Abstract: Hemihydrate phosphogypsum (HPG), as a cementing material for mine filling, will spontaneously transform into phosphogypsum (PG) in the stockpiling state. The gelling activity decreases, and meeting the requirements of mechanical properties required for long-distance mine filling becomes difficult. The key measure in expanding the industrial application radius of HPG as a filling cementitious material is the prevention of the spontaneous conversion of HPG to PG. In-depth research on the conversion process of HPG in the storage state is required to achieve a breakthrough in the HPG resource utilization technology. In the storage process, the HPG chemical reaction will release the heat of hydration, causing the temperature and chemical fields in the system to interact with each other and promote the conversion of HPG to PG. Therefore, the HPG hydration heat release process is accurately calculated, analyzed, and simulated. This is a prerequisite to effectively inhibit the conversion of HPG. This article seeks a model of the heat release of the HPG hydration reaction during the storage process to understand the change of its gelation performance and guide on-site industrial applications. The monitoring of the free water mass fraction and the temperature of HPG stacks with initial temperatures of 35 °C, 40 °C, 60 °C, and 80 °C reveals that the HPG free water mass fraction change law conforms to the first-order reaction kinetic model. Based on thermodynamics and chemical reaction kinetics, a thermal kinetic model of the HPG hydration reaction on the relationship between the storage temperature and time is proposed. Using the COMSOL Multiphysics numerical simulation software, the HPG hydration reaction thermokinetic equation was then embedded in the heat transfer and ODE modules, and the HPG reactor temperature was numerically simulated. The simulated reactor temperature curve was more consistent with experimental results, and the reliability of the proposed model was verified. This model can provide guidance for the later design of the delaying HPG conversion plan and has very important practical significance for the promotion and application of HPG.
Abstract: A ball mill is important grinding equipment in a concentrator, and the accurate detection of the load status ensures that the ball mill runs in the best state, which helps optimize the grinding process, ensure the stable operation of the ball mill equipment, and save energy. The current mainstream detection methods cannot easily detect the movement inside the ball mill. Mill load requires a more efficient and direct detection method. In this study, the SM ?500 mm×500 mm ball mill was taken as the research object. Through theoretical analysis and simulation, intelligent grinding media with an embedded triaxial acceleration sensor and physical properties similar to that of ordinary steel ball media were designed to identify the mill load, and grinding experiments with different filling rates and other grinding conditions were conducted. Results revealed that the filling rate and the material to ball ratio are the important factors affecting the ?0.074 mm size products. Taking the grinding effect coefficient as an index to distinguish different load states and grinding effects, the best load state can be achieved under the conditions of 40% filling rate, 1∶37 material to ball ratio, and ~6 kg sample weight. The ball mill load was evaluated using the convolutional neural network (CNN) method and optimized support vector machine (SVM) models from the acceleration signal obtained by the intelligent grinding media. For the optimized SVM models, preprocessing of the acquired one-dimensional acceleration signal, including complementary ensemble empirical mode decomposition algorithm denoising, time-domain eigenvalue extraction, and sample entropy, was conducted. The feature vectors of mill load were included in the genetic algorithm and SVM (GA?SVM), grid search and SVM (GS?SVM), and partial swarm optimization and SVM (PSO?SVM) classification models for training. The research results revealed that the recognition accuracy of the PSO?SVM algorithm reaches 98.33%, but the training process tends to be tedious and time-consuming. For the CNN algorithm with excellent applicability in the field of image recognition, the detected acceleration signal data were converted into two-dimensional pictures and directly inputted into the CNN model based on the VGG19 network for classification and recognition. The classification and recognition accuracy of the mill load of the CNN method (i.e., 98.89%) was higher than that of the optimized SVM algorithm. Moreover, the calculation time of the CNN method was shorter than that of the optimized SVM algorithm. The ball mill load status identification method using the intelligent grinding media and CNN method could provide critical solutions and technical support for load detection and online evaluation.
