Abstract: With the exhaustion of easy-to-treat gold ore resources, gold ores containing arsenic, carbon, high sulfur, and ultrafine particles have become the focus of gold mining. These difficult-to-treat gold ores have poor leaching effects through conventional cyanide leaching methods. The chemicals involved in the production process are highly toxic, which can endanger human health and seriously pollute the ecological environment. Some countries and regions have restricted or prohibited the use of cyanide to extract gold; besides, cyanide treatment contains copper, zinc, nickel, etc. When removing impurities from gold ores, these impurity metals will increase the consumption of cyanide and oxidant in the leaching system. At the same time, a film will be formed on the gold surface that hinders the leaching of gold in cyanide and reduces the leaching rate of gold. Gold-containing substances that are insoluble in the cyanide solution include antimony compounds, aurostibite, black bismuth gold ore, and gold-containing compounds formed during reduction roasting of lead, antimony, and arsenic minerals. Although these compounds are present in small quantities, they may account for a large proportion of the amount of gold lost during processing. Nevertheless, the cyanidation method is currently the main process for processing gold ores. Due to the existence of defects in the cyanidation method and the declining gold ore suitable for cyanide treatment, scholars from all over the world are studying alternatives for gold leaching to realize an efficient and environment-friendly recovery of gold in difficult-to-treat gold mines. This article summarized the methods of non-cyanide leaching of gold: thiosulfate method, glycine method, halide method, lime sulfur mixture method, iodination roasting process, biological oxidation thiourea leaching method, and non-aqueous gold leaching method. The gold leaching principles of seven non-cyanide gold leaching methods and the latest research progress in refractory gold mines were introduced. The development direction of non-cyanide leaching gold technology was prospected based on the problems of non-cyanide gold leaching methods such as expensive leaching agent, difficulty in recovering gold in leaching solution, complicated leaching system, unstable properties of leaching agent, and large consumption.
Abstract: With continuously increasing urban construction, more underground projects require the breaking of rocks near sensitive areas, such as hospitals, schools, and residential areas. On one hand, since conventional blasting that uses explosives has a negative impact on the safety of the surrounding buildings and brings about noise and flying rocks, the use of explosives are sometimes not allowed. On the other hand, the efficiency of mechanical rock excavation is very low, resulting in a low speed of rock excavation and high operation cost. In view of this situation, techniques that incorporate carbon dioxide phase transition fracturing have been tried in rock fragmentation in complex and sensitive environments such as those mentioned above. Furthermore, carbon dioxide phase transition fracturing is also regarded as an ideal substitute for the explosives in the field of coal permeability improvement. As an environmentally friendly rock-breaking technology, carbon dioxide phase transition fracturing has the advantages of high excavation efficiency, low vibration, and no pollution. In recent years, it has become a hot topic in the field of rock breakage and excavation. Research on this gas explosion technology has been developed rapidly and several useful progresses were made in this technology using theoretical analysis, experiments, and numerical simulations in a wide range. Through investigation and analysis of existing research results, the rock breakage mechanism of carbon dioxide phase transition fracturing was elaborated. A review was also presented on the fracturing load characteristics and its testing method. The main factors influencing the fracture load and fracture result were recapitulated. This review also analyzed the harmful effects of this new technique and generalized the applications of this technology in different fields. Finally, the problems and future challenges of carbon dioxide phase transition fracturing were discussed. The review aims to provide a reference for the theoretical research, generalization, and application of carbon dioxide phase transition fracturing technology.
Abstract: To explore the interaction between directional crack and open joint in slit charge blasting, a dynamic caustics method and high-speed photography technology were used to carry out the blasting model experiment. In the model experiment, the polymethyl methacrylate (PMMA) was used as the specimen material, the lead azide (Pb(N3)2) was used as the explosive, and a 3D printed resin material was used as the slit tube. Additionally, varying shapes of the open joint were prefabricated in the specimen. The failure mode of the specimen was described in detail, the initiation and propagation process of the wing crack at two ends of the joint was also analyzed. Research results show that the open joint has a retarding effect on the propagation of the directional crack in slit charge blasting. The directional crack will not continue to propagate through the open joint, but it will produce two wing cracks at the ends of the joint after interacting with the joint. When the directional crack is incident perpendicular to the open joint, the stress state at both the ends of the joint and the initiation and propagation behavior of the two wing cracks are the same, and the distribution state of the two wing cracks is symmetric. The geometric characteristics of the open joint have a significant impact on the dynamic stress intensity factor when the wing crack is initiated. Moreover, it determines the difficulty of the wing crack initiation to some extent. When the directional crack is incident obliquely, the stress state of the two ends of the joint is different. The end, which is close to the incident point of the directional crack, exhibits more crack initiation energy so that the crack is initiated and propagated preferentially and a longer wing crack is formed.
