Research progress on in-situ intelligent sorting and filling technology of coal gangue underground

Coal gangue needs to be transported to the ground for further treatment in traditional underground mines, which not only occupies land spaces, but also causes atmospheric and environmental pollutions due to spontaneous combustion and rainwater leaching. Moreover, energy consumption problems caused by long-distance ineffective transportation have become a key bottleneck restricting the low-carbon development of coal mines. In order to realize the underground disposal of coal gangue, and to reduce carbon emission and energy resource consumption per unit output from the source of coal production, as well as to realize the green low-carbon intelligent mining of coal, the present situation and intelligent progress of underground sorting and filling technology of coal gangue are comprehensively reviewed, and in this regard, the developing trend of underground sorting and filling technology of coal gangue is also anticipated. Meanwhile, an innovative method of underground in-situ green intelligent sorting and filling of coal gangue is proposed, and the structure and principle of coal gangue intelligent sorting and new filling hydraulic support are described in detail, so as to minimize the invalid transportation distance of gangue. In order to deal with the gangue of coal mining face and prevent the dynamic disaster of coal and rock, an intelligent integrated system of mining, sorting and filling in coal mine is designed, including four subsystems, i.e., an intelligent mining system with less gangue, an in-situ intelligent sorting system, a mine pressure inversion system of working face and a precise scientific filling system. Meanwhile, new logic relationships among multiple subsystems are discussed, so as to form a virtuous cycle of coal mining conducive to sorting, sorting conducive to filling and filling conducive to coal mining. In order to deal with the gangue in the heading face, an intelligent integrated system of underground excavation, aimed at sorting and filling in coal mine is proposed also with four subsystems, i.e., an intelligent fast excavation system, an intelligent sorting system, a coal gangue transportation system and an intelligent filling system, and the work and intelligent realization of each subsystem are described. The proposed new process in this study is expected to realize the underground disposal of coal gangue, and provide new ideas for the research of in-situ intelligent sorting and filling method of coal gangue and the integrated system of mining, sorting and filling

Progress in dark tourism and thanatourism research: An uneasy relationship with heritage tourism

This paper reviews academic research into dark tourism and thanatourism over the 1996–2016 period. The aims of this paper are threefold. First, it reviews the evolution of the concepts of dark tourism and thanatourism, highlighting similarities and differences between them. Second it evaluates progress in 6 key themes and debates. These are: issues of the definition and scope of the concepts; ethical issues associated with such forms of tourism; the political and ideological dimensions of dark tourism and thanatourism; the nature of demand for places of death and suffering; the management of such places; and the methods of research used for investigating such tourism. Third, research gaps and issues that demand fuller scrutiny are identified. The paper argues that two decades of research have not convincingly demonstrated that dark tourism and thanatourism are distinct forms of tourism, and in many ways they appear to be little different from heritage tourism

Critical metal requirement for clean energy transition: A quantitative review on the case of transportation electrification

The clean energy transition plays an essential role in achieving climate mitigation targets. As for the transportation sector, battery and fuel cell electric vehicles (EVs) have emerged as a key solution to reduce greenhouse gasses from transportation emissions. However, the rapid uptake of EVs has triggered potential supply risks of critical metals (e.g., lithium, nickel, cobalt, platinum group metals (PGMs), etc.) used in the production of lithium-ion batteries and fuel cells. Material flow analysis (MFA) has been widely applied to assess the demand for critical metals used in transportation electrification on various spatiotemporal scales. This paper presents a quantitative review and analysis of 78 MFA research articles on the critical metal requirement of transportation electrification. We analyzed the characteristics of the selected studies regarding their geographical and temporal scopes, transportation sectors, EV categories, battery technologies, materials, and modeling approaches. Based on the global forecasts in those studies, we compared the annual and cumulative global requirements of the four metals that received the most attention: lithium, nickel, cobalt, and PGMs. Although major uncertainties exist, most studies indicate that the annual demand for these four metals will continue to increase and far exceed their production capacities in 2021. Global reserves of these metals may meet their cumulative demand in the short-term (2020–2030) and medium-term (2020–2050) but are insufficient for the long-term (2020–2100) needs. Then, we summarized the proposed policy implications in these studies. Finally, we discuss the main findings from the four aspects: environmental and social implications of deploying electric vehicles, whether or not to electrify heavy-duty vehicles, opportunities and challenges in recycling, and future research direction

