming manual corrections. The current work presents a method to implement deep learning segmentation and an approach for quantitative analysis of morphology, porosity, and internal microstructure. Deep learning segmentation was applied to microtomography data. The model, with U-Net architecture, was trained using manual input and algorithm prediction.•The trained and validated deep learning model could accurately segment material (ash) and air (pores and background) for these heterogeneous particles.•Quantitative analysis was performed for the segmented data on porosity, open pore volume, pore size distribution, sphericity, particle wall thickness and specific surface area.•Material features with similar intensities but different patterns, intensity variations in the background and artefacts could not be separated by manual segmentation - this challenge was resolved using the deep learning approach.