Processes, Volume 12, Issue 6 (June 2024) – 110 articles

The classifier is an essential tool for the development of contemporary engineering technology. The application of classifiers is to categorize mixed-sized particles into multi-stage uniform particle sizes. In current studies, the particles in the classifier obtain their initial velocity when feeding. The classification effect is impacted by the inability to precisely control the initial state of the particles. To solve this problem, a pusher feed classifier was designed in this study, and a numerical simulation was performed to investigate its flow field characteristics and classification performance using the RNG-DPM method. A pusher is utilized to achieve particle feeding without initial velocity and to precisely control the initial state of the particles in the classification flow field. A newly developed two-way air inlet structure is designed to provide a superimposed flow field and enable the five-stage classification. Our results show that this pusher feed classifier has the best classification effect when the vertical airflow velocity is 10 m/s and the horizontal airflow velocity is 3 m/s. Meanwhile, the classification size ratio (CSR) from outlet 1 to outlet 5 was 1.24, 0.55, 0.45, 0.39, and 0.15, respectively. Full article

The erosive wear encountered in some industrial processes results in economic loss and even disastrous consequences. Hitherto, the mechanism of the erosive wear is not clear, especially when the erosive wear is caused by large particles (>3.0 mm) carried by a flowing liquid. Current approaches of predicting erosive wear need improvement, and the optimization of relevant equipment and systems lacks a sound guidance. It is of significance to further explore such a subject based on the relevant literature. The present review commences with a theoretical analysis of the dynamics of large particles and the fundamental mechanism of erosion. Then the characteristics of the erosion of various equipment are explicated. Effects of influential factors such as particle size and properties of the target material are analyzed. Subsequently, commonly used erosion models, measurement techniques, and numerical methods are described and discussed. Based on established knowledge and the studies reported, some expectations for future work are proposed. Full article

The main purpose of this study was to develop observation techniques and processing technology for the electrochemical discharge machining (ECDM) of sapphire wafers. To measure the effect of gas-film thickness, discharge-spark conditions, and droplet sliding frequency on machining quality and efficiency in ECDM, this research utilized high-speed cameras to observe the gas film thickness and formation of the gas film during ECDM. Additionally, this study observed the machining-gap phenomena during ECDM. The formation mechanism and machining characteristics of the gas film were understood through experiments. The machining parameters included the liquid level, working voltage, rotation speed, and duty factor. This study analyzed and discussed the effect of each machining parameter on the gas-film thickness, current, electrode consumption, and droplet sliding frequency. Moreover, this study aimed to obtain optimized machining parameters to overcome the difficulty of machining sapphire. The experimental results indicated that utilizing a high-speed camera to capture the phenomena between electrodes during electrochemical discharge could effectively observe the gas-film thickness and the coverage of the gas film. A higher bubble coalescence rate enhanced the machining capability and reduced the lateral discharge. Therefore, this study could obtain better machining-hole depths through observation and analysis to improve gas-film stability and machining capability. This study demonstrated that a liquid level of 700 µm, a working voltage of 48 V, a duty factor of 50%, and a tool electrode rotational speed of 200 rpm could achieve a hole depth of 86.7 µm and a hole diameter of 129.5 µm. Full article

