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Rock burst is one of serious disasters that inhibit safe and high efficient coal mining. The realization of intelligent pre-warning of rock burst is the critical path to ensure coal mine intelligent and safe mining. As the b value is an effective monitoring indicator of rock burst, it is of great significance for a timely pre-warning of rock burst to grasp the evolution trend of b value in the process of mining. Therefore, based on the phase space reconstruction (PSR) and deep learning, a short-term forecast method for the b value of time sequence in mine exploitation is proposed. The b value of time sequence identified by CNN and denoised is extended to a high-dimensional space through phase space reconstruction technique, and then the long short-term memory (LSTM) network optimized by the genetic algorithm (GA) learns the high-dimensional data feature, which constructs the b value prediction Model (PSR−GA−LSTM). Combined with the W1123 fully mechanized mining face of the Kuangou coal mine identified rock burst mine, the b value of time sequence denoised is reconstructed using the optimized parameters of PSR. The prediction performance of different models is evaluated and the case research of the optimal prediction model is carried out. The research results show that after the b value of time sequence is processed by noise reduction technology, the learning ability of the model for the b value trend feature can be enhanced and the interference of noise to the precursory information of rock burst can be reduced. After the b value of time sequence is reconstructed in phase space and the hyperparameters of the LSTM are optimized, the prediction accuracy of the model can be significantly improved. Compared with other models, the residual fluctuation range of the PSR−GA−LSTM model is the smallest and stable within 0.005, and its root mean square error (RMSE), mean absolute error (MAE) and the mean absolute percentage error (MAPE) is 0.001 51, 0.001 33, 0.29%, which are lower than other models. After the PSR−GA−LSTM model is trained on the b value of time sequence, the predicted b value trend contains the precursory information of rock burst, which can provide b value pre-warning indicators for the occurrence of rock burst events in advance. The model has a better ability to predict the trend development of the b value of rock burst mine with uniform advance, and the method used in this paper can provide a reference for the prediction and pre-warning research on the evolution of rock burst in time.

A photoanode composed of Au/SnS/TiO2 sensitized by natural dye is designed and fabricated for dye-sensitized solar cells (DSSCs). A layer of spherical nano-TiO2 is prepared by calcination and a layer of nano-SnS is deposited on the TiO2 photoanode by the continuous ion layer adsorption reaction (SILAR). The Au nanoparticles are modified and sensitized with natural dye. The Au nanoparticles exhibit excellent localized surface plasmon resonance (LSPR) effects and enhanced the electron trapping ability. Owing to the smaller bandgap of SnS and optical activity in the infrared region, the electron mobility improves and electron recombination decreases. Electrochemical assessment reveals that the photoelectric characteristics of the DSSC based on Au/SnS/TiO2 are greatly improved after natural dye sensitization and compared with the TiO2 photoanode, the power conversion efficiency (PCE) of the DSSC improves by 105%.

In this paper, we demonstrate the application of a novel ternary nanocomposite MoS2/Ag/TiO2 photoanode in high efficiency plasma dye sensitive solar cell (PDSSC). Ternary nanocomposites MoS2/Ag/TiO2 was successfully synthesized by a simple solvothermal method without any hazardous substances. In order to achieve higher photoelectric conversion efficiency of DSSC, the photoanode’s ability of capturing light and electron transfer rates needs to be improved. Ag provides a power for light capture, while MoS2 inhibits electron–hole recombination and promotes electron transport in the ternary nano-composite MoS2/Ag/TiO2. The nanocomposite was characterized by scanning electron microscope and transmission electronmicroscope, which indicated that the MoS2/Ag/TiO2 was synthesized. Due to local surface plasmon resonance (LSPR), silver nanoparticles were doped onto TiO2 nanoparticles, which greatly improved the optical parameters. The synthesis of Ag nanoparticles and MoS2 was confirmed by X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS). The performance of dye sensitive solar cell (DSSC) composed of ternary nano-composite material MoS2/Ag/TiO2 was tested, the photoelectric conversion efficiency (PCE) reaches 9.3%, which is 26% higher than that of pure TiO2 nanoparticle photoanode.

In order to prevent the multi-dynamic disasters induced by rock burst and roof water inrush in strong rock burst coal seams under multi-aquifers, such as is the case with the 207 working face in the Tingnan coal mine considered in this study, the exhibited characteristics of two types of dynamic disasters, namely rock burst and water inrush, were analyzed. Based on the lithology and predicted caving height of the roof, the contradiction between rock burst and water inrush was analyzed. In light of these analyses, an integrated method, roof pre-splitting at a high position and shattering at a low position, was proposed. According to the results of numerical modelling, pre-crack blasting at higher rock layers enables a cantilever roof cave in time, thereby reducing the risk of rock burst, and pre-crack blasting at underlying rock layers helps increase the crushing degree of the rock, which is beneficial for decreasing the caving height of rock layers above goaf, thereby preventing the occurrence of water inrush. Finally, the proposed method was applied in an engineering case, and the effectiveness of this method for prevention and control of multi-dynamics disasters was evaluated by field observations of the caving height of rock layers and micro-seismic monitoring. As a result, the proposed method works well integrally to prevent and control rock burst and water inrush.