Abstract: The main reservoirs of oil and gas are in the pores and fractures of rocks. Under deep and complex stress environments, reservoir rock fracture permeability evolution directly affects the flow of oil and gas, which is an important research object of oil and gas exploration and development. In order to study the permeability evolution of fractured rock under complex stress paths, a permeability test of a single sample in the process of loading and unloading complex stress paths was performed using high-precision hydro-mechanics coupled with triaxial experimental equipment. The experimental scheme entails permeability tests under (i) increasing confining pressure; (ii) increasing liquid pressure; (iii) cyclic loading and unloading deviatoric stress; and (iv) increasing confining pressure and deviatoric stress synchronously. The results show that liquid flow in fractured mudstone can be regarded as laminar flow with low velocity. The sample containing more fracture (R2) has a significantly higher permeability and stress sensitivity. The permeability changes with both liquid and confining pressure as a function of positive and negative exponential functions. The increase in deviatoric stress leads to a decrease in permeability, and unloading causes permeability to increase. The whole evolution of permeability is irreversibly reduced. During the increasing confining pressure and deviatoric stress stage, permeability also decreases, and tends to stabilize. Under a confining pressure of 10.3 MPa, permeability remains basically constant. Therefore, based on the double medium model of fracture, the permeability evolution model of fractured rock was proposed considering the interaction among fracture system, matrix system, and the expansion deformation of fracture under external stress. The simulation results of the model are in good agreement with the experimental results. These results can provide an important theoretical basis for the prediction of permeability evolution of fractured mudstone and efficient oil and gas exploitation.
Abstract: The surface roughness of natural rock-fractures is an important factor affecting the fractured rock mass flow characteristics and further complicating the flow process in the natural fractures. To further study the influence of the fracture surface roughness on the permeability coefficient under uniaxial compression and different hydraulic pressures, 3D printing technology and digital modeling were utilized to prepare the fracture specimens with different fracture surface roughnesses and laboratory permeability tests were conducted through a self-made testing device under different normal pressures and different hydraulic pressures. The experimental results show that in the absence of normal pressure, the rough fracture specimens permeability decreases in a negative exponential form with the increase in the fracture surface roughness. The Forchheimer equation is used to quantitatively describe the nonlinear relationship between seepage flow rate and hydraulic gradients. The regression analyses of the experimental data indicate that the Forchheimer equation provides a good description of the flow process through the rough fracture surface. With the increase in the fracture surface roughness, the linear term coefficient decreases, while the nonlinear term coefficient increases. Under the conditions of fixed normal pressure and normal pressure greater than hydraulic pressure, the fracture specimens permeability decreases linearly with the increase in the fracture surface roughness, and the influence of the fracture surface roughness on the permeability increases with the increase in the hydraulic pressure. The coefficient $\delta $ was used to analyze the difference between the influences of fracture surface roughness on the permeability under normal pressure and without normal pressure. The coefficient $\delta $ increases with the increase in the hydraulic gradients and decreases with the increase in the normal pressure. The results can further clarify the fluid flow through rough fracture surfaces and provide a solid foundation for further research in the fields of rock mass flow characteristics.
