China produces and consumes most of coal in the world. This situation is expected to continue within a certain period in the future. Currently, Chinese coal industry is confronted with several serious problems relating to land resource, water resource, environmental, and ecological sustainability. Coal resource exploitation causes the permanent fracture and movement of strata structure, which have caused the fracture and collapse of overlying strata and further led to the subsidence of ground surface as well as the seepage of water in aquifers around the coal seam, which has resulted not only in the loss of land and water resources, but also in serious threats and accidents to underground mining. On the other hand, mining and mineral-processing wastes are one of the world's long plagued concerns among solid wastes. Coal gangue, as the major waste with a huge amount of discharge, has not only occupied the land, but has also contaminated the ambient land resources and hydrological environment, and further led to ecological system destruction and degradation. What is more, in China there are large amounts of coal-located under railways, buildings, and water bodies-which are unavailable with traditional mining methods. These problems are obviously threaten the concept of green sustainable development. This paper introduces a novel developed solid dense stowing mining method, which is able to significantly reduce or event eliminate the corresponding damages caused by underground mining behavior and realize green and sustainable development. The novelty of this research work is realizing the automation and synchronization of mining and material stowing with an appropriate compaction ratio for adequate support of goaf roof. It can improve the stability of rock strata and the safety and efficiency of underground mining. We also studied and designed a perfect stowing material by using coal gangue and fly ash with appropriate proportions under different particle size gradations. By implementation of the above-mentioned methods in China, the solid dense stowing rate of mined seam areas have reached more than 95% and the overburden strata movements have been reduced to extremely low level which had nearly no damages to above buildings. The solid dense stowing mining method has also realized the reuse and recycling of coal mine solid wastes. Meanwhile, considerable previously unavailable coal resources under buildings, railways, and water bodies have been made available for exploration, which could extend the life of coal mines and increase the sustainability for coal industry and the environment. Ultimately, this method is a reliable way to realize green and sustainable mining. The strata structure protection, the surface subsidence prevention, and coal mine solid waste disposal have been realized at the same time.

This paper concentrates on a study of a novel multi-sensor aided method by using acoustic and visual sensors for detection, recognition and separation of End-of Life vehicles’ (ELVs) plastic materials, in order to optimize the recycling rate of automotive shredder residues (ASRs). Sensor-based sorting technologies have been utilized for material recycling for the last two decades. One of the problems still remaining results from black and dark dyed plastics which are very difficult to recognize using visual sensors. In this paper a new multi-sensor technology for black plastic recognition and sorting by using impact resonant acoustic emissions (AEs) and laser triangulation scanning was introduced. A pilot sorting system which consists of a 3-dimensional visual sensor and an acoustic sensor was also established; two kinds commonly used vehicle plastics, polypropylene (PP) and acrylonitrile-butadiene-styrene (ABS) and two kinds of modified vehicle plastics, polypropylene/ethylene-propylene-diene-monomer (PP-EPDM) and acrylonitrile-butadiene-styrene/polycarbonate (ABS-PC) were tested. In this study the geometrical features of tested plastic scraps were measured by the visual sensor, and their corresponding impact acoustic emission (AE) signals were acquired by the acoustic sensor. The signal processing and feature extraction of visual data as well as acoustic signals were realized by virtual instruments. Impact acoustic features were recognized by using FFT based power spectral density analysis. The results shows that the characteristics of the tested PP and ABS plastics were totally different, but similar to their respective modified materials. The probability of scrap material recognition rate, i.e., the theoretical sorting efficiency between PP and PP-EPDM, could reach about 50%, and between ABS and ABS-PC it could reach about 75% with diameters ranging from 14 mm to 23 mm, and with exclusion of abnormal impacts, the actual separation rates were 39.2% for PP, 41.4% for PP/EPDM scraps as well as 62.4% for ABS, and 70.8% for ABS/PC scraps. Within the diameter range of 8-13 mm, only 25% of PP and 27% of PP/EPDM scraps, as well as 43% of ABS, and 47% of ABS/PC scraps were finally separated. This research proposes a new approach for sensor-aided automatic recognition and sorting of black plastic materials, it is an effective method for ASR reduction and recycling.

With the increase the worldwide consumption of vehicles, end-of-life vehicles (ELVs) have kept rapidly increasing in the last two decades. Metallic parts and materials of ELVs can be easily reused and recycled, but the automobile shredder residues (ASRs), of which elastomer and plastic materials make up the vast majority, are difficult to recycle. ASRs are classified as hazardous materials in the main industrial countries, and are required to be materially recycled up to 85–95% by mass until 2020. However, there is neither sufficient theoretical nor practical experience for sorting ASR polymers. In this research, we provide a novel method by using S-Band microwave irradiation together with 3D scanning as well as infrared thermal imaging sensors for the recognition and sorting of typical plastics and elastomers from the ASR mixture. In this study, an industrial magnetron array with 2.45 GHz irradiation was utilized as the microwave source. Seven kinds of ELV polymer (PVC, ABS, PP, EPDM, NBR, CR, and SBR) crushed scrap residues were tested. After specific power microwave irradiation for a certain time, the tested polymer materials were heated up to different extents corresponding to their respective sensitivities to microwave irradiation. Due to the variations in polymer chemical structure and additive agents, polymers have different sensitivities to microwave radiation, which leads to differences in temperature rises. The differences of temperature increase were obtained by a thermal infrared sensor, and the position and geometrical features of the tested scraps were acquired by a 3D imaging sensor. With this information, the scrap material could be recognized and then sorted. The results showed that this method was effective when the tested polymer materials were heated up to more than 30 °C. For full recognition of the tested polymer scraps, the minimum temperature variations of 5 °C and 10.5 °C for plastics and elastomers were needed, respectively. The sorting efficiency was independent of particle sizes but depended on the power and time of the microwave irradiation. Generally, more than 75% (mass) of the tested polymer materials could be successfully recognized and sorted under an irradiation power of 3 kW. Plastics were much more insensitive to microwave irradiation than elastomers. With this method, the tested mixture of the plastic group (PVC, ABS, PP) and the mixture of elastomer group (EPDM, NBR, CR, and SBR) could be fully separated with an efficiency of 100%.