Three environmental parameters, i.e. dust concentrations, dust dispersion, and free silica content, were introduced into the traditional indices of the neural network model in order to construct a new prediction index and explore a new method for preventing the incidence of pneumoconiosis with intelligent accuracy and universality. Data of the pneumoconiosis patients from Huabei Mining Group (HBMG) of China from 1980 to 2017 were collected. SPSS22.0 was used to develop the combined models based on Back Propagation (BP) neural network model, Radial Basis Function (RBF) neural network model, and Multiple Linear Regression (MLR) model. The paired sample t-test was performed between the real and predicted values. According to this model, it was predicted that 382 coal workers in HBMG were likely to suffer from pneumoconiosis in 2022 and the incidence rate was 4.48%. It is necessary to take prevention measures and transfer these workers from their current positions. In four combined models, the BP-MLR combined model achieved the optimal error parameters and the most accurate prediction. This study provided a scientific basis for effective control and prevention of the incidence of the pneumoconiosis.

Aiming at the shortcomings of the current rock-breaking technology, a new type of high-energy expansion agent for energetic materials based on combustion-to-detonation was developed. By characterizing the basic physical and chemical properties of the high-energy expansion agent (HEEA) such as morphology, particle size distribution, and pyrolysis characteristics, the work performance of different types of high-energy expansion agents was analyzed in combination with the energy characteristics. The results showed that the relationship between the expansion work done by the gas to the outside world was W_HEEA-I > W_HEEA-II > W_HEEA-III under the same quality of HEEA combustion. The damage effect of high-temperature and high-pressure gas cracking specimens generated by deflagration of HEEA was obvious, having the rule that the disturbance damage of rock caused by low heat and high gas specific volume was smaller, and the damage degree of rock caused by high heat and low gas specific volume was larger. The mechanism of HEEA combustion and detonation in confined space is revealed, which provides a theoretical basis for the application of HEEA-cracked rock.

Coal resources still occupy a dominant position in the energy consumption structure, and the prevention and control of coal dust explosion has become an important measure to ensure the safe production of coal. To this end, a new type of environmentally friendly, economical, and efficient composite powder explosion suppressant has been developed. CMS@C₁₂H₂₂O₁₄Fe was prepared by an anti-solvent crystallization method using Chinese Maifan stone (CMS) as the carrier and ferrous gluconate (C₁₂H₂₂O₁₄Fe) as the active component. The physicochemical properties of the explosion suppressant were analyzed using characterization techniques such as SEM and FT-IR. At the same time, the Hartmann tube experimental device was utilized to study the inhibition effect of the detonation suppressor on the coal powder flame, and to determine the optimal loading amount of the active component and the addition amount of the detonation suppressor. The results show that the composite powder synthesized by the anti-solvent crystallization method has a uniform particle size and good structure. The flame was almost completely suppressed when the active component loading was 50 wt.% and the additive amount of the detonation suppressant was 30 wt.%. Finally, a physicochemical synergistic inhibition mechanism of CMS@C₁₂H₂₂O₁₄Fe for coal dust explosion is proposed.