Abstract: During the Al-killed steel continuous casting process, the molten steel corrosion and the accumulation of alumina inclusion deposits affect the submerged entry nozzle (SEN) wall surface, including the surface morphologies of the smooth wall, porous refractory wall, and clogged wall. The SEN wall surface morphology affects the boundary layer structure and alumina inclusions transport. In this study, a physical modeling method was adopted, and the surface morphologies simulation was realized by filling up the natural porous refractory material and inserting the real clog material in the polymethyl methacrylate SEN model. The velocity in the boundary layer was measured using the particle image velocimetry (PIV) technology, and the alumina inclusions transport path in the boundary layer was calculated by MATLAB software. The MATLAB codes combined the velocity data from the PIV measurement results and the inclusion transport equation. The four-quadrant analysis showed that sweep and ejection events existed in the boundary layer. The fluctuations of the velocity and the turbulent kinetic energy in the normal direction were increased in the porous refractory and the clogged wall boundary layer when the sweep and ejection events existed. The transport of the alumina inclusions with a diameter of 1–15 μm was affected by the ejection and the sweep events. The alumina inclusions moved toward the boundary in the sweep event. During the sweep event, the transport path of alumina inclusions with 1 μm diameter was close to the boundary; the alumina inclusions were more easily attached to the boundary. The alumina inclusions escaped from the boundary in the ejection event. In the porous refractory and the clogged walls, the alumina inclusion transport path in the normal direction was increased. When the SEN wall’s morphologies changed from smooth wall to porous refractory wall and clogged wall, the sweep event area proportion increased from 10.17% to 39.77%, and the ejection event area proportion decreased from 32.96% to 9.24%. Moreover, the sweep event’s probability increased from 25.83% to 28.24% when the morphologies of the SEN wall changed from smooth wall to porous refractory wall and clogged wall, which will increase the alumina inclusion deposition rate in the porous refractory wall and the clogged wall boundary.
Abstract: Organic contaminants such as dyes and antibiotics have become the focus of water treatment research in recent years due to their complex composition, high toxicity, and difficulty in biodegradation. Spinel ferrite heterogeneous Fenton-like catalysts, with a chemical formula of MFe2O4 (MFe2O4, M is a divalent metallic cation or its combination, and the divalent cation is generally Ni, Zn, Mn, Co, Cu, and Mg, etc.), have attracted much attention because of their excellent structural stability and good magnetic recovery performance. However, the catalytic activity of these catalysts is not ideal and almost all the reported catalysts are synthesized by pure chemical reagents, which restrict their industrial application. Therefore, the preparation of highly efficient heterogeneous Fenton-like catalysts with low cost becomes the key to the treatment of refractory organic wastewater. In this study, copper-doped spinel ferrite (Ni, Mg, Cu)Fe2O4 was successfully synthesized from nickel sulfide concentrate by a coprecipitation–calcination method. The effect of copper doping concentration on the structure, micro-morphology, and catalytic performance of as-prepared samples was systematically investigated by X-ray diffraction, scanning electron microscopy, and X-ray photoelectron spectroscopy. The optimal catalytic system was established as the photo-assisted Fenton-like catalytic system, “(Ni, Mg, Cu)Fe2O4 catalyst/H2O2/visible light”, and the enhancement mechanism of copper doping on the catalytic activity of (Mg, Ni)Fe2O4 was revealed. Results showed that all formed products were pure spinel ferrites under the selected synthesis conditions. With 1∶1 molar ratio of Ni to Cu, the formed (Ni, Mg, Cu)Fe2O4 catalyst achieved 94.5% degradation efficiency for 10-mg?L?1 RhB solution under visible light irradiation for 180 min. This observed behavior may be ascribed mainly to the increased relative contents of Fe3+ and Cu2+ ions at octahedral site. Hydroxyl radical (·OH) reaction accelerated due to increased amount of Fe3+ and Cu2+ exposed on the surface and enhanced synergetic effect between Fe3+ and Cu2+. This improved the degradation efficiency of RhB solution from 73.1% to 94.5%.
