Search

The gas–liquid–solid mini fluidized bed (GLSMFB) combines the advantages of fluidized bed and micro-reactor, and meets the requirements for safety and efficiency of green development of process industry. However, there are few studies on its flow performance and no studies on its mass and heat transfer performance. In this paper, the characteristics of gas–liquid mass transfer in a GLSMFB were studied in order to provide basic guidance for the study of GLSMFB reaction performance and application. Using CO2 absorption by NaOH as the model process, the gas–liquid mass transfer performance of GLSMFB was investigated. The results show that the liquid volumetric mass transfer coefficient and the gas–liquid interfacial area both increase with the increase of the superficial gas velocity within the experimental parameter range under the same given superficial liquid velocity. At the same ratio of superficial gas to liquid velocity, the liquid volumetric mass transfer coefficient increases with the increase of the superficial liquid velocity. Fluidized solid particles strengthen the liquid mass transfer process, and the liquid volumetric mass transfer coefficient is about 13% higher than that of gas–liquid mini bubble column.

The solid holdup, in a 150 mm-ID × 2460 mm-height gas–liquid–solid expanded bed with air, water and glass beads (the diameter of particles is 0.6–0.8 mm) was firstly investigated based on the immersion-type online multiphase measuring instrument, and bubble behavior was studied via the BVW-2 double electrical conductivity probe. The effect of the superficial gas velocity and liquid velocity on the expanded ratio, the transition ratio, the bubble rising velocity, the gas holdup and the solid holdup was studied. It is discovered that compared with the gas velocity, the liquid velocity has stronger impact on the expanded ratio, but it is opposite for the transition ratio. The average gas holdup and solid holdup increase linearly as the superficial gas velocity goes up. Among it, the gas holdup increases greater in the center, while the solid holdup increases greater near the wall. Compared with it, when the superficial liquid velocity rises, the average gas holdup hardly changes, but the average solid holdup keeps decreasing, especially the solid holdup distributes flatter with the increase of the superficial liquid velocity.

In a fluidized bed evaporator, heat transfer between walls of heated tubes and solutions belongs to a vapor-liquid-solid flow boiling, and solid particles are fluidized by a vapor-liquid flow. In t...

Blind single-channel source separation is a long-standing machine learning and signal processing problem. Traditional blind source separation (BSS) algorithms were proposed to solve this task utilizing multiple signal constraints. Generative adversarial network (GAN) are free from statistical constraints and samples, but the role of adversarial training in the task of BSS has not been fully demonstrated. Therefore, a new separation network model that enables to learn the known separated signal distribution from the stepwise fine estimation of the unknown mixture distribution was presented in this paper, and a self-attention mechanism was introduced to solve the problem of the blurring details of the generated image by the generator which preserves image details in the process of image separation. Compared with the existing single-channel blind source separation algorithm based on generative adversarial network-neural egg separation (NES), the detailed information of this new separation algorithm is more prominent, and the source signal in the mixed image has been separated more effectively, and has better separation performance than the classic blind source separation algorithms.

Mixing signal separation is an important field of image processing. However, traditional blind source separation (BSS) algorithms were proposed to solve this task utilizing multiple signal constraints such as independent, non-Gaussian, low rank, sparsity, temporal continuity etc. What’s more, as a case of ill-conditioned signal mixing, the single-channel blind source separation (SCBSS) is more difficult. Because neural networks have strong adaptability and self-organization capability, neural network methods based on training and learning ideas are favored by researchers. However, most BSS methods based on neural network are limited by small sample sizes. Among various neural network, generative adversarial network (GAN) has emerged as an interesting candidate because it is free from statistical constraints and samples. Therefore, we present a single-channel blind image separation algorithm based on attention mechanism GAN, coined AGAN, which uses an end-to-end manner, and it will have more hopeful prospects in the blind image separation task. The network with feature extraction, as well as edge guidance to data creates a new way to iteratively separate mixing images. The experimental results show that AGAN can effectively separate the source signal in the mixing images compared with the neural egg separation (NES) algorithm, which is a neural network separation algorithm. Compared with the classical blind source separation algorithms, this method has better separation performance.

In 2019 and 2020, symptoms of dwarfing, yellowing, and reddening were observed in garlic in the open fields of Shandong Province, China. Milk vetch dwarf virus (MDV) was detected from aphids and symptomatic garlic plants using polymerase chain reaction analysis. Furthermore, it was demonstrated that garlic is the natural host of MDV using the aphid transmission test. Rolling circle amplification was combined with specific primers to amplify the complete genomes of MDV and its related alphasatellites. In this study, the complete genome sequences of MDV and related alphasatellites were amplified from the garlic and aphid samples for the first time.