Abstract: With the gradual development of coal mining to deeper levels, the in-situ stress of coal seams shows an increasing trend, resulting in a gradual decrease in permeability, and the stress state of the coal and rock mass and the properties of the surrounding rock also change. The mechanical properties and mechanical parameters of coal and rock mass greatly differ between depths, which influences the cracking and permeability enhancement effect of coal seam deep-hole cumulative blasting. Aiming at the problem of the increasing permeability of coal seams by deep-hole cumulative blasting under in-situ stress, on the basis of an analysis of the stress field of the surrounding rock and the stress of the blasting crack surface, the process of cumulative blasting and crack development characteristics under different confining pressures were numerically simulated. Through field tests of cumulative blasting under different buried depths, the influence of in-situ stress on the cracking and permeability enhancement effect of coal seam deep-hole cumulative blasting was discussed. The results show that the role of in-situ stress differs greatly between the stages of radial crack expansion of coal seam deep-hole cumulative blasting. Before blasting, the stress state and deformation characteristics of the borehole surrounding rock are determined by borehole shape and in-situ stress. In the initial stage of cumulative blasting, the impact of cumulative blasting on the surrounding rock is obviously stronger than in-situ stress. Therefore, the expansion direction of blasting cracks in the initial stage is mainly determined by the cumulative structure, and directional cracks are formed along the opening direction of the cumulative charge groove. With the crack extension, the blasting effect is gradually weakened, and the in-situ stress is dominant. The surrounding rock of the borehole produces tangential compressive stress under the in-situ stress, which limits the radial crack expansion of blasting. Meanwhile, the coal cracks that are not collinear with the principal stress gradually deflect toward the direction of the maximum principal stress under the action of strong shear stress. When the equivalent dynamic stress produced by blasting cannot continue to compress the coal, the elastic strain energy accumulated in the surrounding rock of the borehole begins to release toward the blasting center, causing the coal to crack and produce new cracks. In addition, the crack expansion range in different directions is controlled by the lateral pressure coefficient. When the vertical principal stress is constant, the crack range toward minimum principal stress further decreases with increasing lateral pressure coefficient.
Abstract: Improving the wettability of coal dust is commonly used for dust control in coal mines. The wettability of coal dust can be affected by various factors. This study aims to explore the quantitative relationship between coal dust molecular structure parameters and wettability at a micro level. We selected three samples with different coal ranks, which were labeled as the Shangwan nonstick coal (BN), Zhaolou gas-fast coal (QF), and Yangquan anthracite coal (WY) respectively, and were crushed to coal dust with a particle size of less than 200 mesh (74 μm). Three different types of surfactants, i.e., alkylphenol polyoxyethylene ether (OP-10), sodium diethylhexyl sulfosuccinate (rapid penetrant T), and hexadecyl trimethyl ammonium-chloride (1631), were used for wetting coal dust. Carbon-13 nuclear magnetic resonance (13C-NMR) and infrared spectroscopy (FTIR) tests were conducted to obtain the microscopic molecular structure parameters of the coal samples. Then the relativity between the contact angle and 13C-NMR structural parameters/FTIR structural parameters were analyzed via the SPSS software to determine the principal factors. Finally, quantitative characterization equations describing the relationship between the wettability and molecular structure parameters of the studied samples were established through multiple linear regressions. Results revealed that under the action of different surfactants, the main factors affecting the wettability of coal dust are different. These factors mainly include quaternary carbon (${{f}}_{\text{al}}^{\text{}\text{H}}$), oxygen-connecting aliphatic carbon (${{f}}_{\text{a}}^{\text{}\text{P}}$), and aromatic bridge carbon (${{f}}_{\text{a}}^{\text{}\text{B}}$) in 13C?NMR experiments and the ester group (?COO?), ether group (?O?), and carbonyl group (C=O) in FTIR experiments. The quantitative characterization equations established in this study provide a micro insight to understanding the affecting mechanism of coal dust wettability, which could facilitate the selection of surfactants and improve the reduction efficiency of coal dust.
Abstract: The flue gas circulation technology is a new type of sintering mode developed based on the principle of the reintroduction of part of the hot exhaust gas into the sintering process. It has considerable effects on improving the utilization rate of the sintering waste heat and reducing pollutant emissions and sintering energy consumption. However, the circulating flue gas flow state in the flue gas hood and air leakage of the flue gas hood are critical to the effect and stability of the flue gas circulation. Simultaneously, they have a considerable impact on the quality indicators of the sintered ore. To optimize the circulating flue gas flow state in the flue gas hood, improve the air leakage of the flue gas hood, and maximize the advantages of low pollution and low emission of the flue gas circulation sintering technology, this study simulated the flue gas flow status in the circulating flue gas hood of a steel plant and the air leakage situation. Results show that although the strength of the smoke entering to form the vortex is weakened to a certain extent when the existing smoke hood manhole is opened, the swirling flow of the smoke is not improved, resulting in the uneven flow velocity of the smoke on the material surface. Moreover, evident air leakage of the flue gas hood is observed. By optimizing the structure and number of baffles in the flue gas hood, the rotating flow of the flue gas in the flue gas hood is weakened, the circulation of the flue gas flow considerably improved, and the flue gas distribution is more uniform. Simultaneously, the air leakage of the gas hood greatly improved. After optimization, the air leakage on the side changes from a leakage of 1.2 m3·s?1 to a suction of 2.4 m3·s?1, which is conducive to the smooth movement of the sintering production.