Cucumbers are a popular vegetable consumed worldwide and are known for their nutritional value, containing carbohydrates, antioxidants, vitamin C, etc. The abundance of a high moisture content is correlated to cucumber perishability, which encourages investigation into ways to maintain its quality and increase shelf-life. This study was carried out to determine the effect of a moringa oil–beeswax coating on the quality of fresh cucumber at different storage temperatures for 27 days of storage. Freshly harvested cucumbers were divided into two groups: the first group was coated with the moringa oil–beeswax edible coating, while the other one was not coated (control). Each group was divided into three other subgroups, for storage at 4, 10, and 22 °C. Different quality parameters, including weight loss, color change, firmness, total soluble solid (TSS), vitamin C, and pH, were evaluated. The findings showed that the weight loss of cucumber was highly increased in non-coated samples stored at high temperature. After 27 days of storage, the highest and lowest weight reduction % were recorded for non-coated cucumbers stored at 22 °C (38.09%) and moringa oil–beeswax-coated cucumbers stored at 10 °C (12.35%), respectively. Color analysis revealed that coating had a significant impact on color values, with distinct patterns in lightness, redness-greenness, and yellowness values for both treatments at various temperatures and days. The lightness values showed minimal fluctuations and stabilized at 13.65 at both 4 °C and 10 °C. Temperature and coating had a significant impact on pH levels, with the coating potentially exhibiting a protective effect on pH stability, particularly at lower temperatures (4 °C). Additionally, both groups’ total acidity levels held steady over time and at various temperatures, with the coating having a highly significant effect on total acidity levels. The amount of vitamin C varied significantly with temperature and storage period, but the coating did not affect vitamin C content. At 22 °C, there were notable variations in the vitamin C content over the storage period, with a final value of 37.7 mg/L on coated samples. Temperature and the duration of storage (p < 0.05) had a significant impact on the levels of total soluble solids (TSS), whereas firmness values changed significantly over the storage period only. Moringa oil–beeswax edible coating has the potential to preserve the nutritional value and quality attributes of cucumber. Full article

This work presents the reconstruction of the pressure head map of a water distribution system (WDS). This approach relies on historical data collected from a reduced number of sensors placed at some nodes of the WDS. Thus, a Gaussian regression process is then applied to estimate the pressure head at those nodes without a sensor, which allows the reconstruction of the pressure map for the whole network. Then, for leak diagnosis purposes, a dataset of pressure head maps of the WDN is created considering leaky scenarios, and a correlation method is applied to estimate the leak location. Then, for clarity, the Hanoi network is used to evaluate the performance of this leak diagnosis strategy in a simulation environment, assuming the availability of only three sensors. The results showed the potential for pressure head map reconstruction and leak localization. Full article

A review of the application of matrix solid-phase dispersion (MSPD) in the extraction of biologically active compounds and impurities from plants and food samples with a particular emphasis on conventional and new types of sorbents has been provided. An overview of MSPD applications for the isolation of organic residues from biological samples, determined using chromatographic and spectroscopic techniques, has been presented. In this study, procedural solutions that may extend MSDP applicability for the extraction such as vortex-assisted, ultrasound-assisted, microwave-assisted, and extraction with a magnetic sorbent have been discussed. Special attention has been paid to MSPD sorbents including modified silica, diatomite, magnesium silicate, alumina, carbon materials (carbon nanotubes, graphene oxide, graphene, or graphite), molecularly imprinted polymers, and cyclodextrin. An important aspect of the MSPD procedure is the use of high-purity and environmentally friendly solvents for extraction (e.g., deep eutectic solvents), with such criteria being the most important for modern analytical chemistry. Many advantages of MSPD are presented, such as high recoveries, the requirement for a smaller volume of solvent, and shorter procedure times than classical methods. Full article

In this study, Principal Component Analysis (PCA) was applied to discern the underlying trends for 31 distinct MFI (Mobil No. 5)-zeolite membranes of 11 textural, chemical, and operational factors related to manufacturing processes. Initially, a comprehensive PCA approach was employed for the entire dataset, revealing a moderate influence of the first two principal components (PCs), which collectively accounted for around 38% of the variance. Membrane samples exhibited close proximity, which prevented the formation of any clusters. To address this limitation, a subset acquisition strategy was followed, based on the findings of the PCA for the entire dataset. This resulted in an enhanced overall contribution and the revelation of diverse patterns among the membranes and the considered manufacturing factors (total variance between 55% and 77%). The segmentation of the data unveiled a robust correlation between silica (SiO₂) concentration and pervaporation conditions. Additionally, a notable clustering of the chemical compositions of the preparation solutions underscored their significant influence on the operational efficacy of MFI zeolite membranes. On the other hand, an exclusive chemical composition of the preparation solution was noticed. This highlighted the high influence of the chemical composition on the operational efficiency of MFI zeolite membranes. The coupling of PCA with experimental results can provide a data-driven enhancement strategy for the manufacturing of MFI-type zeolite membranes used for ethanol/water separation. Full article