In 2019 and 2020, symptoms of dwarfing, yellowing, and reddening were observed in garlic in open fields in Shandong Province, China. Milk vetch dwarf virus (MDV) was detected in aphids and symptomatic garlic plants using polymerase chain reaction analysis. Furthermore, it was demonstrated using an aphid transmission test that garlic is a natural host of MDV. Rolling-circle amplification was combined with the use of specific primers to amplify the complete genomes of MDV and its related alphasatellites. This is the first report of complete genome sequences of MDV and related alphasatellites from garlic and aphid samples.

As a new type of gas-liquid microreactors, the gas-liquid mini-bubble column have potential applications. However, few studies on the flow fields in the mini-bubble column can be found at present. In this work, Particle Image Velocimetry (PIV) was first used to visually study the velocity fields, vorticity fields and bubble dynamics in the gas-liquid mini-bubble columns with column inner diameters of 1to 3 mm and mini-bubble diameters ranged from 0.7 to 1.3 mm. It is found that with the increase of superficial liquid velocity, bubbles rose from almost straight line to Z-shaped or S-shaped trajectory, and the bubble trajectory changed from 1-D to 3-D; when the bubble velocity changed, the bubble size and gas holdup decreased; bubble terminal velocity was controlled by bubble buoyancy and flow resistance, and increased slightly with bubble coalescence. These findings may provide basic reference for the design and scale-up of such mini-bubble column reactor.

In this study, a fluidized bed graphite tube evaporator with an internal vapor-liquid-solid flow boiling in the tube was established and the signals of vibration acceleration of the tube were measured. The attractors of the fluctuations of flow induced vibration acceleration were investigated by applying the chaos theory, and the evolving mechanism of the chaotic attractors of vapor-liquid-solid flow induced vibration signals at varied solid holdup and steam pressure was analyzed. The results indicate that the multiphase flow induced vibration has a chaotic nature. The chaotic attractor of the vibration acceleration signals can display the multiphase flow vibration behavior. Higher fluid mixture density and turbulent vapor bubble number result in denser, bigger and more complex chaotic attractor. Fluidized solid particles have an obvious influence on the vibration behavior of the graphite tube with internal vapor-liquid flow boiling.

The single bubble behavior in a 3 mm rectangular gas–liquid–solid mini-fluidized bed with 90 μm particles was simulated using the combination of VOF and DEM, and the results were validated by experiments. The simulation on the expansion of liquid–solid mini-fluidized shows that the profiles of liquid and particle velocities are consistent, and both become more concentrated as the expansion ratio decreases. The bubble formation size does not change with the increase of the solid holdup from 0.25 to 0.45, while the bubble rise velocity becomes lower. The upward liquid velocity in the bubble wake exceeds 50% of the instantaneous bubble velocity. The size of the low particle concentration bubble wake reducing with the increase of solid holdup can be attributed to the decrease in the bubble rise velocity and the enhancement in the inertia effect of the liquid–solid flow.

The bubble size distribution, phase holdups and mass transfer characteristics in a gas-liquid-solid circulating fluidized bed (GLSCFB) riser were simulated through the three-fluid (Euler-Euler-Euler) approach, the population balance model (PBM) and the gas-liquid mass transfer models. The three-fluid approach coupled with the PBM included the effect of bubble coalescence and breakage on the interphase forces, and gave good predictions on the bubble size distribution and the phase holdups. The bubble size distribution obtained from the PBM, together with the gas holdup, was used to calculate the gas-liquid interfacial area. For the mass transfer coefficient kl, the penetration model, the surface renewal model and the eddy cell model were tested and compared under varied superficial gas velocities, among which the eddy cell model provided the most reasonable prediction for kl. The effects of superficial gas, liquid velocities and particle circulating rate on the bubble size distribution, the phase holdups, the gas-liquid interfacial area, the mass transfer coefficient and the volumetric mass transfer coefficient were investigated numerically. It is concluded that the CFD-PBM coupled model, together with the eddy cell model could predict the hydrodynamics and mass transfer in a GLSCFB riser reasonably.

