Abstract:The spectral imaging (SI) approach is used in analytical electron microscopy for determining chemical compositions in materials at the microscale. A major challenge is how to efficiently unlock microspatially resolved chemical information in large SI data cubes. Tata Steel has developed an in-house software approach called PhAse Analysis, Recognition and Characterization (PARC). PARC combines automated phase recognition with flexible user-defined refinement functions to create phase allocation models. These models may be used in automated batch processing on multiple SI fields, enabling the visualization of complex microstructures, and the quantification of phase proportions and chemistry, at length scales up to several millimeters. The approach bridges the gap between microanalysis and bulk analysis and lends itself to cross-validation with independent bulk analytical techniques such as X-ray fluorescence (XRF) and X-ray diffraction (XRD).

Background Integrated steelmaking is known to emit coarse airborne ‘nuisance’ dust (10–100 µm) to the production site and in the local environs. We present a method to quantitatively analyse the provenance, mineralogical and chemical attributes of the constituent particles in nuisance dust related to the integrated steelworks of Tata Steel, IJmuiden, the Netherlands. The dust is characterised per particle, using scanning electron microscopy with energy-dispersive spectrometry (SEM–EDS) microanalysis, and in bulk with quantitative X-ray diffraction (XRD) analysis. Based on mineralogical characteristics, particles in the dust are sorted into populations that can be related in detail to industrial processes and subsequent atmospheric weathering influence. The method is illustrated by application to a nuisance dust complaint sample from the neighbouring town Wijk aan Zee containing a large contribution of several dust sources from the integrated steelworks. Results Besides a background contribution from urban and natural dust, diverse sources from the integrated steelworks are identified in the nuisance dust sample, derived from coke-making, iron-ore agglomeration processes and blast furnace ironmaking, steelmaking slag processing, process fluxes, as well as steelmaking refractory materials. The most voluminous of these in the sample are directly verified by comparison with a set of reference source materials. The abundances, mineralogical and chemical attributes of the various dust particle populations in the sample are quantitatively examined including, specifically, the occurrence of the potentially toxic elements Mn and V. These elements occur with highest concentrations in dust derived from steelmaking converter slag: V is housed in dilute form (solid solution) in the phases di-calcium silicate and brownmillerite, and Mn chiefly in Mg–Fe-oxide (Mg-wustite ((Mg,Mn,Fe)O) and its oxidation product ((Mg,Mn,Fe)(Fe,Mn)2O4)). Conclusions By treating nuisance dust as a particulate, multi-phase, multi-source material, the outlined method provides crucial information for toxicological evaluation and for mitigation of emissions, which is not obtainable by bulk chemical analyses alone. It also helps address the lack of adequate monitoring options for deposits of nuisance dust from integrated steel production, necessary to evaluate the relationship between deposition and monitored emissions that are regulated by the European Industrial Emissions Directive and by local permits based on this legislation.

Numerous tests are used worldwide to investigate the advanced high temperature reduction, softening, and melting (S&M) of blast furnace ferrous burden material. Commonly, the curve of pressure drop (dP) against temperature, measured over the experimental charge, is taken as directly indicative of the evolution of ferrous layer permeability. Previous authors have expressed concerns about the reproducibility and practical relevance of dP measurements due to narrow crucible diameter and wall effects. Petrological study of samples from interrupted Advanced Softening and Melting (ASAM) experiments, performed with ferrous burdens comprising a mixture of pellets (C/S 0.1–0.7) and highly basic sinter (C/S 2.4–3.5), sheds light on the nature and controlling mechanisms of systematic artefacts influencing the measurement of dP. The observations imply that the dP-T curves in ASAM, and likely other similar S&M, tests most immediately reflect the varying ease of gas flow in a sidewall bypass around the qualitatively impermeable ferrous burden layer, rather than through it. This is controlled by the formation of diverse oxide(-slag)-metal segregations at the exterior of the ferrous burden layer. The potential correlation of parameters derived from dP measurements with input burden characteristics is not in itself sufficient evidence that the measurements are indicative of the ferrous layer permeability.

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A method has been developed to screen large numbers (~103–104 per sample) of coarse airborne dust particles for the occurrence of Pb-rich phases, together with quantification of the particles’ mineralogy, chemistry, and inferred provenance. Using SEM-EDS spectral imaging (SI) at 15 kV, and processing with the custom software PARC, screening of individual SI pixels is performed for Pb at the concentration level of ~10% at a length-scale of ~1 µm. The issue of overlapping Pb-Mα and S-Kα signal is resolved by exploiting peak shape criteria. The general efficacy of the method is demonstrated on a set of NIST particulate dust standard reference materials (SRMs 1649b, 2580, 2584 and 2587) with variable total Pb concentrations, and applied to a set of 31 dust samples taken in the municipalities surrounding the integrated steelworks of Tata Steel in IJmuiden, the Netherlands. The total abundances of Pb-rich pixels in the samples range from none to 0.094 area % of the (total) particle surfaces. Overall, out of ca. 92k screened particles, Pb was found in six discrete Pb-phase dominated particles and, more commonly, as superficial sub-particles (sub-micron to 10 µm) adhering to coarser particles of diverse and Pb-unrelated provenance. No relationship is apparent between the samples’ Pb-rich pixel abundance and their overall composition in terms of particle provenance.