In this study, nanostructured cerium-doped TiO₂ (Ce-TiO₂) films with the addition of different amounts of cerium (0.00, 0.08, 0.40, 0.80, 2.40, and 4.10 wt.%) were deposited on a borosilicate glass substrate by the flow coating sol-gel process. After flow coating, the deposited films were dried at a temperature of 100 °C for 1 h, followed by calcination at a temperature of 450 °C for 2 h. For the characterization of sol-gel TiO₂ films, the following analytic techniques were used: X-ray diffraction (XRD), differential thermal analysis (DTA), thermal gravimetry (TG), differential scanning calorimetry (DSC), diffuse reflectance spectroscopy (DRS), and energy dispersive X-ray spectroscopy (EDS). Sol-gel-derived Ce-TiO₂ films were used for photocatalytic degradation of ciprofloxacin (CIP). The influence of the amount of Ce in TiO₂ films, the duration of the photocatalytic decomposition, and the irradiation type (UV-A and simulated solar light) on the CIP degradation were monitored. Kinetics parameters (reaction kinetics constants and the half-life) of the CIP degradation, as well as photocatalytic degradation efficiency, were determined. The best photocatalytic activity was achieved by the TiO₂ film doped with 0.08 wt.% Ce, under both UV-A and solar irradiation. The immobilized catalyst was successfully reused for three cycles under solar light simulator radiation, with changes in photocatalytic efficiency below 3%. Full article

While technical optimization focuses on maximizing the annual energy yield of utility-scale PV parks, the ultimate goal for power plant owners is to maximize investment profit. This paper aims to bridge the gap between technical and economic approaches by using simulation data from a real-case utility-scale PV park. It analyzes how changes in configuration parameters such as the DC–AC ratio and string length and PV technologies like solar tracking systems and bifacial modules impact the economic metrics of the project, i.e., net present value (NPV) and internal rate of return (IRR). PVSyst software was utilized as a simulation tool, while in-house developed software implementing appropriate technical and economic models served as a comparison platform and was used to validate the outputs generated through PVSyst. Results indicate that the commonly used horizontal single-axis tracking configuration may economically underperform compared with fixed-tilt setups. The optimal DC–AC ratio fell within the range of 1.30 to 1.35. Extending the string length from 25 to 28 modules improved economic indexes. Additionally, fixed-tilt bifacial modules can enhance project economics if a 10% cost premium compared with standard monofacial PV modules is considered. Full article

Bohai X oilfield has reached the extra-high water cut stage of more than 95%, dominated by the bottom water reservoir. The oilfield mainly adopts horizontal-well exploitation, with the characteristics of high difficulty and low success rate for well water plugging. To solve the above problem, the segmented production technology of horizontal wells was developed to guide oilfield applications and tap their potential. In the segmented design stage, the horizontal section is objectively segmented by drilling condition analysis, optimally based on drilling through interlayers or permeability discrepancy formation, simultaneously combined with the numerical simulation method. When implementing measures, annulus chemical packer materials are squeezed between segments to effectively inhibit the fluid flow between the open hole and the sand-packing screen pipe. Moreover, the packers are used to seal between segments to effectively restrain the flow between the screen and the central tube, achieving the establishment of compartments. In the production process, the valve switch on the central tube can be independently controlled by a remotely adjustable method to achieve optimal production. This segmented production technology was successfully tested for the first time in Bohai oilfield. Up to now, a total of six compartment measures have been implemented, remarkably decreasing water cut and increasing oil production for horizontal wells in the bottom water reservoir. This method does not require water testing, and the optimal production section can be chosen through segmented independent production, greatly improving the success rate of water-plugging measures for horizontal wells. This technology opens up a new mode for the efficient development of horizontal wells in bottom water reservoirs and is planned to be widely promoted and applied in similar oilfields. Full article