To solve the challenge of single-channel blind image separation (BIS) caused by unknown prior knowledge during the separation process, we propose a BIS method based on cascaded generative adversarial networks (GANs). To ensure that the proposed method can perform well in different scenarios and to address the problem of an insufficient number of training samples, a synthetic network is added to the separation network. This method is composed of two GANs: a U-shaped GAN (UGAN), which is used to learn image synthesis, and a pixel-to-attention GAN (PAGAN), which is used to learn image separation. The two networks jointly complete the task of image separation. UGAN uses the unpaired mixed image and the unmixed image to learn the mixing style, thereby generating an image with the “true” mixing characteristics which addresses the problem of an insufficient number of training samples for the PAGAN. A self-attention mechanism is added to the PAGAN to quickly extract important features from the image data. The experimental results show that the proposed method achieves good results on both synthetic image datasets and real remote sensing image datasets. Moreover, it can be used for image separation in different scenarios which lack prior knowledge and training samples.

The meso-scale theory, based on the energy minimization multi-scale principle, has recently inspired the accurate modeling of the multi-phase flow. In this study, an axial meso-scale flow model for the gas-liquid-solid fluidized bed is established and verified by the literature models and data. Compared with the reported axial models, this model predicts the axial distribution of all three-phase hydrodynamic parameters with fewer empirical corrections. Predictions of the present model show that the operation conditions and physical properties of solid particles obviously affect the flow behaviors of phases, such as the slip velocity, entrainment of solid particles, wake shedding parameters. Solid particles entrained by bubble wake determine the formation of axial distribution in the gas-liquid-solid fluidized bed. Region with the axial structure is the transition section, length of which depends on the entrainment of solid particles. Furthermore, behaviors of the gas bubbles and the solid particles are mutually influential.

Experiments on the gas-liquid-solid mini-fluidized beds with sizes of 1.45 and 2.3 mm show that the bed diameter has a considerable effect on the phase holdups and gas bubble size, because the macroscopic dimension (macro-scale) in a mini-fluidized bed is close to the dimensions of bubbles (meso-scale) and particles (micro-scale). Hence, the parameter of bed diameter was introduced through the modifications on the energy-minimization multi-scale (EMMS) model for the conventional three-phase mini-fluidized beds to predict the flow behavior of three-phase mini-fluidized bed. These modifications include two correction factors for the liquid and bubble slip velocities and a constraint condition on the bubble coalescence. The correction on the liquid slip velocity is to estimate the effect of column shear stress on the distribution of liquid velocity. The other correction factor is for quantifying the column resistance on the bubble rise. The correction factors were determined by the empirical correlations obtained from the experiments. Similarly, the bubble coalescence constraint is a correlation on the bubble spacing under steady state condition and determined from the experimental data. The modified meso-scale model equations were closed by this constraint condition and the solution of the model met the principle of energy minimization. The model predictions and experimental data agree well within the investigation range of experimental conditions.

The k-nearest neighborhoods (kNN) of feature points of complex surface model are usually isotropic, which may lead to sharp feature blurring during data processing, such as noise removal and surface reconstruction. To address this issue, a new method was proposed to search the anisotropic neighborhood for point cloud with sharp feature. Constructing KD tree and calculating kNN for point cloud data, the principal component analysis method was employed to detect feature points and estimate normal vectors of points. Moreover, improved bilateral normal filter was used to refine the normal vector of feature point to obtain more accurate normal vector. The isotropic kNN of feature point were segmented by mapping the kNN into Gaussian sphere to form different data-clusters, with the hierarchical clustering method used to separate the data in Gaussian sphere into different clusters. The optimal anisotropic neighborhoods of feature point corresponded to the cluster data with the maximum point number. To validate the effectiveness of our method, the anisotropic neighbors are applied to point data processing, such as normal estimation and point cloud denoising. Experimental results demonstrate that the proposed algorithm in the work is more time-consuming, but provides a more accurate result for point cloud processing by comparing with other kNN searching methods. The anisotropic neighborhood searched by our method can be used to normal estimation, denoising, surface fitting and reconstruction et al. for point cloud with sharp feature, and our method can provide more accurate result comparing with isotropic neighborhood.

The industrial cloud robotics (ICR) has the characteristics of intelligence, reliability, and scalability. In the smart manufacturing environment, ICR can be encapsulated as services through virtualization and servilization technology, enabling the rapid matching of personalized manufacturing capabilities and services for end users. However, the manufacturing resources are physically isolated and the physical workshop environment is vulnerable to dynamic disturbances, which reduces manufacturing system performance. In this context, taking the cycle time into consideration, the manufacturing service scheduling model for ICR is established and the digital twin (DT) enhanced scheduling optimization mechanism is proposed. When disturbances occur, the digital twin platform interacts with the cloud layer and physical workshop to analyze multi-source data in order to monitor the manufacturing environment in real time and optimize the production efficiency. Meanwhile, the manufacturing service scheduling based on an improved discrete differential evolution (IDDE) algorithm is proposed, in which the adaptive mutation and crossover operator and double mutation strategies are applied to converge to the optimal scheduling sequence. Finally, the case study is implemented to verify the proposed mechanism shows better performance compared with the existing optimization algorithms.