Abstract: Nitrogen oxides (NOx) are major air pollutants produced by fuel combustion that cause adverse effects on the environment and human health. The deep purification of NOx from ?ue gases has become a worldwide issue. In China, a rigorous ultra-low emission standard (ULES) of NOx≤ 50 mg·m–3 has been implemented in the power and steel industries in recent years. NO2 is a valuable chemical feedstock that is worthy of being recycled from flue gas. Adsorption is a promising technology that can achieve deep purification and resource recovery of NOx from industrial flue gas, in which a high-performance NOx adsorbent plays a key role. However, a systematic understanding of NOx adsorbents for practical applications is still lacking. This study compares and analyzes the NOx adsorption and desorption performances of typical practical adsorbents including zeolites, metal oxides, and silica-alumina gels based on the practical need for both NOx purification efficacy and material thermal stability. NOx adsorption capacities, breakthrough curves, uptake curves, and temperature-programmed desorption (TPD) curves were also measured. Results show that compared to Fe–Mn–Ce and 13X as competitive adsorbents, H-ZSM-5_25 showed NOx deep purification (purification efficiency close to 100% before adsorption breakthrough), great NOx adsorption capacity (0.206 mmol·g–1), and high NO2 concentration ratio in desorption, which are likely due to its high NO catalytic oxidation rate and comparable NO2 physical adsorption rate. Regarding the desorption characteristics, H-ZSM-5_25 showed a bimodal TPD desorption peak with a lower desorption temperature (400–470 K) in the low-temperature region. Meanwhile, NO2 is the primary NOx component in the desorbed gas (adsorption-desorption enrichment ratio of NO2 being up to 57), which can easily be recovered using the liquefaction method. Furthermore, by comparing the adsorption performances on the H-ZSM-5 with different silica-to-alumina ratios, the NOx adsorption was found to decrease (from 0.706 to 0.206 mmol·g–1 for H-ZSM-5_25 and from 0.454 to 0.127 mmol·g–1 for H-ZSM-5_38) with increasing temperature (298–398 K). The dependence of the NO2 adsorption on the temperature was more significant for H-ZSM-5_25 compared to H-ZSM-5_38. Compared to H-ZSM-5_38, H-ZSM-5_25 with a lower silica-to-alumina ratio (consequently, more cation sites) rendered greater NO oxidation performance, a potentially higher NO2 adsorption capacity, and a greater decreasing trend of the adsorption capacity with increasing temperature. Results of adsorption kinetic experiments showed that the NOx mass transfer parameters on H-ZSM-5_25 were lower than those on H-ZSM-5_38 with a smaller primary micro pore channel. Results of the current work can provide technical references for economic flue gas denitrification.