Wobble-plate axial piston pumps, characterized by the lack of a slipper mechanism, experience reduced leakage in comparison to their swash-plate counterparts, which contributes to their higher volumetric efficiency. Presently, the primary focus of the research conducted by scholars both domestically and internationally is concentrated on wobble-plate axial piston pumps. The performance studies within this field are predominantly focused on investigating flow pulsation. They also investigate pressure pulsation. Additionally, they investigate cavitation phenomena. Research on inclined-axis axial piston pumps has been limited. This study focused on analyzing the operational form of the piston within an inclined-axis axial piston pump. A correction factor k was introduced based on the motion characteristics of the piston. The application of this factor significantly improved the accuracy of the simulations when compared to the experimental results. Specifically, at a load pressure of 10 MPa, the discrepancy between the simulation and the experimental data was reduced from 8.95% to 0.23%. Similarly, at a load pressure of 20 MPa, the error rate was minimized. It was reduced from 9.15% to 0.35%. This demonstrates the effectiveness of the proposed correction factor. The correction factor enhances the predictive accuracy of the pump’s performance. This enhancement is observed under varying load conditions. Full article

For mitigating global warming, polymer electrolyte membrane fuel cells have become promising, clean, and sustainable alternatives to existing energy sources. To increase the energy density and efficiency of polymer electrolyte membrane fuel cells (PEMFC), a comprehensive numerical modeling approach that can adequately predict the multiphysics and performance relative to the actual test such as an acceptable depiction of the electrochemistry, mass/species transfer, thermal management, and water generation/transportation is required. However, existing models suffer from reliability issues due to their dependency on several assumptions made for the sake of modeling simplification, as well as poor choices and approximations in material characterization and electrochemical parameters. In this regard, data-driven machine learning models could provide the missing and more appropriate parameters in conventional computational fluid dynamics models. The purpose of the present overview is to explore the state of the art in computational fluid dynamics of individual components of the modeling of PEMFC, their issues and limitations, and how they can be significantly improved by hybrid modeling techniques integrating with machine learning approaches. Furthermore, a detailed future direction of the proposed solution related to PEMFC and its impact on the transportation sector is discussed. Full article

The optimization of cell disruption is a critical step in microalgal biorefineries. We used the same batch of Tetraselmis suecica culture to compare two mechanical cell disruption techniques, focusing on the extraction yield of water-soluble molecules. The conditions for high-pressure homogenization (HPH) studied were two passes at a moderate pressure of 300 bars. For ultrasound (US) treatment, we used an amplitude of 20% (equivalent to 100 W) for 25 min. These conditions were chosen on the basis of a preliminary screen of extraction conditions. HPH extracted proteins and pigments more efficiently than US, whereas US was superior for uronic acid extraction. Interestingly, the two methods had similar extraction yields for carbohydrates under the studied conditions. We also analyzed the kinetics of molecule release by considering the centrifugation time lag for HPH and applying a first-order kinetic model for US. HPH outperformed US in terms of the immediate extraction and release of molecules. Full article

