Abstract: Ningxia province is a major coal-producing province in Northwest China that contributes significantly to China’s economic development. However, long-term coal mining in Ningxia province has brought many environmental issues, such as surface collapse, destruction of water resources, air pollution, and random stacking of waste coal gangue. Hence, promoting the coordinated development of coal mining and environmental protection and improving the overall level of green coal development have become two critical issues for the development of the coal industry in this region. According to the current situation of coal mining in Ningxia province, the technical problems that require immediate resolution, such as the bottleneck of basic theory and technology, imperfect technical standards and norms of green development, insufficient support of green development technique and equipment, and lack of demonstration guidance and policy support were summarized. Moreover, the strategic thought, development blueprint, and strategic objectives of green development of coal mines were put forward based on the realistic demand of future development of the coal industry in Ningxia province, forming the overall strategic layout in this region. On this basis, the technical paths for future green development of coal mines in Ningxia province were proposed from various perspectives, primarily water resources protection technology of coal mining, large-scale and low-cost treatment technology of mine water, surface ecological restoration technology of mining area, intelligent and green mining technology, and clean coal technology. Finally, some feasible suggestions for the green development of coal mines in Ningxia province were proposed. We should clarified the concept and goal of green development and actively implement green mining to promote the ecological environment and ecological civilization to a new level and realize the high-quality, sustainable development of the coal industry in Ningxia province.
Abstract: In China, solid waste backfill technology has been used in metal mines for more than half a century. It has been essential for comprehensive excavation and utilization of mineral resources, environmental protection, and engineering safety. The core concept of solid waste filling has not changed qualitatively despite the rapid development of mining technology from semi-mechanized and mechanized automation and intelligence. It is mainly used in mine tailings, waste rock, and other solid wastes to control the hazards of goaf and tailings reservoir from the source to treat waste and hazards. However, the quality requirements, degree of fine control, and intelligent allocation technology of solid waste filling are undergoing considerable changes. Furthermore, the design philosophy of modern backfill technology consider comprehensive realization of solid waste utilization, environment conservation, mined-out area disposal, resource recovery, and ground pressure control that require optimal backfill method, sufficient mechanical strength, and pumpable backfill slurry, using information technology, automatic technology, and equipment technology. Besides, new backfill concepts are introduced, such as collaborative paste and multimaterial, synchronous, and functional backfills. In this paper, the framework of backfill mechanics was described, relying on rheology and solid mechanics. The influences of in-situ multifield factors were analyzed based on the characteristics of the in-situ stope, introducing the latest multifield coupling monitoring system. More details about the development trend and research status of critical processes in the backfill, including deep thickening, mixing, and long-distance pipe transport, have been updated. We propose a picture for the future development of intelligent backfill based on the advantages of intelligent backfill and performance of intelligent algorithms in the backfill field. In terms of the future trend of solid waste backfill, we feel that it can enrich green mining, explore the realization of modular, scale, and intelligent backfill, and actively examine and serve the needs of deep mining. Thus, the backfill method appears as the optimal choice for deep mining and green mining.
Abstract: To study the Brazilian splitting characteristics of sandstone under the coupling effect of high temperature and sandstone’s size, Brazilian splitting laboratory tests were carried out on standard sandstone specimens treated at 25, 200, 400, 600, 800, and 1000 ℃, respectively. A Brazilian splitting numerical simulation of sandstone with different sizes under high temperature was carried out based on particle flow software to study the Brazilian splitting strength and deterioration law of sandstone. In addition, the hysteresis law of porosity rise relative to the crack propagation and penetration was also investigated. Results are as follows: (1) In the temperature range of 25?1000 ℃ and in the diameter range of 50–100 mm, the temperature and size significantly affect the Brazilian splitting strength of sandstone, with size having a greater influence. During the heating process, due to the initial thermal expansion in the rock and subsequent damage under the action of thermal stress, the splitting strength of sandstone first increases and then decreases by approximately 34.66%–35.10% after 400 ℃. With the increase in the size, the energy accumulated in the rock is released, and a large number of microfractures are produced, resulting in decreasing the splitting strength of sandstone samples by approximately 55.61%–56.99%. (2) The relationship between the degradation amplitude of the Brazilian splitting strength of sandstone and its diameter satisfies a negative exponential function, which can predict the Brazilian splitting strength of sandstone with different sizes at high temperatures. (3) The porosity of sandstone increases during Brazilian fracturing, and the load difference relative to fracture propagation and penetration increases with increasing temperature and size. Considering the coupling effect of the two factors, the influence of size and temperature on the load difference decreases with increasing temperature and sandstone’s size. This study is of high significance for roof maintenance and preliminary prediction of the roof strength after a fire. In addition, it can also provide a useful reference for rock engineering design involving high temperatures and size changes, such as nuclear waste treatment, geothermal resource development, and deep well engineering.
