Coal gangue is a kind of typical by-product emitted during the coal mining and washing process. With the increase in coal resource utilization, a large amount of coal gangue was not reasonably utilized, causing environmental pollution and resource waste. The main purpose of this article is to introduce the surface structural features and compositional characteristics of coal gangue and to summarize the utilization of coal gangue in the fields of building materials, energy production, agricultural utilization, and high-value-added areas such as catalysts and adsorbents. Secondly, this review discussed the environmental challenges and technical difficulties derived from the process of coal gangue utilization and how to solve these problems through innovative methods and technological improvements. Finally, the article proposed the development direction and strategies for the future resource utilization of coal gangue, emphasizing the importance of coal gangue as a sustainable resource and its significant role in achieving a circular economy for reducing environmental pollution. By analyzing the potentiality of coal gangue for resource utilization systematically, this article aims to provide valuable references and insights for researchers and decision-makers in related fields.

As an extremely important energy source, improving the efficiency and accuracy of coal classification is important for industrial production and pollution reduction. Laser-induced breakdown spectroscopy (LIBS) is a new technology for coal classification which has the ability to rapidly analyze coal compared with traditional coal analysis methods. In the practical application of LIBS, a large amount of labeling data is usually required, but it is quite difficult to obtain labeling data in industrial sites. In this paper, to address the problem of insufficient labeled data, a semi-supervised classification model (SGAN) based on adversarial neural network is proposed, which can utilize unlabeled data to improve the classification accuracy. The effects of labeled and unlabeled samples on the classification accuracy of the SGAN model are investigated, and the results show that the number of labeled and unlabeled samples are positively correlated, and the highest average classification accuracy that the model can achieve is 98.5%. In addition, the classification accuracies of SGAN and other models (e.g., CNN, RF) are also compared, and the results show that, with the same number of labeled samples in the three models, SGAN performs better after the number of unlabeled samples reaches a certain level, with an improvement of 0.7% and 2.5% compared to the CNN and RF models, respectively. This study provides new ideas for the application of semi-supervised learning in LIBS.

Fly ash from waste incineration and waste poplar bark from furniture manufacturing are domestic wastes. In this study, fly ash and poplar bark were used as raw materials to prepare activated carbon via carbonization, steam activation and reagent modification. The effects of the raw material mixing ratio, carbonization temperature, activator concentration and modifier concentration on the physicochemical properties of the semi-coke and activated carbon were investigated through experiments. The experiment showed that when the carbonization temperature was 300 °C, the steam concentration was 20%, the mixing ratio of the poplar bark and fly ash (B:F) was 5:1, and the modifier was 6% K₂CO₃ reagent and 9% CaCl₂. The prepared activated carbon had a better yield, specific surface area and pore structure, and had an abundant surface functional group structure. This paper points out the direction for the industrial directional production of activated carbon adsorbents with excellent physical and chemical properties, which has practical significance.

Elevated emissions of hydrogen chloride (HCl) from the combustion of biomass in utility boilers are a major issue because they can cause corrosion problems and deposit molten alkali chloride salts on boilers’ water tubes, resulting in further corrosion. Pyrolysis is a good pre-treatment for solving this problem. This work conducted pyrolysis and co-pyrolysis of pulverized corn straw and lignite coal in a horizontal muffle furnace, with compositions typical of power plant combustion effluents (5% O₂, 15% CO₂, 80% N₂) at different temperatures. Cl compounds were monitored in fuel, flue gas, and solid production of pyrolysis. The co-pyrolysis significantly affected Cl release from fuel. Cl release from corn straw into fuel gas was reduced during biomass co-pyrolysis with lignite. Co-pyrolysis had little influence on the release of organic Cl and KCl. Furthermore, at moderate-temperature pyrolysis, O₂ promoted HCl release, when compared with pyrolysis under a N₂ atmosphere.