Search Results (2)

To address the issue of air pollution caused by automobile exhaust in China, a titanium dioxide/graphite carbon nitride (TiO₂/g-C₃N₄) composite photocatalyst capable of degrading automobile exhaust was prepared in this study. It was used as an additive to modify styrene–-butadiene latex (SBR) emulsified asphalt. The basic properties of modified emulsified asphalt before and after aging were analyzed, and the dosage range of TiO₂/g-C₃N₄ (TCN) was determined. The environmentally friendly micro-surfacing of degradable automobile exhaust was prepared. Based on 1 h and 6 d wet wheel wear test, rutting deformation test, surface structure depth test, and pendulum friction coefficient test, the road performance of TCN environmentally friendly micro-surfacing mixture with different contents was analyzed and evaluated, and the effect of environmentally friendly degradation of automobile exhaust was studied by a self-made degradation device. The results show that when the mass ratio of TiO₂ and melamine was 1:4, the TCN composite photocatalyst had strong photocatalytic activity. The crystal structure of TiO₂ and g-C₃N₄ was not damaged during the synthesis process. The g-C₃N₄ inhibited the agglomeration of TiO₂. The introduction of N-Ti bond changed the electronic structure of TiO₂, narrowed the band gap and broadened the visible light response range. When the TCN content was in the range of 1~7%, the softening point of SBR- modified emulsified asphalt increased with the increase in TCN content, the penetration decreased, the ductility decreased gradually, and the storage stability increased gradually. The penetration ratio and ductility ratio of the composite-modified emulsified asphalt after aging increased with the increase in TCN content, and the increment of the softening point decreased. This shows that the TCN content is beneficial to the high-temperature performance and anti-aging performance of SBR-modified emulsified asphalt, and has an adverse effect on low temperature performance and storage stability. The addition of TCN can improve the wear resistance and rutting resistance of the micro-surfacing mixture, and has no effect on the water damage resistance and skid resistance. The environment-friendly micro-surfacing asphalt mixture had a significant degradation effect on NO, CO, and HC. With the increase in TCN content, the degradation efficiency of the three gases was on the rise. When the content was 5%, the degradation rates of NO, CO, and HC were 37.16%, 25.72%, and 20.44%, respectively, which are 2.34 times, 2.47, times and 2.30 times that of the 1% content, and the degradation effect was significantly improved. Full article

A novel hybrid model is proposed to improve the accuracy of ultra-short-term wind speed prediction by combining the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), the sample entropy (SE), optimized recurrent broad learning system (ORBLS), and broadened temporal convolutional network (BTCN). First, ICEEMDAN is introduced to smooth the nonlinear part of the wind speed data by decomposing the raw wind speed data into a series of sequences. Second, SE is applied to quantitatively assess the complexity of each sequence. All sequences are divided into simple sequence set and complex sequence set based on the values of SE. Third, based on the typical broad learning system (BLS), we propose ORBLS with cyclically connected enhancement nodes, which can better capture the dynamic characteristics of the wind. The improved particle swarm optimization (PSO) is used to optimize the hyper-parameters of ORBLS. Fourth, we propose BTCN by adding a dilated causal convolution layer in parallel to each residual block, which can effectively alleviate the local information loss of the temporal convolutional network (TCN) in case of insufficient time series data. Note that ORBLS and BTCN can effectively predict the simple and complex sequences, respectively. To validate the performance of the proposed model, we conducted three predictive experiments on four data sets. The experimental results show that our model obtains the best predictive results on all evaluation metrics, which fully demonstrates the accuracy and robustness of the proposed model. Full article