Gasoline molecular blending technology on?line requires rapid access to detailed molecular composition information of various types of component oils. In this paper, an autoencoder?based method for the rapid resolution of gasoline molecular composition is developed, which can directly predict the detailed monomeric hydrocarbon composition of gasoline from near?infrared spectra. The constructed autoencoder model of gasoline molecular composition can explore the potential features and recover the original molecular composition by decoding the potential features. The artificial neural network algorithm is used to correlate the NIR spectral information with the potential features of gasoline composition. The accuracy of the model is verified by using hydrogenated gasoline with the average absolute error is 0.033. The model developed in this work applies the current popular autoencoder algorithm to the traditional petrochemical process, which is an important guideline for blending online and real?time optimization of gasoline molecules.
The direct method was used to prepare polyurea lubricating grease, and the effects of several different types of additives on the extreme pressure and anti?wear performance for polyurea lubricating grease were investigated and studied. The results demonstrate that several types of additives have good compatibility with polyurea grease, and they have little undesirable influence on colloidal stability of polyurea grease, and they can also enhance thermal stability of polyurea grease. Moreover, the addition of additives can significantly improve the performance of extreme pressure and wear resistance for polyurea lubricating grease. And in the experiments, it was found that the composite multifunctional additive containing sulfur and phosphorus has a relatively better effect on improving the extreme pressure and wear resistance of polyurea lubricating grease. The research results can provide a reference basis for the improvement of extreme pressure and anti?wear property for polyurea lubricating grease, achieving a relatively optimized comprehensive performance, thereby enhancing its practical utilization performance under the harsh working conditions for high load and high speed mechanical equipment.
Aerobic oxidative desulfurization is a safe and environmentally friendly desulfurization method, but oxygen usually needs to be activated under harsh conditions, the synthesis of efficient catalyst is an effective way to improve desulfurization activity. In this work, flower?like cobalt molybdate with large specific surface area was synthesized by one?step hydrothermal method using cobalt chloride and ammonium molybdate as raw materials. In addition, urea is used as precipitation agent and structure control agent. The morphology and structure of the CoMoO4 were characterized by FT?IR, XRD, SEM, XPS and N2 adsorption desorption techniques. Dibenzothiophene in simulated oil was removed using CoMoO4 as catalyst and molecular oxygen as oxidant. The effects of reaction temperature, the flow of oxygen, the amount of CoMoO4 and the type of sulfur compounds on the desulfurization rate were investigated. In addition, the recycling performance of flower?like CoMoO4 was studied. The experiment shows that the desulfurization rate can reach 98.2% under the optimal reaction conditions. The catalyst can be reused for 5 times without significant reduction in oxidative desulfurization activity. The formation of superoxide radical in the oxidation desulfurization process resulting in high desulfurization activity.
As an electrochemical inert cation, Mg2+ has an ionic radius (0.072 nm) similar to that of Li+ (0.076 nm), which is widely used to replace Li+ in Li?rich layered oxides (LLOs) materials. However, the influence of Mg2+ on the crystal structure of LLOs materials is still controversial. In this work, the Mg?doped Li?rich cathode materials Li1.2-x Mg x Mn0.54Ni0.13Co0.13O2 were synthesized by a sol?gel and high?temperature calcination method. The crystal structure, and valence state of elements in synthesized materials were systematically studied via X?ray diffraction, and X?ray photoelectron spectroscopy. These results indicated that Mg2+ doping can increase the cell parameters of LLOs materials. At the same time, compared with Li1.2Mn0.54Ni0.13Co0.13O2, Mg?doping can effectively improve the electrochemical performance of LLOs materials. After optimization, the Mg?0.03 sample exhibits anomalous electrochemical performance, that is, the initial discharge?specific capacity is 291.9 mA?h/g and the initial coulomb efficiency is 78.40%.
A new type of Ruddlesden?Popper cobalt?rich layer perovskite oxide La1.5Ca0.5Ni0.2Co0.8O4+δ (LCNC) was synthesized by a sol?gel process. The results show that the conductivity of LCNC in air at 400 ℃ to 800 ℃ is 4~58 S/cm, which is better than that of most reported SOFC cathode materials. The polarization impedance of symmetrical battery LCNC|LSGM|LCNC is 0.16 Ω·cm2 at 800 ℃. The maximum power density of the single cell supported by 300 μm thick La0.9Sr0.1Ga0.8Mg0.2O3-δ (LSGM) with the LCNC cathode was 527 mW/cm2, and the performance of the single cell decreases slightly after working continuously for 50 hours. The experimental results show that LCNC is a potential SOFC cathode material.