Abstract: Discarded walnut shells were modified by the chemical composition of special steel slag ultrafine powder to obtain steel-slag-based biomass-activated carbon. The influences of the mass ratio of discarded walnut shell ultrafine powder and special steel slag ultrafine powder, the fineness of special steel slag ultrafine powder, and adsorption ambient temperature on the absorbed chlorine gas performance of steel-slag-based biomass-activated carbon were studied. Results show good chlorine gas absorption performance when the mass ratio of discarded walnut shell ultrafine powder and special steel slag ultrafine powder is 100∶6, the fineness of special steel slag ultrafine powder is 600 mesh, and adsorption ambient temperature is 30 ℃. The magnetic property of Fe2O3 in special steel slag ultrafine powder is conducive to the formation and enrichment of chlorine gas on the surface of steel-slag-based biomass-activated carbon, improving its absorption performance. Catalytic performance of CuO and MnO helps promote the absorbing performance of steel-slag-based biomass-activated carbon. When the fineness of special steel slag ultrafine powder is excessively large, agglomeration of small particle size occurs and affects the adsorption capacity of steel-slag-based biomass-activated carbon to chlorine gas. When the particle size of special steel slag ultrafine powder is small, the special steel slag ultrafine powder with good uniformity is less effective in improving the adsorption of chlorine on the steel-slag-based biomass-activated carbon. The higher adsorption ambient temperature may lead to the analytical phenomenon of chlorine gas from steel-slag-based biomass-activated carbon. Moreover, no superfine agglomeration and deposition of special steel slag ultrafine powder on the surface of steel-slag-based biomass-activated carbon are observed. The obtained carbon exhibits the layered structure characteristics and provides space for chlorine gas adsorption.
Abstract: Natural fiber, as an alternative to synthetic fiber, is of great potential to reinforce composites that are applied in engineering fields such as automotive aerospace, automotive, sports, packaging, medical, and construction due to their renewability, environmental friendliness, high specific strength, and modulus. To realize this potential, ramie fiber reinforced poly (lactic acid) (PLA) composites with different fiber loadings were fabricated by injection molding. The heat deformation temperature, microstructure, crystallization behavior, rheological behavior, and mechanical properties of the composites were also analyzed. Results indicated that the heat resistance of the composites was improved with increased fiber loading. Particularly, the heat deformation temperature of the composites was improved by 10.5% when fiber with mass fraction of 40% was blended into the matrix. In addition, there were numerous fiber pull-outs and holes in the fractured surface due to poor interfacial adhesion between the fibers and PLA. Meanwhile, ramie fibers were uniformly distributed in the matrix when incorporating a low fiber content, but fiber agglomerations occurred in the matrix when introducing a high fiber loading (mass fraction of 40%) because of the poor wettability between the fibers and PLA. Differential scanning calorimetry (DSC) showed that the high fiber loading in the composites restricted the movement of the PLA molecular chain and promoted the formation of the perfect crystal. At the same time, samples with a high content of fiber contributed to the enhancement of the storage modulus, loss modulus, and complex viscosity of the composites due to the fibers’ physical joint in the matrix. Finally, the tensile and flexural strengths of the composites were improved with increased fiber loading. However, when the mass fraction of loading fiber was greater than 30%, the increase of tensile and flexural strengths of the composites was slow due to the weak wettability of the PLA matrix to the fiber. Compared to PLA, the incorporation of fiber with mass fraction of 40% increased the tensile and bending strengths of the composites by 30% and 21.9%, respectively.
Abstract: Carbon steels are widely used structural materials in vessel and marine engineering. Many studies acknowledge that the pitting corrosion of these materials are heavily subject to their metallurgic factors. Although much attention has been paid on inclusions and microstructure, little had been dealt with metallurgical processing including deoxidizing degrees. Deoxidization is one of the most important processes in steelmaking. Stronger deoxidizing degree helps to improve steel’s mechanical property and welding property. However, some studies demonstrated that weaker deoxidizing degree tends to improve pitting corrosion resistance. Manufacturing ordinary structural steels nowadays have been changed from mould casting to continuous casting. However, the deoxidizing degree of continuous casting steel may be different due to different deoxidization techniques. Particularly, the oxygen content in current steels has a fairly low level, which may be harmful to pitting corrosion resistance of steels. In this study, the influence of oxygen content in carbon hull steels on corrosion and mechanical properties of steel was investigated by mechanical properties tests and alternate immersion test. Results show increased oxygen content when there is duction of deoxidization of molten steel in the range permitted by continuous casting. Interestingly, the average corrosion rate of steel slightly decreases and an obvious enhancement of resistance to pitting is observed. The average pit depth corresponding to the high oxygen side of the pit depth-oxygen curve is about 22.7% lower than that of the low oxygen side. The mechanical and cold bending properties of tested steels are able to meet technical code requirements and can reach the level of Grade D hull steel. Findings of this study suggest that solid solution oxygen in steel plays a major role in improving pitting resistance. It can enhance the thermodynamic stability of iron, elevate the corrosion potential of the iron in the pit, and reduce the pitting rate. Therefore, using oxygen as a corrosion resistant element is an economic strategy to reduce the cost of corrosion resistant steel.