Remote ischemic perconditioning (RIPerC) results in collateral enhancement and a reduction in middle cerebral artery occlusion (MCAO) induced ischemia. RIPerC likely activates multiple metabolic protective mechanisms, including effects on matrix metalloproteinases (MMPs) and protein kinases. Here we explore if RIPerC improves neuroprotection and collateral flow by modifying the activities of MMP-9 and AMPK/e-NOS. Age matched adult male Sprague Dawley rats were subjected to MCAO followed one hour later by RIPerC (3 cycles of 15 min ischemia). Animals were euthanized 24 h post-MCAO. Haematoxylin and Eosin (HE) staining 24 h post-MCAO revealed a significant (p 0.02) reduction in the infarction volume in RIPerC treated animals (24.9 ± 5.4%) relative to MCAO controls (42.5 ± 4.2, %). TUNEL staining showed a 42.6% reduction in the apoptotic cells with RIPerC treatment (p 0.01). Immunoblotting in congruence with RT-PCR and Zymography showed that RIPerC significantly reduced MMP-9 expression and activity in RIPerC + MCAO group compared to MCAO group (218.3 ± 19.1% vs. 148.9 ± 12.05% (p 0.01). Immunoblotting revealed that RIPerC was associated with a significant 2.5-fold increase in activation of p-AMPK compared to the MCAO group (p 0.01) which was also associated with a significant increase in the e-NOS activity (p 0.01). RIPerC resulted in reduction of infarction volume, decreased apoptotic cell death and attenuated MMP-9 activity. This together with the increased activity of p-AMPK and increase in p-eNOS may, in part explain the neuroprotection and sustained increase in blood flow observed with RIPerC following acute stroke.

In order to investigate the regulation mechanisms of carotenoid biosynthesis in mango flesh, carotenoid content and the expression patterns of 15 carotenogenic gene in flesh of two mango cultivars ‘Tainong1’and ‘Hongyu’ were analsyed during fruit development and ripening. Carotenoid were increasingly accumulated during fruit growth and sharply incaresed during postharvest ripening in both cultivars. In ripe fruit, total carotenoid content in the flesh of ‘Tainong1’ was 177.16 ug/g (Fresh weight) FW, and for ‘Hongyu’ 36.24 ug/gFW. The major carotenoids in the flesh were β-Carotene and α-carotene,with minor carotenoids being β-cryptoxanthin, zeaxanthin, lutin and neoxanthin. RT-PCR analysis revealed that the expression of carotenogenic genes CRTISO, PSY, ZDS, BCH and ZEP were up-regulated in flesh, whereas the transcript levels of LCYB, LCYE and NCED were down-regulated in two cultivars and during fruit development and ripening. CRTISO, PSY, ZDS, BCH and ZEP transcripts were significantly positively correlated with the total carotenoids content, while no differences were detected for CRTISO expression between cultivars. These results suggested that PSY, ZDS, BCH and ZEP coordinately contribute to carotenoid accumulation during mango fruit development and ripening.

Mechanism modeling is an important method to quantify the flow heterogeneous phenomena of gas-liquid-solid fluidization. In this study, an improved meso-scale flow model, based on the energy minimum multi-scale (EMMS) theory, is developed to predict the global flow parameters of gas-liquid-solid fluidized bed. The improved EMMS model introduces gas bubble and solid particle accelerations, which make the model be capable of accurately predicting both the nominal-steady and the unsteady hydrodynamics of gas-liquid-solid fluidization. Compared with the reported models, the predicted results of this improved EMMS model are closer to the experiment data. Furthermore, the predictions indicate that both gas bubble acceleration and solid particle acceleration have an influence on the flow behavior of gas-liquid-solid fluidized bed, such as gas bubble diameter and relative slip velocity, which seriously affect the momentum and mass transfer between phases. Meanwhile, the accelerations of gas bubble and solid particle are not always equal to 0 at the nominal-steady state. Furthermore, the acceleration behavior of the unsteady-state gives a reasonable explanation for gas bubble coalescence. However, the relative error between model predictions and experimental data increases with the superficial gas velocity and the solid particle inertia. Thus this improved EMMS model is suitable for flow system at low superficial gas velocity with light and small solid particles.