13 research outputs found

    The effects of supercritical CO2 on the seepage characteristics and microstructure of water-bearing bituminous coal at in-situ stress conditions

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    CO2 geological storage (CGS) is considered to be an important technology for achieving carbon peak and carbon neutralization goals. Injecting CO2 into deep unminable coal seams can achieve both CGS and enhance coalbed methane (ECBM) production. Therefore, the deep unminable coal seams are considered as promising geological reservoirs. CO2 exists in a supercritical CO2 (ScCO2) when it was injected into deep unminable coal seams. The injection of ScCO2 can induce changes in the seepage characteristics and microstructure of deep water-bearing coal seams. In this study, typical bituminous coal from Shenmu, Shanxi Province was used to investigate the effects of ScCO2 on seepage characteristics, pore characteristics, and mineral composition through experiments such as seepage tests, low-temperature liquid nitrogen adsorption, and X-ray diffraction (XRD). The results indicate that ScCO2 treatment of dry and saturated coal samples caused a significant increase in clay mineral content due to the dissolution of carbonates, leading to the conversion of adsorption pores to seepage pores and an improvement in seepage pore connectivity. Therefore, the Brunauer-Emmett-Teller (BET) specific surface area and pore volume of the two coal samples both decreased after ScCO2 treatment. Moreover, the permeability of dry and saturated coal samples increased by 191.53% and 231.71% at 10 MPa effective stress respectively. In semi-saturated coal samples, a large amount of dolomite dissolved, leading to the precipitation of Ca2+ and CO32- to form calcite. This caused pore throats to clog and macropores to divide. The results show that the pore volume and average pore size of coal samples decrease, while the specific surface area increases after ScCO2 treatment, providing more space for gas adsorption. However, the pore changes also reduced the permeability of the coal samples by 32.21% and 7.72% at effective stresses of 3 MPa and 10 MPa, respectively. The results enhance our understanding of carbon sequestration through ScCO2 injection into water-bearing bituminous coal seams

    Research on Dam Deformation Prediction Model Based on Optimized SVM

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    Although constructing a dam can bring significant economic and social benefits to a region, it can be catastrophic for the population living downstream when it breaks. Given the dynamic and nonlinear characteristics of dam deformation, the traditional dam prediction model has been unable to meet the actual engineering demands. Consequently, this paper advocates for a novel method to solve this issue. The proposed method is based on the optimization of improved chicken swarm (ICSO) and support vector machine (SVM). To begin with, the mean square error is used as the objective function, and then, we apply the improved chicken swarm algorithm to iterate continuously, and finally, the optimal SVM parameters are obtained. Through the modeling and simulation experiments of a nonlinear system, the validity of the improved chicken swarm algorithm to optimize an SVM model has been verified. Based on the horizontal displacement monitoring data of FengMan Dam, this paper analyzed the influencing factors of horizontal displacement. According to the results, three prediction models have been established, respectively: the SVM prediction model optimized by the improved chicken swarm algorithm, the SVM prediction model optimized by the basic chicken swarm algorithm and the BP neural network prediction model optimized by the genetic algorithm. The obtained results from the experiment authenticate the validity and superiority of the proposed method

    Research on the Uncertainty of Landslide Susceptibility Prediction Using Various Data-Driven Models and Attribute Interval Division

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    Two significant uncertainties that are crucial for landslide susceptibility prediction modeling are attribute interval numbers (AIN) division of continuous landslide impact factors in frequency ratio analysis and various susceptibility prediction models. Five continuous landslide impact factor interval attribute classifications (4, 8, 12, 16, 20) and three data-driven models (deep belief networks (DBN), random forest (RF), and neural network (back propagation (BP)) were used for a total of fifteen different scenarios of landslide susceptibility prediction studies in order to investigate the effects of these two factors on modeling and perform a landslide susceptibility index uncertainty analysis (including precision evaluation and statistical law). The findings indicate that: (1) The results demonstrate that for the same model, as the interval attribute value rises from 4 to 8 and finally to 20, the forecast accuracy of landslide susceptibility initially increases gradually, then progressively grows until stable. (2) The DBN model, followed by the RF and BP models, provides the highest prediction accuracy for the same interval attribute value. (3) AIN = 20 and DBN models have the highest prediction accuracy under 15 combined conditions, while AIN = 4 and BP models have the lowest. The accuracy and efficiency of landslide susceptibility modeling are higher when the AIN = 8 and DBN models are combined. (4) The landslide susceptibility index uncertainty predicted by the deeper learning model and the bigger interval attribute value is comparatively low, which is more in line with the real landslide probability distribution features. The conditions that the environmental component attribute interval is divided into eight parts and DBN models are used allow for the efficient and accurate construction of the landslide susceptibility prediction model
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