In order to understand the mechanism of CO2 dissolved buried mechanism in high temperature and high pressure porous environment,physical simulation experiment was conducted to study CO2 dissolved buried mechanism,mineralization buried mechanism and free buried mechanism in porous media by means of indoor physical model experiment.The results show that the solubility of CO2 in formation water is mainly affected by temperature, pressure and salinity of formation water. CO2 dissolved in formation water will mineralize with minerals in rocks, and the mineral content of rocks will change significantly before and after the reaction. The long core displacement experiment characterized the amount of free CO2 storage and the oil displacement effect. The experiment reveal that CO2 flooding in porous media has dual effects of burying and enhancing oil recovery.
In order to study the influence of cross?fracture on the mechanical properties and damage modes of deep shale, and better understand the damage evolution law of shale containing cross?fracture under high temperature and high pressure coupling, 15 groups of shale models containing cross?fracture with different inclination angles were established, and simulation experiments were carried out to study the stress?strain relationship, damage evolution and acoustic emission characteristics of the shale specimens. The results show that the compressive strength and modulus of elasticity of shale are negatively correlated with the inclination angle of the main cracks, and show an upward concave trend with the increase of the inclination angle of the secondary cracks, and the compressive strength of shale decreases significantly when there are cracks perpendicular to or close to perpendicular to the loading direction; the damage modes of shale specimens under the influence of cross?cracks are mainly divided into "X"?shaped damage, diagonal "N"?shaped damage, diagonal "W"?shaped damage, inverted "V"?shaped damage, damage along the main crack, "V"?shaped damage and "λ"?shaped damage; the fractal dimension of shale specimens is negatively correlated with the inclination angle of the main crack, and as the inclination angle of the main crack decreases, the value of fractal dimension tends to increase, and the corresponding damage pattern of the specimens is more complicated and the internal damage is more intense.
The Cu plate was fabricated by a novel grinding tool via multiple rotational grinding under deep cooling condition. The surface morphology, microstructures and properties of the grinding zone were analyzed via super depth of field microscope, optical microscope, friction and wear test, respectively. The results show that well surface formation and ultrafine grains were obtained after multiple rotational grinding under deep cooling condition. Combined with the temperature distribution simulation, the main grain refinement mechanism of 50 r/min is the breaking and split of the grain. The grain refinement mechanism at 500 r/min is dynamic recrystallization and forced cooling to inhibit the growth of recrystallized grains. Microhardness tests and wear tests showed that the ultrafine?grained zone prepared by multiple rotational grinding under deep cooling condition could improve the wear resistance of the materials.
Copper nickel alloys have excellent corrosion resistance and mechanical properties, therefore it is widely used in marine engineering. However, due to its complex service environment, copper nickel alloys are prone to corrosion and leakage, resulting in irreversible losses. A corrosion?resistant conversion film was prepared on the surface of copper nickel alloy to improve its corrosion resistance performance; Electrochemical impedance, Tafel polarization curve, scanning electron microscopy, and energy spectrum analysis were performed, and the surface covered corrosion products of the conversion film were observed by X?ray photoelectron spectroscopy (XPS). The results showed that when the B10 copper nickel alloy sample was immersed in a solution containing molybdate for 120 minutes, the solution transfer resistance (Rct) and membrane resistance (Rp) reached their peak values, and the corrosion inhibition rate was as high as 92.5%; the conversion film contains copper and nickel oxides as well as hydroxides, while molybdenum exists in the form of +4 and +6 valence oxides.
The data of chemical processes often contains dynamic timing characteristics, and traditional fault detection has low usage of dynamic information, which limits the fault diagnosis performance. To address this problem,a new method of chemical process fault diagnosis based on an attention?enhanced encoder?decoder network model (AEN) was proposed. The coding part uses the LSTM to extract the feature information of the process data and combine it with the attention mechanism to utilize the dynamic information among the process data more effectively； the decoding part uses the LSTM and combines the context vector provided by the attention mechanism to provide more accurate state information for the softmax regression, and finally, the softmax regression is used to obtain the probability value of the fault category for each sample data. The introduction of the attention mechanism improves the efficiency of the model in using process dynamic information in the time domain. The proposed method is experimented with using Tennessee Eastman process data and compared with the results of standard PCA?SVM, DBN and ResNet, and the results show that the proposed method is more effective in diagnosing faults.
