Particle and Phase Analysis of Combusted Iron Particles for Energy Storage and Release

Abstract

The combustion of metal fuels as energy carriers in a closed-cycle carbon-free process is a promising approach for reducing CO₂ emissions in the energy sector. For a possible large-scale implementation, the influence of process conditions on particle properties and vice versa has to be well understood. In this study, the influence of different fuel-air equivalence ratios on particle morphology, size and degree of oxidation in an iron-air model burner is investigated by means of small- and wide-angle X-ray scattering, laser diffraction analysis and electron microscopy. The results show a decrease in median particle size and an increase in the degree of oxidation for leaner combustion conditions. The difference of 1.94 μm in median particle size between lean and rich conditions is twentyfold greater than the expected amount and can be connected to an increased intensity of microexplosions and nanoparticle formation for oxygen-rich atmospheres. Furthermore, the influence of the process conditions on the fuel usage efficiency is investigated, yielding efficiencies of up to 0.93. Furthermore, by choosing a suitable particle size range of 1 to 10 μm, the amount of residual iron content can be minimized. The results emphasize that particle size plays a key role in optimizing this process for the future.

Keywords: iron combustion; metal fuels; microexplosions; nanoparticles; particle characterization; small-angle X-ray scattering (SAXS); wide-angle X-ray scattering (WAXS).

Figure 1

SEM image of the original carbonyl iron powder. The particles are spherical and non-porous.

Figure 2

Characteristic Bragg peaks of the powder materials used for the phase quantification. The scattering curve for iron(II) oxide has an additional peak at 44.9° due to residual iron. The corresponding calibration curve was calculated with the assumption of 5% iron content, the maximum amount according to the producer.

Figure 3

Wide-angle scattering curve of an iron oxide sample produced with a fuel–air equivalence ratio of ${\mathsf{\Phi }}_{{\mathrm{Fe}}_2{\mathrm{O}}_3}$ = 1.5. The particle exhibits four characteristic Bragg peaks due to the respective iron and iron oxide phases: α-iron (Fe) at 44.9°, iron(II) oxide (Wue) at 42.2°, iron(II,III) oxide (Mag) at 30.3° and iron(III) oxide (Hem) at 33.4°.

Figure 4

Weight fractions of the respective oxide phases over ${\mathsf{\Phi }}_{{\mathrm{Fe}}_2{\mathrm{O}}_3}$, as calculated with WAXS calibration (Section 2.6.1).

Figure 5

(a) Fuel usage efficiency for different fuel–air equivalence ratios. The efficiency is calculated as the ratio between the released energy during oxidation and the maximum possible released energy for full oxidation into Fe₂O₃. (b) Fuel usage efficiency for the stoichiometric ratio of oxygen to iron particles. The particles have been separated into two fractions, one above and one below 10 μm. The larger fraction shows increased residual iron content, which leads to decreased fuel usage efficiency.

Figure 6

Median particle size of the iron oxide particles for different fuel–air equivalence ratios, ${\mathsf{\Phi }}_{{\mathrm{Fe}}_2{\mathrm{O}}_3}$. The median of the carbonyl iron powder is included as a straight line for reference.

Figure 7

SEM images of the sampled iron oxide particles after combustion at different resolutions, (a) and (b), in the first separation stage and (c) in the second separation stage for a fuel–air equivalence ratio of 1. The particles in the first separation stage are spherical, exhibit multiple cracks and holes on the surface and are covered by fine nanoparticles adhering to the surface. In (c), the separated nanoparticles show a smooth surface as well as a spherical morphology.

Figure 8

(a) Small-angle X-ray scattering curve of the iron oxide nanoparticles. The original scattering data have been fitted (with local Guinier and power-law fits) using a unified fit approach. Guinier’s law yields a prefactor of 413.27 with a radius of gyration of 22.6 nm. The evaluation of the power-law fit delivers an exponent of −4 and a prefactor of 5.58·10⁻⁷. (b) Particle size distributions of the nanoparticles. Both distributions have a geometrical standard deviation of 1.37. For the SEM distribution, 300 particles have been counted.