Abstract: Improvements in ore blending could be realized by an optimal match of iron ores, sinters, and fuel conditions. To further increase iron grade and reduce the cost of ore blending, in view of the actual raw material and fuel conditions of the 500 m2 large-scale sintering machine of S Steel company, the conventional physical and chemical properties of the iron ore powders used and their basic characteristics under high-temperature sintering were studied using sintering cup experiments in this study. The content of the adhesion powder, the theoretical liquid phase formation, and performance with different limonite ratios were simulated and calculated using the FactSage 7.1 software. The microstructures of sinters were also analyzed using a mineral phase microscope. The results show that Australian limonite exhibits coarse particle size, weak mineralization ability, low assimilation temperature, poor bonding phase strength, but strong liquid phase absorption. When the mass fraction of limonite is increased from 45% to 55%, the OD ore mass fraction of the magnetite concentrate is increased to 15% and the mass fraction of the OC ore is reduced to 10%, improving the sinter drum strength and RDI+3.15 mm. When the OD ore ratio of the magnetite concentrate is increased, not only the proportion of adhesion powder is increased, improving the amount and performance of liquid phase formation, but the liquid phase distribution also becomes uniform and overmelting is eliminated. On the other hand, increasing the ratio of the OC ore can improve the particle size composition of the sinter mixture and reduce the amount of liquid phase absorbed by the limonite, thus increasing the strength of the sinter. Therefore, a higher ratio of magnetite concentrate under a high amount of limonite is conducive to stabilizing the sinter quality and improving the overall sinter performance.
Abstract: The effect of annealing time on the microstructure and mechanical properties of Ti?6.0Al?3.0Zr?0.5Sn?1.0Mo?1.5Nb?1.0V new titanium alloys were studied based on the optimum annealing temperature of 740 ℃. Results show that after smelting thrice by vacuum consumable arc furnace and thrice hot rolling processes, the microstructure of the sheet is the partial recrystallization structure composed of the primary α phase, structure of β transformation, and the processing status structure. With increased annealing time, the microstructure of the annealed sheet is mainly composed of the primary α phase, with the proportion of the α phase being gradually increased from 81.73% to 85.61%. The strip-shaped α phase in the microstructure is broken and spheroidized gradually, and an equiaxial α phase begins to be homogenized and coarsened. With the increase of annealing time, the elongation of annealed sheets increases greatly; the tensile strength initially decreases, increases, and then decreases again; and the yield strength and the microhardness first increase and then decrease. When the annealing time is 1 h, the fracture of the sheet has a ductile fracture mode and is composed of slip bands, ripple appearance, and small equiaxial dimples. When the annealing time is more than or equal to 2 h, the fracture exhibits a ductile fracture mode and is completely composed of equiaxial dimples. The optimal annealing process is achieved at 740 ℃ for 2 h, in which the tensile strength, yield strength, elongation, and microhardness of the alloy plate is 984 MPa, 941 MPa, 15.27%, and HV 347.67, respectively. The main results from this paper can guide the formulation of the annealing process of high-strength corrosion-resistant titanium alloy and provide a scientific basis for solving problems encountered in the actual production of titanium alloy.