Employing catalytic decomposition to break down reducing agents in intermediate-level radioactive waste during nuclear fuel reprocessing offers significant advantages. This study focuses on investigating the deactivation behavior of 5% Ru/C catalysts by two different synthesis processes used for reducing agent destruction. Deactivation experiments were conducted by subjecting the 5% Ru/C catalysts to 100 and 150 reaction cycles. Changes in the concentration of free radicals on the carbon-based carrier were measured to analyze the loading position and loss of Ru ions. Additionally, sorption–desorption curves and pore size distributions of the four catalysts were obtained. Analysis results reveal that Ru ions on the catalyst adsorb onto active free radical sites on the carbon-based carrier. Under ultrasonic conditions, some Ru ions partially desorb from the free radical sites on the carbon-based carrier, and desorbed Ru ions may adsorb onto weak free radical sites, while undesorbed Ru ions may adsorb onto strong free radical sites. After hundreds of hours of reaction, SM1 and SM2 exhibited approximately a 30% decrease in specific surface area and pore volume compared to SM0. However, the catalyst activity remained unchanged, and the catalyst pore size remained essentially unchanged, which primarily means that the micropores on the catalyst’s surface have undergone corrosion and damage. Full article

Carbonate reservoirs account for 60% of global reserves for oil, making them one of the most important types of sedimentary rock reservoirs for petroleum production. This study aimed to identify key production features that significantly impact oil production rates, enhancing reservoir management and optimizing production strategies. A comprehensive dataset is built from reserves and production history data of 377 onshore carbonate oilfields globally, encompassing features such as production, recovery rate, and recovery degree of the whole lifecycle of an oilfield. XGBoost classifier is trained by K-fold cross-validation and its hyperparameters are optimized by Optuna optimization framework. The results show that XGBoost has the best performance evaluated with metrics including accuracy, precision, recall, and F1 score comparing with decision tree, random forest, and support vector machine. Key production features are identified by analyzing the classification feature importance of XGBoost classifier, including build-up stage cumulative production, plateau stage cumulative production, plateau stage recovery rate, plateau stage recovery degrees, and peak production. In conclusion, oilfield reserve size, build-up stage cumulative production, plateau stage cumulative production, and peak production increase, while plateau stage recovery rate decreases, and the plateau stage recovery degree of small-sized oilfields is slightly greater than that of moderate and large oilfields. The research methodology of this study can serve as a reference for studying production features of other types of oil and gas reservoirs. By applying the methodology to low-permeability oilfields, this paper concludes the key production features that are as follows: low-permeability oilfields generally have lower peak recovery rate, lower plateau stage recovery rate, lower decline stage recovery degree, and lower decline stage recovery rate, along with a wide but generally lower range of decline stage cumulative production compared to conventional oilfields. Full article

Wearing safety helmets is an important way to ensure the safety of workers’ lives. To address the challenges associated with low accuracy, large parameter values, and slow detection speed of existing safety helmet detection algorithms, we propose a receptive field-enhanced lightweight safety helmet detection algorithm called YOLOv5s-CR. First, we use a lightweight backbone, a high-resolution feature fusion network, and a small object detection layer to improve the detection accuracy of small objects while substantially decreasing the model parameters. Next, we embed a coordinate attention mechanism into the feature extraction network to improve the localization accuracy of the detected object. Finally, we propose a new receptive field enhancement module (RFEM) to substitute the SPPF module in the original network, enabling the model to acquire features under multiple receptive fields, thereby enhancing the detection precision of multi-scale objects. Using the Safety Helmet Detection dataset for validation, in contrast to the initial YOLOv5s, the parameters of the improved algorithm were reduced by 62.8% to 2.61 M, and P, R, and mAP_0.5 were increased by 1.5%, 1.2%, and 2.0%, respectively. The detection speed can reach 149FPS on the RTX3070 GPU, which satisfies the accuracy and real-time requirements for detecting safety helmets. Full article

The classification of microseismic signals represents a fundamental preprocessing step in microseismic monitoring and early warning. A microseismic signal source rock classification method based on a convolutional neural network is proposed. First, the characteristic parameters of the microseismic signals are extracted, and a convolutional neural network is constructed for the analysis of these parameters; then, the mapping relationship model between the characteristic parameters of the microseismic signals and the rock class is established. The feasibility of the proposed method in differentiating acoustic emission signals under different load conditions is verified by using acoustic emission data from laboratory uniaxial compression tests, Brazilian splitting tests, and shear tests. In the three distinct laboratory experiments, the proposed method achieved a source rock classification accuracy of greater than 90% for acoustic emission signals. The proposed and verified method provides a new basis for the preprocessing of microseismic signals. Full article