Magnetic tomography method has been widely used for nondestructive external inspection of buried and submarine pipelines, which is based on the principle of metal magnetic memory to discern the danger level and location of the stress concentration zone by measuring the anomalies in the spatial magnetic field distribution outside the pipeline. The distribution characteristics and spatial propagation law of pipeline inspection signal detected by magnetic tomography method, the energy distribution and change law of spatial magnetic memory signal in the stress concentration zone of magnetized pipelines are studied in this paper. The magnetic dipole field is used to establish the magnetic field model in the stress concentration zone of the inner wall of the pipeline, and the magnetic energy and energy density of spatial magnetic memory signals under different lift?off values outside the pipeline are finite element calculated based on the magnetic energy theory to derive the distribution law of spatial magnetic field and the correlation of magnetic energy density of magnetic signals under different lift?off value is analyzed. The results show that the spatial magnetic field energy outside the pipe decays with the increase of lift?off value, and the decay is the fastest within the distance of 50 mm from the outer wall of the pipe to the physical force; the correlation of magnetic energy density of different lift?off values shows that the magnetic signal detected by magnetic tomography method outside the pipe is homologous with the signal in the stress concentration zone of the inner wall of the pipe. Theoretically, it explains the effectiveness of magnetic tomography method and also provides evaluation indexes for extracting effective signals from the detection data.
In view of the irregularity of the bottom floor of working face and the diversity of the shape of the flying gangue in steeply dipping coal seam, based on the geographic information system data such as contour line of bottom floor of working face, the 3d grid model of bottom floor is established, combined with the energy tracking method(ETM) C + + programs, four typical shapes of flying gangue with the same mass and different shapes are simulated to obtain the motion trajectories of the migration of flying gangue in the actual working face, as well as the velocity, angular velocity and energy change curves at any time. The influence of the shapes on the motion of flying gangue is analyzed. In order to verify the accuracy and feasibility of the method in this paper, the trajectory simulated by Rockyfor3D software is compared. The results show that the transport capacity of ellipsoidal flying gangue is much higher than that of polyhedral flying gangue. Compared with common polyhedral flying gangue, the regular polyhedral flying gangue has farther migration distance and less energy loss due to collision. The number of edges of flying gangue of regular polyhedron is inversely proportional to the energy loss of flying gangue in collision, which indicates that flying gangue of regular polyhedron with multiple edges is most likely to cause danger．
Recognition of weather phenomena based on images is essential for the analysis of weather conditions. To address the problems that traditional machine learning methods are difficult to accurately extract various weather features and poor in classifying weather phenomena and the accuracy of deep learning for weather phenomena recognition is not high, a weather recognition model based on image block and multi?headed attention mechanism is proposed. The model introduces Swin Transformer into the field of weather recognition for the first time, and adopts a multi?headed attention mechanism combining window multi?head self?attention layer and shifted?window multi?head self?attention layer, whose regionally relevant features extraction capability makes up for the shortcomings of traditional methods and can extract complex weather features from images. The model is trained using transfer learning, and the fully connected parameters of the fine?tuned model are input to the Softmax classifier to achieve recognition of multi?category weather images with 99.20% recognition accuracy, which is better than several mainstream methods in comparison, and it can be applied to ground weather recognition systems as a weather recognition module.
The robustness of the particle swarm system is great, which is very helpful for solving ill?conditioned problems such as image reconstruction. However, the large number of pixels in the reconstructed image leads to a large dimension of particle and it is difficult for the particle to achieve the optimal solution in the optimization process. In order to solve this problem, a constraint is added to the particle position, imaging by Tikhonov regularization algorithm is used as the reference of particle position. The search for particles is constrained to the range of Tikhonov regularization algorithm reconstructs the image. Using the penalty function to solve the constraint problem to improve the particle search speed. Linearly decreasing weights as inertial weights for particle swarms optimization to realize the adaptive dynamic adjustment of the inertia weight and improve the flexibility of the algorithm; the chaotic operator is added to the position search process of the particle swarm optimization, when the particle falls into the local optimum, the chaotic variable will fluctuate within a certain range, reducing the missed rate of the optimal solution. The simulation results show that The improved particle swarm algorithm is more accurate and efficient than the traditional LBP algorithm and Tikhonov regularization algorithm.
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