The melt blending was used to prepare 3 wt% ZnO/low density polyethylene (ZnO/LDPE) nanocomposites in this article. The effect of different inorganic ZnO particles doping on the dielectrical property and crystal habit of LDPE matrix was explored. The nanoparticles size was 9 nm, 30 nm, 100 nm, and 200 nm respectively. Scanning electron microscope (SEM) was used to characterize ZnO nanoparticles whereas differential scanning calorimetry (DSC) was used to make thermal characterization of the samples. Besides, the AC (alternating current), DC (direct current breakdown characteristics and electrical conductivity of the nanocomposites was studied in this article. The experimental results showed that nano-ZnO/LDPE composites had the advantages such as small crystal size, high crystallization rate and crystallinity owing to nano-ZnO particles doping, when doping nano-ZnO particles size was 30 nm, the ZnO/LDPE nanocomposite crystallinity crest value 39.77% appeared. At the mean time, the DC and AC breakdown field strength values of composites were 138.0 kV/mm and 340.4 kV/mm respectively. They were the maximal values which improved 8.24% and 13.85% than LDPE. The AC breakdown field strength of samples decreased with specimen thickness increase. The DC breakdown field strength of LDPE and ZnO/LDPE composites were greater than AC breakdown field strength. From the conductivity experimental result it could be seen that when the experimental temperature and electric field intensity rose, the current density and conductivity of ZnO/LDPE composites increased with the enlargement of ZnO particles size. But the values were less than which of LDPE.
Cave backfill grouting implies grouting of the caving rock mass prior to it being compacted. The filling materials strengthen the caving rock and support the overlying strata to achieve the purpose of slowing down the surface subsidence. The broken roof will fail and collapse during mining operations performed without appropriate supporting measures being taken. It is difficult to perform continuous backfill mining on the working face of such roofs using the existing mining technology. In order to solve the above problems, fly ash and mine water are considered as filling materials, and flow characteristics of fly-ash slurry are investigated through laboratory experiments and theoretical analyses. Laws governing the diffusion of fly-ash slurry in the void of caving rock masses and in the void between a caving rock mass and a basic roof are obtained and verified. Based on the results obtained from the above analyses and actual conditions at the Zhaoguan coal mine, Shandong Province, China, a cave backfill grouting system of the hauling pipeline is developed and successfully tested at the 1703 working face in the Zhaoguan coal mine. The results demonstrate that a filling rate of 43.46% is achieved, and the surface subsidence coefficient of the grouting process is found to be 0.475. Compared to the total caving method, the proposed system is found to achieve a reduction rate of 40.63%. This effectively helps in lowering the value of the surface subsidence coefficient. Fly ash and mine water, considered as primary materials in this study, also play a significant role in improving the air quality and water environment.
Accurately predicting the height of water flowing fractured zone is of great significance to coal mine safety mining. In recent years, most mines in China have entered deep mining. Aiming at the problem that it is difficult to accurately predict the height of water flowing fractured zone under the condition of large mining depth, the mining depth, height mining, inclined length of working face and coefficient of hard rock lithology ratio are selected as the main influencing factors of the height of water flowing fractured zone. The relationship between various factors and the height of water flowing fractured zone is analyzed by SPSS software. Based on the data mining tool Weka platform, Bayesian classifier, artificial neural network and support vector machine model are used to mine and analyze the measured data of water flowing fractured zone, and the detailed accuracy, confusion matrix and node error rate are compared. The results show that, the accuracy rate of instance classification of the three models is greater than 60%. The accuracy of the artificial neural network model is the highest and the node error rate is the lowest. In general, the training effect of the artificial neural network model is the best. By predicting engineering examples, the prediction accuracy of the model reaches 80%, and a good prediction effect is obtained. The height prediction system of water flowing fractured zone is developed based on VB language, which can provide a reference for the prediction of the height failure grade of water flowing fractured zone.
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