Nowadays, heavy metal pollution in soil caused by human production activities is increasingly serious. The heavy metal ions in soil inhibit plant growth and endanger human health as they can disrupt the physicochemical properties of soil. However, the elimination of heavy metals in soil is so difficult that more and more researchers are studying effective soil conditioners. The negatively charged groups in microbial communities can bind with heavy metal ions in the soil to remove them. In this paper, Cr- and Cd-polluted soils were used to simulate heavy-metal-polluted soil, and microbial compound fertilizer (MOF) was used as a soil conditioner for removing Cr and Cd in soil. The effects of different additive amounts of MOF on the physicochemical properties, the concentration of metal binding forms in soil and the enzyme activity of soil were investigated. The results showed that when the addition amount of fertilizer was 10%, the improvement effect on Cr- and Cd-polluted soils was the best. Compared with polluted soils without MOF addition, the physicochemical properties of MOF-treated polluted soils improved significantly, the concentration of effective forms of heavy metals decreased significantly, and the concentration of organic and residual forms as well as soil enzyme activity increased significantly. This indicates that the addition of MOF can increase the activity of soil microbial communities and soil fertility, and has the ability to remediate heavy-metal-polluted soil. MOF is expected to become an efficient soil conditioner for heavy metals. Full article

This study investigated the synthesis and functional characteristics of Fe₃O₄@Ag core–shell nanoparticles, focusing on the impact of amino functionalization on their structural and chemical properties. Utilizing self-assembly methods driven by electrostatic interactions, we achieved the effective adsorption of Ag nanoparticles into Fe₃O₄ cores previously modified with silane (APTES) or polymer (PEI) precursors. Our results elucidate how the type of amino precursor affects the surface charge and subsequent adsorption dynamics, revealing that PEI-modified Fe₃O₄ nanoparticles exhibit more substantial Ag nanoparticle adsorption than those modified with APTES. This enhanced adsorption was attributed to the higher density of the amine groups introduced by PEI, which also affected the electrostatic properties of the nanoparticles, as evidenced by their zeta-potential values. Moreover, this study highlighted the role of electrostatic attraction in the self-assembly process, facilitating a controlled synthesis environment that enhances the stability and functionality of nanoparticles for potential biomedical and catalytic applications. This research not only advances our understanding of nanoparticle behavior under different surface chemistries but also demonstrates the importance of surface engineering in optimizing nanoparticle performance for targeted applications. Full article

This study aimed to evaluate the sequential hydrolysis of the biomass from unconventional and versatile Y. lipolytica to recover mannoproteins, carbohydrates, and other compounds as well as to determine the antioxidant activity of ultrafiltered fractions. The crude biomass underwent autolysis, and the resulting supernatant fraction was used for mannoprotein recovery via precipitation with ethanol. The precipitate obtained after autolysis underwent acid hydrolysis, and the resulting supernatant was ultrafiltered, precipitated, and characterized. The process yields were 55.5% and 46.14% for the crude biomass grown in glucose and glycerol, respectively. The mannoprotein with higher carbohydrate content (from crude biomass grown in glycerol) exhibited a higher emulsification index of 47.35% and thermal stability (60% weight loss). In contrast, the mannoprotein with higher protein content (from crude biomass grown in glucose) showed a better surface tension reduction of 44.50 mN/m. The technological properties showed that the crude biomass and the food ingredients are feasible to apply in food processing. The fractionation of the acid hydrolysis portion allowed the evaluation of the antioxidant power synergism among the components present in the hydrolysate, mostly the protein peptide chain. The sequential hydrolysis method is viable for extracting valuable products from Y. lipolytica. Full article

