Prior-Informed Separation of Long-Scale Shape and Short-Scale Texture on Blast Furnace Burden Surfaces
Jiuzhou Tian, Akira Tanaka, Di GaoParticle-scale analysis of blast furnace burden surfaces lacks an operational criterion for separating long-scale shape from short-scale texture on complex digital elevation models. This study proposes a prior-informed framework in which the application cutoff ω*=argminJ minimizes the mismatch between high-pass texture RMS height and the tiled-surface prior of the same particle batch. On cold-state large-coke belts with identical particles but different long-scale morphology, numerical validation via RMS–frequency transition analysis shows coincident transition structures. At a transition-informed validation cutoff of ω=4.2, absolute texture errors of 1.56–3.22 mm are comparable in magnitude to the approximately 2 mm instrument depth resolution. Grid-search application yields ω*=5.8 and 4.4 with absolute errors of 0.03 and 0.35 mm and operationally distinct shape and texture components. The separated fields can supply bed-surface boundaries and local roughness inputs for gas–solid simulation and charging optimization.