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Hydraulic fracturing stands as a pivotal technological approach for enhanced tight gas recovery. This paper investigates the influences of geological and engineering parameters on the vertical extension mechanism of hydraulic fractures. In addition, the feasibility and effectiveness of fracture height prediction method and various fracture height control techniques have been examined. The results indicate that the height of hydraulic fractures decreases with an increase in the thickness of the barrier layers, the stress difference between the barrier and reservoir layers, the difference in tensile strength, and the difference in fracture toughness, whereas it increases with the increasing of difference in elastic modulus between the barrier and reservoir layers. Compred with the difference in Poisson's ratio, the volume of fracturing fluid, discharge rate, and fluid viscosity have little impactd. The influence of these factors on fracture height, in descending order, is stress difference between barrier and reservoir layers, fracturing fluid viscosity, fracturing discharge, fracturing fluid volume, barrier layer thickness, tensile strength difference between barrier and reservoir layers, elastic modulus difference between barrier and reservoir layers, Poisson's ratio difference between barrier and reservoir layers. Furthermore, based on typical geomechanic and reservoir parameters of the target area, a fracture height prediction workflow has been developed. Engineering practice has proven the reliability of fracture height prediction method. The results of this study provide theoretical support and guidance for predicting fracture morphology, controlling fracture height in the hydraulic fracturing development of the tight gas reservoir, and optimizing fracturing process design. [ABSTRACT FROM AUTHOR]

Flexible job shop scheduling problem is the allocation of available shared resources and the sequencing of processing tasks within a certain period of time to meet certain or certain specific production indicators. The research and application of effective scheduling methods and optimization technologies are the foundation and key to realizing advanced manufacturing and improving production efficiency. Improving the production scheduling plan can greatly improve production efficiency and resource utilization, thereby enhancing the competitiveness of enterprises. Therefore, the production scheduling problem has always been a research hotspot in manufacturing systems. In this paper, we introduce the opposition-based learning strategy and combine it with whale optimization algorithm to solving flexible job shop scheduling problem better. 10 FJSP cases are introduced to test the performance of our algorithm and other comparison algorithms. The results obtrain show that our algorithm is more better and practical than other algorithm when dealing with FJSP cases.

Carbon-coated SnO2 nanocomposites were synthesized successfully by the hydrothermal method and carbonization at 500 °C with glucose and SnCl2·2H2O as precursor materials. The SnO2/C nanocomposites were characterized by various techniques such as X-ray powder diffraction (XRD), scanning electron microscopy (SEM), high-resolution transmission electron microscopy (HR-TEM), Raman spectra, and electrochemical analyses. It was demonstrated that SnO2/C-0, SnO2/C-5, SnO2/C-15, SnO2/C-30, and SnO2/C-50 had initial discharge capacities of 1359.2, 1626.8, 2124.9, 1525.8, and 1349.4 mAhg−1, respectively. In particular, the SnO2/C-15 sample exhibited excellent high reversible lithium storage capacity, good rate capability, and cycling stability. The electrodes show a long cycling ability and high charge/discharge capacity due to the presence of carbon. [ABSTRACT FROM AUTHOR]

Cu nanoparticles with different sizes, morphology, and surface structures exhibit distinct activity and selectivity toward CO2 reduction reaction, while the reactive sites and reaction mechanisms are very controversial in experiments. In this study, we demonstrate the dynamic structure change of Cu clusters on graphite‐like carbon supports plays an important role in the multicarbon production by combining static calculations and ab‐initio molecular dynamic simulations. The mobility of Cu clusters on graphite is attributed to the near‐degenerate energies of various adsorption configurations, as the interaction between Cu atoms and surface C atoms is weaker than that of CuCu bonds in the tight cluster form. Such structure change of Cu clusters leads to step‐like irregular surface structures and appropriate interparticle distances, increasing the selectivity of multicarbon products by reducing the energy barriers of CC coupling effectively. In contrast, the large ratio of edge and corner sites on Cu clusters is responsible for the increased catalytic activity and selectivity for CO and H2 compared with Cu(100) surface, instead of hydrocarbon products like methane and ethylene. The detailed study reveals that the dynamic structure change of the catalysts results in roughened surface morphologies during catalytic reactions and plays an essential role in the selectivity of CO2 electro‐reduction, which should be paid more attention for studies on the reaction mechanisms. [ABSTRACT FROM AUTHOR]