Search Results (5)

Accurate identification of lithology in petroleum engineering is very important for oil and gas reservoir evaluation, drilling decisions, and petroleum geological exploration. Using a cross-plot to identify lithology only considers two logging parameters, causing the accuracy of lithology identification to be insufficient. With the continuous development of artificial intelligence technology, machine learning has become an important means to identify lithology. In this study, the cutting logging data of the Junggar Basin were collected as lithologic samples, and the identification of argillaceous siltstone, mudstone, gravel mudstone, silty mudstone, and siltstone was established by logging and logging parameters at corresponding depths. Aiming at the non-equilibrium problem of lithologic data, this paper proposes using equilibrium accuracy to evaluate the model. In this study, manifold learning is used to reduce logging and logging parameters to three dimensions. Based on balance accuracy, four dimensionality reductions including isometric feature mapping (ISOMAP), principal component (PCA), independent component (ICA), and non-negative matrix factorization (NMF) are compared. It is found that ISOMAP improves the balance accuracy of the LightGBM model to 0.829, which can effectively deal with unbalanced lithologic data. In addition, the particle swarm optimization (PSO) algorithm is used to automatically optimize the super-parameters of the lightweight gradient hoist (LightGBM) model, which effectively improves the balance accuracy and generalization ability of the lithology identification model and provides strong support for fast and accurate lithology identification. Full article

Quasi-equilibrium directional crystallization was performed on a melt composition (at. %): 18.50 Cu, 32.50 Fe, 48.73 S, 0.03 Pt, Pd, Ag, Au, Te, As, Bi, Sb, and Sn, which closely resembles the Cu-rich massive ores found in the platinum-copper-nickel deposits of Norilsk. Base metal sulfides (BMS) such as pyrrhotite solid solution (Fe,Cu)S_1±δ (Poss), non-stoichiometric cubanite Cu_1.1Fe_1.9S₃ (Cbn*), and intermediate solid solution Cu_1.0Fe_1.2S_2.0 (Iss) are progressively precipitated from the melt during the crystallization process. The content of noble metals and semimetals in the structure of BMS is below the detection limit of SEM-EDS analysis. Only tin exhibits significant solubility in Cbn* and Iss, meanwhile Pt, Pd, Au, Ag, As, Bi, Sb, and Te are present as discrete composite inclusions, comprising up to 11 individual phases, within their matrices. These microphases correspond to native Au, native Bi, hessite Ag₂Te, sperrylite Pt(As,S)₂, hedleyite Bi₂Te, michenerite PdTeBi, froodite PdBi₂, a solid solution of sudburite-sobolevskite-kotulskite Pd(Sb, Bi)_xTe_1−x, geversite PtSb₂, and a multicomponent solid solution based on geversite Me(TABS)₂, where Me = Σ(Pt, Pd, Fe, Cu) and TABS = Σ(Te, As, Bi, Sb, Sn). Most of the inclusions occur as thin layers between BMS grain boundaries or appear drop-shaped and subhedral to isometric grains within the sulfide matrix. Only a small fraction of the trace elements form mineral inclusions of sizes ≤ 0.5 μm in Poss, most likely including PtAs₂ and (Pt,Pd)S. It is likely that the simultaneous presence of noble metals (Pt, Pd, Au, Ag) and semimetals (As, Te, Bi, Sb) in the sulfide melt leads to the appearance of liquid droplets in the parent sulfide melt after pyrrhotite crystallization. The solidification of droplets during the early stages of Cbn* crystallization may occur simultaneously with the cooling of later fractions of the sulfide melt, resulting in the formation of Iss. In addition, abundant gas voids containing micro-inclusions were observed in Cbn* and Iss. These inclusions showed similar chemical and mineral compositions to those in BMS matrices, i.e., the presence of gas bubbles did not affect the main features of noble metal fractionation and evolution. Therefore, it is reasonable to assume that ore particles suspended in the melt are either trapped by defects at the crystallization front or transported towards gas bubbles via the Marangoni effect. Full article

In the current literature, information can mainly be found about free and hindered settling of isometric particles in Newtonian and non-Newtonian fluids. These conclusions cannot be used to describe the sedimentation of non-isometric particle in non-Newtonian fluids. For this reason, we have carried out systematic experiments and calculated the correlation of the hindered settling velocity of a cloud of non-isometric particles in high-viscosity and pseudoplastic liquid. The experiments were performed in transparent model fluids, namely, glycerine (a Newtonian fluid) and an aqueous solution of carboxylmethylcelulose CMC (a non-Newtonian pseudo-plastic liquid). These fluids have similar rheological properties, for example, the fresh fine-grained cementitious composites HPC/UHPC. The experiments were carried out with steel fibers with a ratio of d/l = 0.3/20. The settling velocity was determined for fiber volumes from 1% to 5%. While it is known from previous studies that for spherical particles the hindered settling velocity is proportional to the porosity of a suspension cloud on exponent 4.8, which was confirmed by our verification experiment, for the studied fiber particles it is proportional to the porosity on exponent 22.1. This great increase in the exponent is an effect of both the shape of the particles and, in particular, a mutual influence that arises from their interweaving and connection in the suspension. Full article

Video images of ice formation from moist air under temperature and electric potential gradients reveal that ambient electricity enhances ice production rates while changing the habit of ice particles formed under low supersaturation. The crystals formed under an electric field are needles and dendrites instead of the isometric ice particles obtained within a Faraday cage. Both a non-classical mechanism and classical nucleation theory independently explain the observed mutual feedback between ice formation and its electrification. The elongated shapes result from electrostatic repulsion at the crystal surfaces, opposing the attractive intermolecular forces and thus lowering the ice-air interfacial tension. The video images allow for the estimation of ice particle dimensions, weight, and speed within the electric field. Feeding this data on standard equations from electrostatics shows that the ice surface charge density attains 0.62–1.25 × 10⁻⁶ C·m⁻², corresponding to 73–147 kV·m⁻¹ potential gradients, reaching the range measured within thunderstorms. The present findings contribute to a better understanding of natural and industrial processes involving water phase change by acknowledging the presence and effects of the pervasive electric fields in the ambient environment. Full article

Particle size distributions (PSDs) belong to the most critical properties of particulate materials. They influence process behavior and product qualities. Standard methods for describing them are either too detailed for straightforward interpretation (i.e., lists of individual particles), hide too much information (summary values), or are distribution-dependent, limiting their applicability to distributions produced by a small number of processes. In this work the distribution-independent approach of modeling isometric log-ratio-transformed shares of an arbitrary number of discrete particle size classes is presented. It allows using standard empirical modeling techniques, and the mathematically proper calculation of confidence and prediction regions. The method is demonstrated on coarse-shredding of mixed commercial waste from Styria in Austria, resulting in a significant model for the influence of shredding parameters on produced particle sizes (with classes: >80 mm, 30–80 mm, 0–30 mm). It identifies the cutting tool geometry as significant, with a p-value < 10⁻⁵, while evaluating the gap width and shaft rotation speed as non-significant. In conclusion, the results question typically chosen operation parameters in practice, and the applied method has proven to be valuable addition to the mathematical toolbox of process engineers. Full article