Abstract: Vanadium alloys are an attractive candidate material for advanced fusion reactors’ structural components. Some leading vanadium alloys, such as V?(4?5)Cr?(4?5)Ti alloy, exhibit several important advantages, including excellent strength at elevated temperatures, high resistance to neutron irradiation damage, inherently low long-term activation, as well as good fabricability and weldability. However, the corrosion and embrittlement via oxygen pickup during the high-temperature oxidation process of vanadium alloys remains a key issue, restricting their operation conditions and long service life. In a high-pressure oxygen environment, the main oxidation product V2O5, with a low melting point of ～680 ℃, is formed on the vanadium alloy surface, which cannot offer reliable protection to mitigate further oxidation over 650 ℃. However, despite being exposed to a very low-pressure oxygen environment, it is still unlikely for vanadium alloys to form an effective oxidation film to retard the oxygen absorption at temperatures over 450 ℃, mainly due to the high solubility of oxygen in vanadium. When the oxygen concentration reaches 0.2% in the matrix of V?4Cr?4Ti alloy, it can cause severe oxygen embrittlement, possibly due to oxygen accumulation and formation of fine oxidation precipitates at the grain boundaries and the adjacent matrix. Therefore, it is significantly important to enhance the high-temperature oxidation-resistant performance of the vanadium alloy to broaden the operation conditions. In this work, this research progress on the high-temperature oxidation resistance of vanadium alloys is systematically reviewed. In summary, three main methods for enhancing the oxidation–corrosion resistance of vanadium alloys at elevated temperatures are elaborated, i.e., oxidation-resistant element addition, diffusion coating, and overlay coating. Additionally, the characteristics and existing problems of these methods and the responding examples are also analyzed and discussed in detail. In the first two methods, it is impossible to completely isolate the alloy substrate from the service environment; thus, the typical oxidation product V2O5 is easily formed in the high-pressure oxygen environment, leading to severe oxidation corrosion and embrittlement, especially at elevated temperatures. Expectedly, the dense overlay coating presents a greater potential application mainly because of the thorough protection from the service environment. Finally, the development trend in the modification and technical requirements of the advanced overlay coatings on high-performance oxidation resistance are prospected in this paper as per the practical application demands for vanadium alloys, aiming to provide a beneficial reference for further research.
Abstract: In recent years, a new family of two-dimensional (2D) transition metal carbides and/or carbonitrides, labeled MXenes, has attracted immense attention from researchers. Due to unusual hydrophilicity, electrical conductivity, flexibility, and pseudocapacitance, MXenes have great potential application in energy storage, water desalination, catalysis, electromagnetic interference shielding, transparent conductive films, and so on. However, MXenes exhibit poor stability because of their structural defects, active transition metals, and termination groups. These greatly destroy the sheet structure and decrease their conductivity, thereby restricting their application fields. In this review, the structure and synthesis methods of MXenes are briefly introduced. Then, we focus on current research studies regarding the stability of MXenes. The mechanism of oxidation is also discussed. Ti vacancies and the edges are the preferential oxidation sites in MXene sheets. Based on this, the methods to improve the MXene stability, including controlling the storage environment, improving the synthesis method, annealing in an atmosphere, modification based on the surface electric state, and doping impurities, are further discussed. First, the optimal requirements for MXenes storage are low temperature, desiccation, and oxygen isolation. Second, soft etching methods must be applied to synthesize MXenes to reduce the defect density of their sheet surface. Then, annealing MXenes in an atmosphere can enable the tailoring of the surface structure and functional groups for enhanced MXene stability. Lastly, more methods have been applied to improve the stability of MXenes based on their surface electric state. Since the MXene sheet surface is electronegative, their oxidation can be impeded by loading cations into the sheets. Similarly, since the edge of these sheets is electropositive, polyanions can be absorbed onto the edge to protect the MXene sheets. Moreover, compositing metal oxides, organic macromolecules, and nanocarbon on their surface can also improve the stability of MXenes. Finally, doping with impurities can also improve the band energy of MXenes. Meanwhile, our idea to improve the stability of MXenes is also briefly introduced.
Abstract: To address the problem of high filling cost in an open pit to an underground mine, based on the machine learning method, the filling cementitious material needed for subsequent backfill mining method was developed using the available industrial wastes around the mine, and the ratio of filling slurry was optimized. First, the physical and chemical properties of the materials were analyzed. Unconfined compressive strength tests were conducted with different activator formulations to analyze the influence of each component on the strength of the backfill body. A genetic algorithm and support vector machine (GA?SVM) model was established to predict the steel-slag-based cementitious material formula using the experimental data, and the optimal ratio was determined based on the model prediction results. X-ray diffraction (XRD) and scanning electron microscope (SEM) were used to analyze the hydration products and microstructure characteristics of steel-slag-based cementitious materials at different curing ages and slag dosage conditions and determine the hydration mechanism of steel-slag-based cementitious materials. Finally, the slurry proportion was optimized by strength (i.e., 7 and 28 days) and working characteristics (i.e., slump and bleeding rate) based on the principle of gray target decision. Results revealed that the relative errors of the GA?SVM model for predicting the steel-slag-based cementitious materials strength at 7 and 28 days are 3.6%–12.62% and 6.9%–10.19%, respectively, thereby indicating high prediction accuracy. The optimal proportion of steel-slag-based cementitious materials determined by prediction analysis is steel slag content of 30%, desulfurized gypsum content of 4%, cement clinker content of 12%, and mirabilite content of 1%. The main hydration products of steel-slag-based cementitious materials are amorphous C?S?H gel, ettringite, tricalcium aluminate hydrate, Ca(OH)2, and CaCO3. The calcium hydroxide content increases with the steel slag content, which generates a large number of pores and deteriorates the structure and strength of the sample. When the new steel-slag-based cementitious material is applied to the actual backfilling of the mine, the optimal ratio parameters of filling slurry are obtained through the optimization of the model of the gray target decision (i.e., cement?sand ratio of 1∶4 and mass concentration of 72%). Corresponding verification experiments were conducted, and the corresponding strength and working characteristic parameters were 1.74 MPa, 3.61 MPa, 24.2 cm, and 5.91%, which all met the requirements of subsequent filling. With this proportion, the filling cost is 113 ￥·m?3, which is 38.92% lower than that of the cement filler. The research results will benefit the comprehensive utilization of solid waste and provide support for safe, clean, and efficient mining.
