Volcanism on the Moon has produced surface basalt deposits, which record lunar interior processes. The Chang'e-6 mission retrieved samples from the South Pole-Aitken basin (SPA) on the Moon's far side. We analyze basalt fragments collected by Chang'e-6, finding their composition resembles low-Ti basalts previously sampled by the Apollo missions.
View Article and Find Full Text PDFImpact glasses found in lunar soils provide a possible window into the impact history of the inner solar system. However, their use for precise reconstruction of this history is limited by an incomplete understanding of the physical mechanisms responsible for their origin and distribution and possible relationships to local and regional geology. Here, we report U-Pb isotopic dates and chemical compositions of impact glasses from the Chang'e-5 soil and quantitative models of impact melt formation and ejection that account for the compositions of these glasses.
View Article and Find Full Text PDFThe aluminum fluoride (AF) addition in aluminum electrolysis process (AEP) can directly influence the current efficiency, energy consumption, and stability of the process. This paper proposes an optimization scheme for AF addition based on pruned sparse fuzzy neural network (PSFNN), aiming at providing an optimal AF addition for aluminum electrolysis cell under normal superheat degree (SD) condition. Firstly, a Gaussian mixture model (GMM) is introduced to identify SD conditions in which the operating modes of AEP are unknown.
View Article and Find Full Text PDFOrbital data indicate that the youngest volcanic units on the Moon are basalt lavas in Oceanus Procellarum, a region with high levels of the heat-producing elements potassium, thorium, and uranium. The Chang’e-5 mission collected samples of these young lunar basalts and returned them to Earth for laboratory analysis. We measure an age of 1963 ± 57 million years for these lavas and determine their chemical and mineralogical compositions.
View Article and Find Full Text PDFOutlet ferrous ion concentration is an essential indicator to manipulate the goethite process in the zinc hydrometallurgy plant. However, it cannot be measured on-line, which leads to the delay of this feedback information. In this study, a self-adjusting structure radial basis function neural network (SAS-RBFNN) is developed to predict the outlet ferrous ion concentration on-line.
View Article and Find Full Text PDFIn the iron removal process, which is composed of four cascaded reactors, outlet ferrous ion concentration (OFIC) is an important technical index for each reactor. The descent gradient of OFIC indicates the reduced degree of ferrous ions in each reactor. Finding the optimal descent gradient of OFIC is tightly close to the effective iron removal and the optimal operation of the process.
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