Abstract: Cross-wedge rolling (CWR) die generally has three parts: a knifing section, a stretching section, and a finishing section. When forming an inside step, to avoid generating spiral steps, a new transitional section is introduced between the knifing and finishing sections, during which the surface is cut in the same shape as the inside step. The resulting surface is called the shaping surface, and its intersection with the base surface of the die is called the shaping curve. The rolling of the inside right-angle step has long been a key technology of CWR. The general formula and algorithm for the rolling alignment curve are not suitable for producing small right-angle steps. To solve this problem, we improve the geometric model and propose a new method for calculating the volume of the spiral cone of the small right-angle step. Based on the characteristics of the CWR process, the initial radius of the rolled product is compared with the radius of the corresponding auxiliary circle to preliminarily determine the conditions required for the small inside right-angle step. Based on the relationship between the radius of large section and the rotation angle, the shaping process is divided into three phases, the volume formulas for which are deduced by dividing the spiral cone into three regular volumes. Based on the volume fixedness theory, an accurate shaping curve of the small right-angle step is obtained by changing the rotation angle of the rolled piece. Finally, the finite element software Deform-3D is used to simulate the large diameter part within a certain area reduction range, the results of which verify the applicability of the proposed calculation method. The results of a comparative analysis also reveal that the stretching angle should be as small as possible when producing large-diameter shaft parts with small right-angle steps.
Abstract: With the advancement of blockchain, smart contracts have become increasingly popular. However, the uncertain status by law severely limits their practical applications. To address the problem, smart legal contract (SLC) is proposed as a transitional technology between legal and smart contracts. Starting with the basic concept of SLCs, in this paper, we discussed the legalization of smart legal contracts based on the requirements of existing legislation items and highlight that legalization should meet three elementary principles, including the specified grammatical requirements (for regulating terminology and eliminating ambiguity), the principle of nonempowerment (for resolving the inherent contradiction between automatic execution and the rights of parties), and examination criteria (for handling legal validity and code security issues). Moreover, we analyzed SLC’s legal effect by taking typical smart legal contract languages, SPESC and CML as examples, and show that the contract program or chaincode has the same legal effect as the original contract, if and only if they satisfy three necessary conditions: (1) adopting the technical specification for generation and conclusion of SLCs; (2) complying with three abovementioned elementary principles; and (3) agreeing on declaration with the same legal effect. Furthermore, investigating the smart contract architecture and deployment, the legal status of both contract program and compiled chaincode was demonstrated in legal analysis of the deployed smart contract. Last, we discussed and evaluated the current situation of smart legal contract logic models and language models on SLCs. This work shows that the research on smart legal contracts is a suitable approach to guarantee the legal status of smart contracts, and the results will contribute to grasping the future research directions in several fields, such as contract logic, arbitration process, and formal verification, from the existing legislation viewpoint.
Abstract: As the service time of military equipment increases, equipment failure data is continuously accumulated during events such as routine maintenance, training, and combat readiness exercises, and the data presented is often imbalanced to varying degrees and consists of small samples. In addition, due to fault tolerances of various electrical component parameters in the equipment and widespread nonlinearity and feedback loops of the circuit, it is often difficult to accurately express the fault mechanism using mathematical models. This poses new challenges for the fault diagnosis of equipment. To address the aforementioned problems, machine learning methods are widely used for fault diagnosis. The essence of such methods is that they transform a fault diagnosis problem into a pattern recognition problem. By learning the characteristic data of normal modes and various failure modes, a diagnosis model is constructed and, ultimately, a diagnosis strategy is formed. Aiming at the problems of the unbalanced distribution of various fault samples from equipment and low fault diagnosis accuracy of existing algorithms, in this paper, we define a regularized weighted multiple kernel ensemble under a p-norm constraint by introducing a p-norm constraint weighted multicore extreme learning machine and an ensemble learning strategy based on the AdaBoost fault diagnosis model of extreme learning machine. Under the p-norm constraint, the model performed two types of adaptive sample weight distribution based on the size of various fault samples; simultaneously, the model combines the multisource data fusion and extreme learning abilities of the multiple kernel learning machine with high efficiency. The weight of a sample, W , is integrated into the optimization objective function of the multiple kernel extreme learning machine. Through the Adaboost integration strategy, the information-rich sample in the model is adaptively improved. Thus, the weight of a sample significantly improves the accuracy of fault diagnosis. Taking 6 UCI public data sets and 1 actual installation case as examples, a fault diagnosis experiment was conducted. The results of the experiment show that the model constructed in this study has significantly improved diagnostic accuracy compared with other models such as kernel extreme learning machine, weighted kernel extreme learning machine ($ {{\boldsymbol{W}}^{\left( 1 \right)}} $ and $ {{\boldsymbol{W}}^{\left( 2 \right)}} $ weighting method), and weighted multiple kernel extreme learning machine under 1-norm constraint, and the model’s diagnostic performance impact is limited.