Abstract: In 2020, the municipal solid waste removal and transportation volume reached 235.117 million tons, of which 146.076 million tons were incinerated in China. Because it can reduce the harmfulness of waste and recycle energy, municipal solid waste incineration (MSWI) technology has become the primary method for the disposal treatment of urban domestic waste in China. However, this method produces MSWI fly ash, which is defined as a hazardous waste rich in dioxins and heavy metals. Calculated based on 5% (mass fraction) of the original waste, the output of MSWI fly ash in China nearly reached 7.304 million tons in 2020. Moreover, the stockpile management and treatment capacities are seriously out of balance. At present, the main disposal method of MSWI fly ash is landfilling, which consumes land resources and poses an environmental hazard. As a result, the harmlessness and recyclability of MSWI fly ash have become a bottleneck for green development. In this review, the harmless melting and recycling of MSWI fly ash are introduced in detail. The mechanisms of heavy metal solidification and dioxin degradation during MSWI fly ash melting have been explained. MSWI fly ash can be transformed into glass slag containing CaO?SiO2?Al2O3 after co-melting with other solid wastes rich in silicon aluminum oxide. Heavy metals in MSWI fly ash can be solidified at the atomic scale in the silicate network of glass. More importantly, as the temperature increases beyond 800 °C, dioxins undergo dechlorination and degradation, reducing the harmfulness and revealing the harmlessness of MSWI fly ash. This review also describes how to deal with the glass slag that forms because of co-melting. The glass slag has low added value and poor mechanical properties. Future disposal trends for vitrified slag from MSWI fly ash, including glass–ceramic, glass–ceramic foam, and cementitious materials, have been proposed. Given that vitrification can solidify heavy metals in the process of subsequent resource usage and product service, the migration and leaching characteristics of heavy metals need to be further investigated. This study provides a reference for the comprehensive usage of MSWI fly ash.
Abstract: The click-through rate (CTR) prediction task is to estimate the probability that a user will click an item according to the features of user, item, and contexts. At present, CTR prediction has become a common and indispensable task in the field of e-commerce. Higher accuracy of CTR prediction results conduces to present more accurate and personalized results for recommendation systems and search engines to increase users’ actual CTR of items and bring more economic benefits. More researchers used a deep neural network (DNN) to solve the CTR prediction problem under the background of big data technology in recent years. However, there are a few models that can process time series data and fully consider the context information of users’ history effectively and efficiently. CTR prediction models based on a DNN learn users’ interests from their history; however, most of the existing models regard user interest, ignoring the differences between the long-term and short-term interests. This paper proposes a CTR prediction model named Long- and Short-Term Interest Network (LSTIN) to fully use the context information and order information of user history records. This use will help improve the accuracy and training efficiency of the CTR prediction model. Based on the attention mechanism, the transformer and activation unit structure are used to model long-term and short-term user interests. The latter is processed using the recurrent and convolutional neural networks further. Eventually, a fully-connected neural network is applied for prediction. Different from DeepFM and Deep Interest Network (DIN) in experiments on an Amazon public dataset, LSTIN achieves modeling with context and order information of user history. The AUC of LSTIN is 85.831%, which is 1.154% higher than that of BaseModel and 0.476% higher than that of DIN. Besides, LSTIN achieves distinguishing the long-term and short-term interests of users, which improves the performance and maintains the training efficiency of the CTR prediction model.