Empirical Variational Mode Decomposition Based on Binary Tree Algorithm

Aiming at non-stationary signals with complex components, the performance of a variational mode decomposition (VMD) algorithm is seriously affected by the key parameters such as the number of modes K, the quadratic penalty parameter α and the update step τ. In order to solve this problem, an adaptive empirical variational mode decomposition (EVMD) method based on a binary tree model is proposed in this paper, which can not only effectively solve the problem of VMD parameter selection, but also effectively reduce the computational complexity of searching the optimal VMD parameters using intelligent optimization algorithm. Firstly, the signal noise ratio (SNR) and refined composite multi-scale dispersion entropy (RCMDE) of the decomposed signal are calculated. The RCMDE is used as the setting basis of the α, and the SNR is used as the parameter value of the τ. Then, the signal is decomposed into two components based on the binary tree mode. Before decomposing, the α and τ need to be reset according to the SNR and MDE of the new signal. Finally, the cycle iteration termination condition composed of the least squares mutual information and reconstruction error of the components determines whether to continue the decomposition. The components with large least squares mutual information (LSMI) are combined, and the LSMI threshold is set as 0.8. The simulation and experimental results indicate that the proposed empirical VMD algorithm can decompose the non-stationary signals adaptively, with lower complexity, which is O(n2), good decomposition effect and strong robustness

Emerging information and communication technologies for smart energy systems and renewable transition

Since the energy sector is the dominant contributor to global greenhouse gas emissions, the decarbonization of energy systems is crucial for climate change mitigation. Two major challenges of energy systems decarbonization are renewable transition planning and sustainable systems operations. To address the challenges, incorporating emerging information and communication technologies can facilitate both the design and operations of future smart energy systems with high penetrations of renewable energy and decentralized structures. The present work provides a comprehensive overview of the applicability of emerging information and communication technologies in renewable transition and smart energy systems, including artificial intelligence, quantum computing, blockchain, next-generation communication technologies, and the metaverse. Relevant research directions are introduced through reviewing existing literature. This review concludes with a discussion of the industrial use cases and demonstrations of smart energy technologies

A Fast Sparse Decomposition Based on the Teager Energy Operator in Extraction of Weak Fault Signals

In order to diagnose an incipient fault in rotating machinery under complicated conditions, a fast sparse decomposition based on the Teager energy operator (TEO) is proposed in this paper. In this proposed method, firstly, the TEO is employed to enhance the envelope of the impulses, which is more sensitive to frequency and can eliminate the low-frequency harmonic component and noise; secondly, a smoothing filtering algorithm was adopted to suppress the noise in the TEO envelope; thirdly, the fault signal was reconstructed by multiplication of the filtered TEO envelope and the original fault signal; finally, sparse decomposition was used based on a generalized S-transform (GST) to obtain the sparse representation of the signal. The proposed preprocessing method using the filtered TEO can overcome the interference of high-frequency noise while maintaining the structure of fault impulses, which helps the processed signal perform better on sparse decomposition; sparse decomposition based on GST was used to represent the fault signal more quickly and more accurately. Simulation and application prove that the proposed method has good accuracy and efficiency, especially in conditions of very low SNR, such as impulses with anSNR of −8.75 dB that are submerged by noise of the same amplitude

Modeling of Torque Output and Magnetic Force for Novel Spherical Actuator with Three-dimensional Pole Arrays

A novel PM spherical actuator based on three-dimensional pole array is proposed and developed in this paper. Conventionally, 2D pole arrays are widely employed in the design of spherical actuators, which constraints the system torque output greatly. The concept of 3D pole array is aimed at improving the torque performance. The torque has been analyzed and the corresponding analytical model is established based on curve fitting method (CFM) due to the importantance to real-time control. Magnetic force has been studied in a similar way. The results shows that the modeling method has a relatively high precision and can be further used in the real-time control

Improved Empirical Formula Modeling Method Using Neuro-Space Mapping for Coupled Microstrip Lines

In this paper, an improved empirical formula modeling method using neuro-space mapping (Neuro-SM) for coupled microstrip lines is proposed. Empirical formulas with correction values are used for the coarse model, avoiding a slow trial-and-error process. The proposed model uses mapping neural networks (MNNs), including both geometric variables and frequency variables to improve accuracy with fewer variables. Additionally, an advanced method incorporating simple sensitivity analysis expressions into the training process is proposed to accelerate the optimization process. The experimental results show that the proposed model with its simple structure and an effective training process can accurately reflect the performance of coupled microstrip lines. The proposed model is more compatible than models in existing simulation software