Abstract: With the rapid development of Internet of Things technology, the use of front-end sensors realizes the corrosion potential online detection of low alloy steels in a marine environment, thereby obtaining multitudes of corrosion data. Concerning the problems of data information loss and modeling accuracy reduction caused by the use of the traditional mean value method when processing dual-rate corrosion data, a new dual-rate data processing and modeling algorithm combining the comprehensive index value (CIV) and improved relevance vector regression (IRVR) was proposed. First, the CIV was constructed to characterize the comprehensive influence of the input data, and the beetle antennae search (BAS) algorithm was applied to optimize its parameters. Then, linear regression models between the best CIV sequence and the output data were established to convert the dual-rate corrosion data into single-rate data for modeling, which retained more information of the original corrosion data. Finally, the IRVR method based on BAS optimization of compounding kernels was given to establish the prediction model for dual-rate seawater corrosion data of low alloy steels. The results show that the proposed model CIV-IRVR increases the number of modeling samples from 196 for the mean value method to 1834. Moreover, the mean absolute error, root mean square error, and coefficient of determination of the CIV-IRVR model are 1.1914 mV, 1.5729 mV, and 0.9963, respectively, which outperforms commonly used comparison algorithms, such as the artificial neural network (ANN) and support vector regression (SVR). Moreover, the CIV-IRVR model can help obtain the prediction results with error bars, and it has the absolute error distribution closest to 0, which highlights its excellent predictive performance on the seawater corrosion potential of low alloy steels. Thus, the proposed model not only reduces the information loss and improves the modeling accuracy but also has practical significance for modeling dual-rate seawater corrosion data.
Abstract: With the rapid development of machine learning and deep neural network and the popularization of intelligent devices, face recognition technology has rapidly developed. At present, the accuracy of face recognition has exceeded that of the human eyes. Moreover, the software and hardware conditions of large-scale popularization are available, and the application fields are widely distributed. As an important part of face recognition technology, facial expression recognition has been a widely studied subject in the fields of artificial intelligence, security, automation, medical treatment, and driving in recent years. Expression recognition, an active research area in human–computer interaction, involves informatics and psychology and has good research prospect in teaching evaluation. Micro-expression, which has great research significance, is a kind of short-lived facial expression that humans unconsciously make when trying to hide some emotion. Different from the general static facial expression recognition, to realize micro-expression recognition, besides extracting the spatial feature information of facial expression deformation in the image, the temporal-motion information of the continuous image sequence also needs to be considered. In this study, given that static expression features lack temporal information, so that the subtle changes in expression cannot be fully reflected, facial dynamic expression sequences were used to fuse spatial features and temporal features, and neural networks were used to provide good features in the field of image classification. Expression sequences were processed, and a micro-expression recognition method based on separate long-term recurrent convolutional network (S-LRCN) was proposed. First, the micro-expression data set was selected to extract the facial image sequence, and the transfer learning method was introduced to extract the spatial features of the expression frame through the pre-trained convolution neural network model, to reduce the risk of overfitting in the network training, and the extracted features of the video sequence were inputted into long short-term memory (LSTM) to process the temporal-domain features. Finally, a small database of learners’ expression sequences was established, and the method was used to assist teaching evaluation.