Abstract: As an extension of the cloud computing paradigm, fog computing has attracted wide attention due to its advantages of low energy consumption, short time delay, and high bandwidth saving. Meanwhile, the fog computing-based computation offloading mechanism provides strong support for alleviating the pressure of data processing, realizing low delay service, and prolonging the network lifetime. To construct a green and long lifetime Internet of Things (IoT), this paper proposes a fairness and energy co-aware computation offloading scheme for fog-assisted IoT. Based on the joint optimization consideration of the fog node’s computing capacity, bandwidth resource, and offloading decision with energy consumption fairness, an optimization problem is first formulated to minimize the total energy consumption of all computation tasks. Second, a momentum gradient and coordinate collaboration descent-based fair energy minimization algorithm are proposed to solve the above mixed integer nonlinear programming problem. In this algorithm, based on the historical average energy consumption, distance, computing capacity, and residual energy of the fog node, a fair index is designed to obtain the offloading decision with the optimal energy consumption fairness. Minimization of the total energy consumption for processing all tasks can be achieved by jointly optimizing the occupation ratios of computing and bandwidth resources with the developed momentum gradient and coordinate collaboration descent method. Finally, simulation results show that the proposed scheme can achieve a faster convergence speed. Meanwhile, the total energy consumption of this scheme is the lowest compared to the random selection and greedy task offloading (GTO) schemes, the energy consumption fairness of the fog node is the highest, and the network lifetime is enhanced by 23.6% and 31.2% on average, respectively. Furthermore, this scheme can still maintain its performance advantage under different numbers of fog nodes and different task sizes, indicating the high robustness of the proposed scheme.
Abstract: Traditional cloud computing is developed from a high-performance cluster. Every server in the high-performance cluster has its own resources, including a CPU, memory, a network, I/O (Input/Output), a power system, and a heat dissipation system. Using software virtualization technologies such as the kernel-based virtual machine (KVM), Xen, VMware, and Hyper-V, these exclusive resources can be shared among these servers to improve the utilization rate. Although these technologies provide a great improvement in the resource utilization rate, some overhead in the process of software virtualization is inevitable. Server architecture and virtualization technology are the two factors that mainly affect cloud computing efficiency. With the rapid development of internet services, big data, and cloud computing, the cloud server has become mainstream instead of the traditional server. On the other hand, hardware virtualization technology has gradually developed. Compared with the traditional cloud computing solutions based on virtual machines, the cloud server based on hardware virtualization can achieve much higher efficiency to better meet cloud computing requirements by removing the software overhead. The cloud server’s design concept of configuration on demand, distributed sharing of hardware resource architecture, and construction method of hardware resource virtualization are presented. A three-level interconnection architecture of the cloud server is designed. In Level-1, the computing pool and the memory pool are built, while Level-2 is for the network pool, and Level-3 is for all resource pools. Different applications in these levels can be realized in the cloud server: Level-1 for computing-intensive applications, Level-2 for transactional applications, and Level-3 for virtual applications. A prototype system of a 16-processor cloud server using hardware virtualization architecture is designed and implemented. In this system, there are sixteen physical nodes. Every physical node is composed of a CPU and two DIMMs (dual inline memory modules). Different types of CPUs may be used in these physical nodes. Every four physical nodes form a computing module. In every computing module, a field-programmable gate array (FPGA)-based interconnection fabric controller (IFC) integrated network, storage, and general I/O resources is designed to interconnect these processors. All IFCs are interlinked. All the processors in this prototype system can share the network, storage, and general I/O resources to realize hardware resource virtualization through these IFCs. For the prototyping system, evaluation experiments on network performance tests by the Netperf program and storage performance tests by the FIO program are performed. The test results show that the prototyping system not only keeps the traditional cloud server’s advantages but also provides better scalability and performance. The advantages of this cloud server are in providing a high-density, high performance-to-cost ratio, a high performance-to-Watt ratio, and high scalability compared with the existing traditional cloud server.
