Powder Technology

Low and intermediate-level radioactive wastes roll compaction technology Volume reduction

Fig.6

CT scan data for three-dimensional visualization of the evolution of pores and fractures in reservoir rocks under triaxial stress.

These data is the raw or processed simulation result of our numerical experiments.

In Torres-Serra et al, we propose a methodology based on cluster analysis of data sets including quantitative flow descriptors of a wide range of powders and grains, with application in the packaging industry. Our work has implications for objectifying the commonly qualitative process of selecting the most suitable bulk feeding technique in handling equipment design. The first data set DS1 (‘DS1.csv’) describes 174 materials characterised by 6 conventional material properties. The second data set DS2 (‘DS2.csv’) describes 11 representative materials, fully-characterised by 126 conventional and specialised variables. Numbering of the specialised variables in DS2 identifies average measurements of up to 20 new specialised material properties for 6 different test cases, as discussed in the associated article. The tables in ‘legends.pdf’ detail the legend of variables in the two data sets DS1 and DS2, comprising material property symbols and descriptions used in the associated article. The interactive MATLAB® figures ‘classes.fig’ and ‘clusters.fig’ show observations in DS1 projected into a reduced 3D space defined by PCA, corresponding respectively to figures Fig. 8a (feeder-type classification from industrial know-how) and Fig. 8b (flowability-based clustering) in the associated article.

XRF, XRD, laser and optical granulometry (for PSD and morphological parameters, respectively), CEC, BET and Zeta potential measurement for four samples: (a) Moanda Mn ore fines enriched in Pyrolusite and cryptomelane, (b) FeMn refining dusts, (c) bentonite and (d) kaolinite.

Data are from a manufacturing froth flotation plant. The first column shows time and date range (from march of 2017 until september of 2017).The second and third columns are quality measures of the iron ore pulp right before it is fed into the flotation plant. Column 4 until column 8 are the most important variables that impact in the ore quality in the end of the process. From column 9 until column 22, we can see process data (level and air flow inside the flotation columns, which also impact in ore quality. The last two columns are the final iron ore pulp quality measurement from the lab.

The simulation results in Origin files, with figures.

All the simulated and experimental data.

The analysis conditions and log file obtained from the transient state, free-surface simulations have been attached here.