Herein, a series of Co-Fe mixed oxide modified ZSM-5 adsorbents were synthesized using the ultrasonic-assisted impregnation method for the capture of elemental mercury. In comparison with other samples, Co₄Fe₁-ZSM-5 produced a relatively better performance, with the removal efficiency of around 96.6% Hg⁰ and the adsorption capacity of around 901.63 ug/mg Hg⁰ achieved at 120 °C. The interaction between CoO_x and FeO_x improved the reducibility of oxygen species, thus promoting the oxidation of Hg⁰. Among a variety of other gas components, O₂ and NO exerted a positive effect on Hg⁰, which improved its removal to a certain extent. By contrast, SO₂ caused an adverse effect on the capture of Hg⁰, which could be reversed to some degree by the introduction of 5% O₂. After five cycles, the mercury removal efficiency of Co₄Fe₁-ZSM-5 remained above 90%, suggesting excellent recyclability. Finally, XPS analysis was conducted to reveal that Mars–Maessen mechanisms are dominant in the process of mercury adsorption.

In ventilation systems of metal mines, the real-time measurement of the airflow field and a reduction in pollutants are necessary for clean environmental management and human health. However, the limited quantitative data and expensive detection technology hinder the accurate assessment of mine ventilation effectiveness and safety status. Therefore, we propose a new method for constructing a mine intelligent ventilation system with a global scheme, which can realize the intelligent prediction of unknown points in the mine ventilation system by measuring the airflow parameters of multiple known points. Firstly, the nodal wind pressure method combined with the Hardy–Cross iterative algorithm is used to solve the mine ventilation network, and the airflow parameters under normal operation and extreme working conditions are simulated, based on which an intelligent ventilation training database is established. Secondly, we compared the airflow parameter prediction ability of three different machine learning models with different neural network models based on the collected small-sample airflow field dataset of a mine roadway. Finally, the depth learning method is optimized to build the intelligent algorithm model of the mine ventilation system, and a large number of three-dimensional simulation data and field measurement data of the mine ventilation system are used to train the model repeatedly to realize the intelligent perception of air flow parameters of a metal mine ventilation network and the construction of an intelligent ventilation system. The results show that the maximum error of a single airflow measurement point is 1.24%, the maximum overall error is 3.25%, and the overall average error is 0.51%. The intelligent algorithm has a good model training effect and high precision and can meet the requirements of the research and application of this project. Through case analysis, this method can predict the airflow parameters of any position underground and realize the real-time control of mine safety.

A series of Cu-ZSM-5 molecular sieve adsorbents were prepared by the impregnation method. The experimental results revealed that the mercury removal efficiency of the 2.5 wt% CuCl₂-ZSM-5 can reach up to 99%. Furthermore, both the crystal type and pore size distribution of the ZSM-5 molecular sieve remain the same after the process of copper-based active materials impregnated modification, and its specific surface area decreases as the load increases. Importantly, the surface of ZSM-5 modified by CuCl₂ equips many more Cu-O functional groups, which are beneficial to the catalytic oxidation of mercury and can even oxidize Hg⁰ to Hg²⁺. The adsorption process strictly follows the Mars–Maessen reaction mechanism.