Abstract: The endurance time method (ETM) is a novel dynamic analysis method in which artificially intensified accelerograms characterized by the increase in seismic intensity with time are used as loading inputs. In this method, various dynamic responses, i.e., ranging from elastic to failure, under seismic excitations of different intensity levels are estimated with a reduced dynamic calculation effort. Based on these merits, this study investigated the accuracy and effectiveness of ETM in predicting the seismic responses and damage to continuous rigid-frame bridges considering the real internal force state (called element initial strain state) of the completed bridge. In detail, first, a typical irregular continuous rigid-frame bridge was selected as the target of the analysis, and its finite element model considering the real construction process was established by MIDAS/Civil. Then, the real internal force state considering the 10-year concrete shrinkage and creep was determined through construction phase analysis, and a dynamic analysis model considering the real internal force state was built via OpenSees utilizing the equivalent load method. Subsequently, the incremental dynamic analysis results under natural ground motions were obtained and compared with the results of the ETM, and the applicability of the ETM to obtain seismic responses rapidly and accurately was verified. Finally, the seismic responses of pier displacement, girder displacement, and pounding force were analyzed using the ETM, and the damage to piers was evaluated using the displacement ductility factor and Park–Ang damage index. The results indicate that ETM can predict the time when a continuous rigid-frame bridge reaches a certain damage status under the real internal force state of the completed bridge. Moreover, the damage to the main-bridge pier is smaller than that of the approach-bridge pier when the endurance time is short. However, when the endurance time is long, the opposite is true.
Abstract: In the marine environment far away from the mainland, the coral sand foundation can be improved by injecting the cement grout with a very small amount of graphene oxide (GO) through grouting or mixing piles and other processes, which can greatly increase the stone body’s ability to block chloride ion penetration. Based on the comparative analysis of the difference in the particle morphology of river sand and coral sand and changes in hydration products and microstructure before and after GO incorporation, this study employed a rapid chloride ion migration test, scanning electron microscope experiment, and Image-Pro Plus image processing to reveal the mechanism of the modified coral sand cement stone body blocking permeation by chloride. The result reveals that high particle angles, irregular shapes, and porous and internal pores are the main reasons for the lower coral sand cement stone body than the river sand cement stone body in blocking chloride ion permeability under the same process conditions. After mixing 0.02% (mass fraction) GO, 28 d and 56 d coral sand cement stones have the highest degree of improvement in blocking chloride ion permeability (39.43% and 48.93%) and are similar to those of ordinary river sand cement stones without GO addition under the same process conditions. The coral sand cement stone body’s antichloride ion penetration performance improvement is related to the amount of GO. The two are first positively correlated and then negatively correlated. 0.02% is the best mix-up measure after the experiment in the assay. Regulating cement hydration products to form a regular and orderly hydrated crystal shape, improving the morphology of the interface transition zone, filling the space of internal cracks, and repairing the morphological characteristics of the pores are the main reasons that allow the incorporation of GO to affect the resistance of coral sand cement stones to chloride ion permeability.
Abstract: With the rapid development of microscale/nanoscale manufacturing technology, electronic microchips, microreactors, and microscale fuel cells have attracted considerable attention. The practical applications of miniaturized devices require not only advanced fabrication procedures and materials but also efficient thermal management to maintain their performance. For electronic microchips with high integration and frequency, high heat flux not only significantly limits their performance but also considerably affects their lifetime and reliability. Given that conventional air cooling and single-phase liquid convection cooling methods cannot meet the heat dissipation requirements, microchannel heat transfer technology has become an important alternative to solve the heat transfer problem of miniaturized devices. However, conventional microchannel heat transfer methods usually face two major challenges, namely, microscale dimensions that result in high-pressure drop and high-pump power consumption and temperature increase along the microchannels that considerably affect stability and reliability. The resulting high flow resistance and temperature nonuniformity significantly limit the practical applications of microchannel heat sinks. In recent years, inspired by natural fractals, such as mountain ranges, rivers, leaf venations, plant roots, tree trunks, blood vessels, and lung bronchus, researchers have developed a series of new types of fractal microchannels for heat transfer process intensification. This review provides a comprehensive overview of state-of-the-art research on fractal microchannel heat sinks, such as Y-shaped, H-shaped, T-shaped, Ψ-shaped, Cantor, and Koch fractals. We highlight the principles of heat transfer fractal microchannels, discuss the theoretical and experimental research findings, and identify the current problems and future research directions. Although research on fractal heat sinks has already gained considerable progress, the following challenges should be carefully considered: most studies focus on numerical simulations; meanwhile, experimental studies are relatively limited because of the difficulties in device fabrication. Compared with Y-shaped fractals, the other types of fractal microchannels exhibited a better performance but have received significantly less attention. Both multilayer and hydrogel-assisted fractal microchannels have typically high heat transfer capacity; however, their fabrication process is complicated. There are still a few contradictory results concerning the impact of fractal structures on heat transfer enhancement that need in-depth theoretical modeling and experimental observations. This review can not only provide an in-depth understanding of fractal microchannels but also shed new light on the development of robust fractal heat sinks for intensifying heat transfer applications.