Abstract: The precision machining of the threaded cartridge relief valve sleeve is a manufacturing process of grinding after carbonitriding. The shape and position error of inner cone will affect the service life and static and dynamic characteristics of the relief valve. This requires the need of manufacturing process to accurately control the error of the inner cone. Based on the process analysis, a manufacturing error model was established and applied to obtain a reasonable error range of the inner cone angle and to determine the relationship between the inner cone angle error and the grinding amount. According to the structural characteristics of the valve sleeve, a special detection device was designed and the detection principle and measurement error were analyzed to improve the detection accuracy through error proofreading. After heat treatment, the valve sleeve was divided into axial size groups and the unified principle of datum was adopted to ensure the stability of grinding accuracy. According to the detection principle and error model, the error calculation of the grinding test piece was carried out, and the grinding parameters were adjusted accordingly to make a qualified manufacturing error. In the subsequent manufacturing, the axial dimension of the detection sealing circle of the valve sleeve is quickly measured by the detection device, so that the manufacturing error falls within the control range, ensuring the controllability of the batch production. Based on the design and process parameters of a relief valve, results reveal that the control error of the inner cone’s own angle should be ±0.8°. The corresponding maximum grinding tolerance value of the axial direction of the sealing circle is 0.186 mm, while the corrected minimum grinding tolerance is 0.075 mm. Through experiments, the accuracy of the error model is verified. The angle measurement error of the detection method is 0.06°, while the measurement error of the axial dimension of the sealing circle is 2 μm. The deviation of the maximum grinding amount and the minimum grinding amount range caused by the angle measurement error is compensated by the shrinkage of the actual manufacturing error of the inner cone angle. The theory and method also provide a systematic process for manufacturing control and reverse engineering of the other inner cone.
Abstract: The Li-ion battery is an important energy source for electric vehicles (EVs), and the accurate estimation of the battery power state provides a reliable reference for balancing the battery packing and battery management system (BMS). It also has great practical significance for making full and reasonable utilization of batteries, and improving the battery life cycle and vehicle operation efficiency. Practical issues that must be addressed include the filtering divergence caused by the non-positive definite error covariance matrix in the standard unscented Kalman filter (UKF) and the state estimation errors that accumulate from the simplified mathematical modeling of the Li-ion battery, with its inherently strong non-linearity, time variation, and uncertainty. To resolve these issues, in this article, a real-time state co-estimation algorithm was proposed based on a fast square-root unscented Kalman filter (SR-UKF) framework. First, during the iteration process, the non-linear measurement function, which describes the propagation of each sigma point, is called by an unscented transform. A reduction in computational complexity can be achieved if the non-linear measurement function is quasi-linearized. Second, instead of a state error covariance matrix, the square root of the state error covariance matrix is used, which is obtained by QR decomposition and first-order updating of the Cholesky factor. This step deals with the problem that arises if the state error covariance matrix is negative definite due to the computational errors accumulated while performing recursive estimation with the standard UKF. This guarantees the numerical stability of the battery’s estimated state of charge (SOC) in real time. Third, the inner ohmic resistance and nominal capacity that indirectly characterize the state of health can be estimated online, and a highly precise SOC estimation can be realized due to the accuracy and efficiency of the battery model. Comparative experimental results confirm and validate the feasibility and robustness of the proposed fast SR-UKF algorithm and co-estimation strategy.