Abstract: The flight operation risk is equal to the occurrence probability multiplied by the severity of the consequences. Flight operation risks include many types, forms, and numbers, and they frequently change with conditions. In the face of this complex system, through principle analysis, the risk formation mechanism research, and the spreading process, a scientific risk management and control method can be constructed. Based on the risk management technology, an informative and automated management control system can be developed and applied. The overall safety level of flight operations will be effectively improved. To analyze and study the flight operations risk propagation and then effectively control flight safety based on the complex network theory, 29 terminal factors were selected as network nodes according to the Civil Aviation Administration’s advisory notice, initially including the flight cabin crew, civil aviation aircraft, and operating environment. Civil aviation safety monitoring records from 2009 to 2014 were counted, and an undirected network was constructed based on node relationships. The relationships and occurrence probability between the nodes were counted, and a directed and weighted network was constructed. The concepts of improved infection rate and improved recovery rate were introduced, and an improved susceptible-infected-recovered (SIR) model suitable for flight operation risks was proposed. Finally, the initial infection range was clearly defined, and a multi-parameter control method was adopted. For directed networks, large-scale propagation and control simulations were calculated. The results indicate that the average shortest path of the directed network was 1.788, which belonged to the small-world network. The directed network infection node decreased to 37.4% with conventional control measures. After controlling top three or four nodes of the entry degree value sequence, the infected nodes peak drop rate was the biggest, as high as 50.6%/58.1%, the risk spread in the network was significantly suppressed. The results confirm that controlling nodes based on the entry degree value is the most effective method to suppress risk propagation in the directed and weighted network.
Abstract: Horizontal directional drilling (HDD) technology is widely used in the construction and maintenance of municipal lifeline projects, the laying of long-distance oil and gas pipelines, and directional survey of mountains. It is one of the important technologies in the trenchless engineering field. Nearly 30 a of development of China’s HDD technology has made significant progress in the research and application of equipment and technology, creating a world record. The present work presents literature research considering 6 aspects: HDD equipment technology, detection and informationization technology of underground lifeline engineering, bi-directional crossing technology, large caliber HDD technology, HDD drag force calculation model, surface deformation and mud spillover. Moreover, this work analyzed the HDD research and applications progress of equipment and technology. In terms of the HDD equipment, the world’s largest drag force (20000 kN) electric drive drilling rig has been designed and developed. Based on the material properties of underground lifeline systems, electromagnetic induction methods are widely used for geospatial detection of existing lifelines. However, data analysis and accuracy improvement under complex interference still need to be investigated. Based on three-dimensional data, integrating building information modeling with artificial intelligence, big data and other technologies, and learning from the US “811” system the informatization of underground lifelines has been partially completed. Long distance underground lifeline engineering has been laid using bi-directional crossing technology. Process-based HDD process parameters, equipment parameters, and the control and monitoring technology have been widely used to effectively improve the HDD applications’ risk identification capabilities. The drag force calculation under different stratum conditions provides a basis for the equipment selection and facilitates HDD multidisciplinary research. In addition, preliminary explorations on hotspots with difficulties, such as slurry eruption under complex geological conditions, have been conducted. Theoretical, experimental, and numerical analysis models have also been constructed to provide a basis for improving the application efficiency and quality of HDD. Based on the abovementioned progress, this paper further analyzes the development trends of HDD technology.
Abstract: The Brazilian splitting test is widely used to determine the tensile strength of rocks and rock-like materials due to its easy sample preparation and an easier compressive test setup as an indirect testing method compared with performing a direct uniaxial tensile test. However, the accuracy of this method has also been criticized for a long time in the literature since its introduction. This paper carried out two-dimensional (2D)/three-dimensional (3D) numerical simulations of the Brazilian tensile test using a continuum elastoplastic analysis to reveal the variation of fracture modes of the Brazilian disk and its fracture evolution process. The effect of compression-tension ratios and contact loading angles on the fracture modes of the disk specimens was studied through 2D simulations. Through 3D simulations, the initiation and expansion processes of the 3D fracture under different loading angles were explored. The simulated results of failure modes, stress distributions, and calculated tensile strengths were analyzed. The 2D numerical results show that the larger the contact loading angle and the compression–tension ratio, the more likely the Brazilian disk specimens crack first at the disk center. The fracture initiation under the loading rims is caused by shear failure, but further propagation of the split fracture is driven by tension failure. The 3D numerical simulation results show that the crack initiation point is always located on the end face of the disk and gradually moves to the center from the loading ends as the loading angle increases. When the central tensile cracking appears, the 3D fracture expanded toward the inside of the specimen with an arc boundary. Regardless of whether the disk specimen starts to fracture initially at the disk center or the loading points, the Brazilian tensile test may underestimate the tensile strength of rocks due to the 3D effect.