Abstract: Lime is an important industrial raw material widely used in iron- and steel-making, flue gas desulfurization, construction, and papermaking industries. Lime is generally obtained via calcining limestone in a kiln, i.e., limestone is heated and decomposed to generate lime and carbon dioxide (CO2). In the conventional lime calcination, the CO2 released by the limestone decomposition is mixed with the flue gas because the fuel is burned in the shaft kiln, requiring gas separation for CO2 capture. The new lime calcination process using CO2 as a circulating carrier gas to heat limestone particles can avoid the above mixing problem, thereby directly capturing the CO2 generated by limestone decomposition, which is expected to reduce carbon emissions from lime production by approximately 70%. However, the new calcination process based on CO2 heating is quite different from the conventional calcination process. To understand the new calcination process and accurately design and optimize it, a mathematical model of the lime calcination process based on CO2 heating was established. Based on the model, a shaft kiln with a capacity of 200 t·d?1 was simulated and calculated. In addition, profiles of key parameters such as the gas-solid temperature difference, gas flow rate, gas temperature, particle surface temperature, reacting interface temperature, and conversion ratio in the shaft kiln were obtained. Besides, the three operating parameters (feed gas temperature, feed gas flow rate, and radius of the feeding limestone particle) on the calcination were analyzed. The following observations were made: (1) the lower the feed gas temperature, the lower are the final conversion ratio, pinch temperature difference, and tail gas temperature of the kiln. In addition, the changing trend of the final conversion ratio and pinch temperature difference conforms to a quadratic polynomial law, and the changing trend of the tail gas temperature conforms to a linear law. (2) The lower the feed gas flow rate, the lower are the final conversion ratio, pinch temperature difference, and tail gas temperature of the kiln. Moreover, the changing trend of each parameter conforms to a quadratic polynomial law. (3) Finally, the larger the radius of the feeding limestone particle, the lower is the final conversion ratio of the kiln, the higher is the tail gas temperature, and the greater is the pinch temperature difference. The changing trends of various parameters conform to cubic polynomial laws. Compared with the feed gas temperature and the feed gas flow rate, the radius of the feeding limestone particle has a greater impact on the pinch temperature difference and the tail gas temperature when the final conversion ratio changes in the same range.
Abstract: As environmental problems become increasingly severe, achieving qualitative breakthroughs in the energy consumption and emissions of traditional internal combustion engine vehicles is difficult. In contrast, new energy vehicles are environmentally friendly and have low fuel consumption, which is important for the future development of vehicles. A plug-in hybrid electric vehicle (PHEV) is widely regarded as the most promising alternative solution for improving energy efficiency and reducing emissions. The optimization of the energy management strategy (EMS) mainly focuses on reducing fuel consumption and improving the economy. However, the durability of the power battery also needs attention, as the lack of life remains a major obstacle to the large-scale commercialization of PHEVs. Because PHEV batteries can obtain relatively cheap power through the grid, the traditional control strategy only considers the full use of the battery power but ignores its excessive use, which will accelerate the decline of the power battery capacity. Therefore, determining how to make full use of the battery power and control the decline of the battery capacity is a new research focus. Based on the semiempirical decay model of the battery, the energy management strategy of balancing the degradation of the battery capacity was established by introducing the battery utilization degree factor. The multiobjective optimization problem was transformed into a single-objective problem by selecting the appropriate weight factor through the Pareto noninferior target domain. A dynamic programming algorithm was used to obtain the global optimal solution of the weight coefficient. The optimal weight coefficient was selected by weighing the fuel consumption and battery capacity decline degree under different weights. In the case of equivalent fuel consumption, the decay rate of battery life can be effectively inhibited when the weight coefficient is 0.9. Finally, the validity of the proposed solution is verified by comparing the online equivalent consumption minimization strategy (ECMS) simulation with the dynamic programming solution under the same weight.
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