Abstract: As a new generation of new energy battery, lithium-ion battery is widely used in various fields, including electronic products, electric vehicles, and power supply, due to its advantages of high energy density, light weight, long cycle life, small self-discharge, no memory effect, and no pollution. With the wide application of lithium-ion battery, numerous research on its performance has been done, including its health assessment as one of the hot spots. Repeated charging and discharging of a lithium-ion battery that was run under full charge state results to internal irreversible chemical changes leading to a fall in the maximum available capacity. Specifically, a decline to 70%–80% of the rated capacity results in lithium-ion battery failure. Battery failure may lead to electrical equipment damage, resulting in safety accidents. Therefore, it is of great significance to predict the remaining usable life of lithium-ion battery for improving system reliability. In this paper, a combination prediction model for lithium-ion batteries with multimode decomposition was presented based on the long and short-term memory (LSTM) prediction model to learn about small changes in its degradation process. A complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm was used to divide the capacity into main degradation trend and some local degradation trend. Long Short-Term Memory Neural Network (LSTMNN) algorithm was then introduced to perform the capacity prediction of decomposed degradation data. Finally, some prediction results were integrated effectively. The maximum mean absolute percentage error (MAPE) of the proposed CEEMDAN–LSTM lithium-ion battery combination prediction model does not exceed 1.5%. The average relative error is less than 3%, which is better than the other prediction model.
Abstract: Establishing the dynamics model of robot and its parameters is significant for simulation analysis, control algorithm verification, and implementation of human–machine interaction. Especially under various working conditions, the errors of the calculated predicted torque of each axis have the most direct negative effect. The general robot dynamics model rarely takes the minor and complex characteristics into consideration, such as the reducer flexibility, inertia force of motor rotors, and friction. However, as the structure of collaborative robots is lighter and smaller than the ordinary industrial robots, the characteristics neglected by general dynamics models account for a relatively large amount. The above facts result in a large error in the calculation and prediction of collaborative robots analysis. To address the short comings of general robot dynamics model, a network based on long short-term memory (LSTM) in deep recurrent neural network was proposed. The network compensates the general dynamics model of a self-developed six-degree-of-freedom collaborative robot based on the consideration of gravity, Coriolis force, inertial force, and friction force. In the experiment, the nondisassembly experimental measurement combined with least-squares method was used to identify the parameters. The motor current was used to evaluate the joint torque instead of mounting an expensive and inconvenient torque sensor. The excitation trajectory based on the Fourier series was optimized. The raw experimental data were used to train the proposed LSTM network. About the accuracy of the dynamic model and the compensation method for the collaborative robot, the root-mean-square error of the calculated torque relative to the actual measured torque was used to train the network and evaluate the proposed method. The analysis and the results of the experiment show that the compensated collaborative robot dynamics model based on LSTM network displays a good prediction on the actual torque, and the root-mean-square error between predicted and actual torques is reduced from 61.8% to 78.9% compared to the traditional model, the effectiveness of the proposed error compensation policy is verified.
Abstract: With the gradual transition of coal mining to deep mining, the number and intensity of rock burst events in the deep mining process are gradually increasing. Thus, it is of great significance to study the change of rock burst precursor signal for the prediction of rock burst. Microseismic signal monitoring plays an important role in rock burst prediction. The microseismic energy level changes with time, a good corresponding relationship exists between the high-energy microseismic events and rock burst. To advance the time node of rock burst prediction and provide more time guarantee for rock burst prevention and control, a time series prediction model of mine microseismic energy based on the one-dimensional convolutional neural network (CNN) was established to predict the temporal variation of mine microseismic energy. Through model training, the energy level of the previous 10 microseismic events can be used as input to predict the energy level of the next microseismic event. Due to the imbalance of the microseismic sample data, the microseismic events of the 106-energy level were all judged as 105-energy level microseismic events in the model test. To improve the prediction accuracy of the model for the 106-energy level microseismic events, a hybrid sampling method was used to train the improved model. Using the microseismic energy level data of 250202 working face in Yanbei coal mine, the overall test accuracy of the improved model reaches 98.4% and the test accuracy of the 106-energy level microseismic events increased to 99%. The improved prediction model of the microseismic energy level time series based on the one-dimensional convolution neural network was applied to 250202 working face of Yanbei coal mine to predict the microseismic energy level time series. The overall prediction accuracy of the model is 93.5%, and the prediction accuracy of high-energy microseismic events is close to 100%.
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
