Search

The effect of temperature on phase transformation, metallization rate, carbon residue and agglomeration of metal Fe after the pre-reduction of carbon-containing pellets in high-chromium vanadium–titanium magnetite (HCVTM) was investigated. The results indicated that titanomagnetite was finally reduced to metal Fe and (Mg, Fe)Ti2O5 without low valence titanium oxide with increasing pre-reduction temperature from 950 to 1,250℃; Vanadium magnetite produces Fe, VO and VC0.88 without V, and chromite magnetite produces Fe, Cr2O3, CrCx and a small amount of Cr. With an increase in the pre-reduction temperature, the metallization rate in the metallized pellets gradually increases, while the content of the residual carbon gradually decreases. Meanwhile, the iron particles precipitated on the surface of ore particles begin to soften into strips and finally agglomerate to form spherical iron particles, which leads to the diameter of iron particles increasing from 8.23 to 182.35 μm. When the pre-reduction temperature is 1,250℃, the maximum metallization rate in the metallized pellets is 92.49 wt% and the minimum content of residual carbon is 10.37 wt%.

Aluminum–air batteries are characterized as "green energy for the 21st century" due to their clear advantages in terms of high current discharge, high specific energy, low cost, and easy-to-obtain electrode materials. This study develops the SLM aluminum anode quality prediction model and evaluates its learning and training results using the BP neural network architecture. By altering the network topology of the SLM aluminum anode quality prediction model, we create a process parameter backpropagation model that takes advantage of the extremely adaptable capabilities of artificial neural networks. The quick and exact selection of process parameters meets the goals of density, self-corrosion current, and anode usage, hence improving the forming quality and processing efficiency of SLM aluminum anodes. The experimental results show that the process parameter backpropagation model's parameter configurations match to the real densities and self-corrosion currents, which are somewhat higher than the specified target values. The maximum error rate for the aluminum anode forming quality prediction model is 8.23%. Furthermore, the actual anode utilization rate is somewhat lower than the projected target value, indicating that the backpropagation model can satisfy actual production needs. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is a critical element of the hydrological cycle, land surface processes, and surface energy balance. However, the low spatial resolution of commonly used SM products limits the application of SM in agriculture and eco‐hydrology in cold and arid regions. In this study, the normalized difference soil index (NDSI) and bare soil index (BSI) were added to traditional downscaling factors including land surface temperature, normalized difference vegetation index, digital elevation mode, apparent thermal inertia, Albedo, and temperature vegetation dryness index, as they are more strongly correlated with surface SM in the bare soil‐vegetation alternation zone of such region. Using the random forest algorithm, a downscaling model of SM was constructed for such region. The accuracy of the downscaled SM estimates was validated by comparing them with the original SM data collected from May to September 2021, which is the non‐freezing period of the soil. The findings indicate that the newly added NDSI and BSI have good correlation with SM. Incorporating NDSI and BSI to construct the downscaled model enhances the accuracy by over 19% compared to excluding them, while also providing a more comprehensive representation of SM information. NDSI and BSI can be well applied to the downscaled research of SM in the bare soil‐vegetation alternation zone, which is of great value for the study of eco‐hydrology and agricultural drought monitoring in cold and arid regions. Core Ideas: Adding soil factors improves the downscaling of soil moisture in cold and arid regions.Compared with the traditional model, the improved model showed a significant increase in accuracy.This improved model better characterized soil moisture spatial and temporal variability. [ABSTRACT FROM AUTHOR]

To advance oil and gas exploration in the South Yellow Sea Basin, characteristics of Triassic, including its correlation with regional strata, distribution, lithology characteristics, and karst development characteristics, are studied by geological mapping. Results show that there exist Zhouchongcun Formation and Qinglong Formation in the Triassic. Zhouchongcun Formation corresponds to Dongma Formation in the lower Yangtze Region, Badong Formation in the middle Yangtze Region, and Lekoupo Formation in the upper Yangtze region. The upper Qinglong Formation corresponds to the Nanlinghu Formation and Longshan Formation in Xiyanzi district, and Jialingjiang Formation in the middle and upper Yangtze region. Its lithology is limestone, intercalated with marl, mudstone, and argillaceous siltstone. The lower Qinglong Formation corresponds to the Yinkeng Formation at the lower Yangtze group. The lower Qinglong group has the complex lithologic characteristics, which can be divided into six categories from top to bottom, including limestone weathering crust layer, limestone, marl with mudstone, mudstone with limestone, marl with limestone, and mudstone interbedding limestone and mudstone. Karst phenomenon is well developed in the lower Qinglong Formation, and the thickness of weathering crust, revealed by cz35-2-1 well, reaches 60.5 m. Triassic in the South Yellow Sea Basin is mainly distributed in the central uplift and southern depression, showing the regional differences in distribution. Karstification is probably the main reason for this significant difference. Identification of karst phenomenon and stratigraphic division method towards Triassic drilling coring are two important directions of Triassic study in the South Yellow Sea Basin.