Abstract: The hierarchical loading compression creep test fails to fully consider the viscoplastic strain in a stable creep. Thus, the triaxial cyclic loading compression creep test is adopted to realize the separation of viscoelastic and viscoplastic strain to fully consider the two strains in each stage of the creep. With the development of hydropower project construction in China being moved towards the mountain valley, the geological conditions and engineering environment faced by geotechnical engineering become more complex. Moreover, in the process of geotechnical engineering design, engineering construction, and safe operation, the effect of the rheological, mechanical properties of rock becomes more difficult to ignore. This engineering problem is becoming more significant for the staff involved. Therefore, rheological, mechanical properties of rock have become a very important research content. This study took the diorite porphyry of one hydropower station as an example to discuss the creep characteristics of this type of rock. Before the failure, the instantaneous elastic strain and instantaneous plastic strain increase linearly as the deviator stress gradually increases. With increased deviator stress, the viscoelastic strain and viscoplastic strain exhibit a nonlinear increase. The fractional Abel viscous pot and Kelvin model were introduced to form a new viscoelastic model. The fractional Abel viscous pot was used to replace the linear Newtonian fluid in the traditional viscoplastic model, and a viscoplastic damage model was established based on the damage variables. The new viscoelastic plastic model and viscoplastic damage model were then connected in series with the transient elastic model and transient plastic model to form a new rock creep damage model. Finally, the model is fitted with the rock creep curve to prove the applicability of the model.
Abstract: Antibiotic residue, a kind of biomass, is classified as hazardous waste. However, it is considered a good biomass resource because it contains rich organic matter and bacterial protein with a calorific value equivalent to that of low-rank coal. The hydrothermal method uses high-temperature liquid water as the reaction medium and reactant, which has the characteristics of high energy, fast reaction speed, large material flux, convenient feeding, and high product separation efficiency, especially avoiding the evaporation of high water content of aquatic substances. Although bio-oil obtained from the noncatalytic hydrothermal process has a high calorific value, it exhibits negative characteristics, such as high oxygen and nitrogen and high viscosity, which makes it unsuitable for use as a fuel. Therefore, catalysts are needed to improve the quality of bio-oil. This paper investigates the hydrothermal liquefaction of bacterial residues into bio-oil under a retention time of 30–240 min at 220–300 °C. Results show that the maximum yield of bio-oil is 28.01% at 260 °C for 135 min. Catalyzed by six kinds of catalysts (HCOOH, CH3COOH, K2CO3, Na2CO3, NaOH, and KOH), the highest yield of bio-oil is achieved with Na2CO3 (36.06%) and NaOH (36.31%). The content of hydrocarbons and their derivatives in the produced bio-oil is found to be relatively low at varying amounts of Na2CO3 and NaOH catalysts. The mass fraction of nitrogen-containing compounds in the alkali-catalyzed and acid-catalyzed bio-oil is 41.16%–49.74% and 57.62%–59.32%, respectively, with the best nitrogen removal obtained at a mass dosage of 8%. In particular, the contents of nitrogen compounds in the bio-oil catalyzed by Na2CO3 and NaOH are 29.12% and 35.67%, respectively. The best removal effect of oxygen is achieved at a dosage of 10%. Specifically, bio-oil components produced by Na2CO3 and NaOH contains 32.12% and 29.02% oxygen-containing compounds, respectively. Moreover, the higher heating value (HHV) of bio-oil produced with these catalysts is the largest, with an HHV of 33.3220 and 34.7320 MJ?kg?1 for Na2CO3 and NaOH, respectively.
Monthly, started in 1955 Supervising institution：Ministry of Education Sponsoring Institution：University of Science and Technology Beijing Editorial office：Editorial Department of Chinese Journal of Engineering Publisher：Science Press Chairperson：Ren-shu Yang Editor-in-Chief：Ai-xiang Wu ISSN 2095-9389CN 2095-9389