CO₂-enhanced coalbed methane recovery (CO₂-ECBM) has been demonstrated as an effective enhanced oil recovery (EOR) technique that enhances the production of coalbed methane (CBM) while achieving the goal of CO₂ sequestration. In this paper, the grand canonical Monte Carlo simulation is used to investigate the dynamic mechanism of CO₂-ECBM in anthracite pores. First, an anthracite pore containing both organic and inorganic matter was constructed, and the adsorption and diffusion characteristics of CO₂ and CH₄ in the coal pores under different temperature and pressure conditions were studied by molecular dynamics (MD) simulations. The results indicate that the interaction energy of coal molecules with CO₂ and CH₄ is positively associated with pressure but negatively associated with temperature. At 307.15 K and 101.35 kPa, the interaction energies of coal adsorption of single-component CO₂ and CH₄ are −1273.92 kJ·mol⁻¹ and −761.53 kJ·mol⁻¹, respectively. The interaction energy between anthracite molecules and CO₂ is significantly higher compared to CH₄, indicating that coal has a greater adsorption capacity for CO₂ than for CH₄. Furthermore, the distribution characteristics of gas in the pores before and after injection indicate that CO₂ mainly adsorbs and displaces CH₄ by occupying adsorption sites. Under identical conditions, the diffusion coefficient of CH₄ surpasses that of CO₂. Additionally, the growth rate of the CH₄ diffusion coefficient as the temperature increases is higher than that of CO₂, which indicates that CO₂-ECBM is applicable to high-temperature coal seams. The presence of oxygen functional groups in anthracite molecules greatly influences the distribution of gas molecules within the pores of coal. The hydroxyl group significantly influences the adsorption of both CH₄ and CO₂, while the ether group has a propensity to impact CH₄ adsorption, and the carbonyl group is inclined to influence CO₂ adsorption. The research findings are expected to provide technical support for the effective promotion of CO₂-ECBM technology. Full article

To address the effect of a ship’s rolling on the fluidization quality of fluidized beds, in this study, a simulation of a rolling fluidized bed with longitudinal internal members added (R-FBLIM) was carried out and compared with that of a rolling fluidized bed without internal members added (R-FBWIM). The transient motion, as well as the behavioral characteristics of the bubbles within the R-FBLIM, was analyzed; the variation patterns of the number of bubbles, as well as the equivalent diameter of the bubbles, were compared for different apparent gas velocities, oscillation periods, and amplitudes; and the mechanism of the action of the longitudinal internal members was investigated. The results show that the structural design of the longitudinal internal members can effectively improve the gas–solid fluidization quality of the rolling fluidized bed. The horizontal support plate and the cap hole structure can effectively break the air bubbles, the cap hole structure promotes the radial mixing of the gas–solid fluid, and the internal and outer rings of the curved surface plate roll in rows, which inhibit the aggregation behavior of the gas–solid fluid to the two sides of the oscillating planes, respectively, by cooperating with the cap hole structure. Compared with R-FBWIM, the gas–solid phase within R-FBLIM is more spatially distributed, with the number of bubbles increasing by about 2–4 times and the mean diameter decreasing by about 50–60%. The number of bubbles increases with the gas velocity but decreases with the rolling amplitude; the mean diameter decreases with the gas velocity but responds less to the rolling amplitude change. Full article

N4-methylcytosine (4mC) is a critical epigenetic modification that plays a pivotal role in the regulation of a multitude of biological processes, including gene expression, DNA replication, and cellular differentiation. Traditional experimental methods for detecting DNA N4-methylcytosine sites are time-consuming, labor-intensive, and costly, making them unsuitable for large-scale or high-throughput research. Computational methods for identifying DNA N4-methylcytosine sites enable the rapid and cost-effective analysis of DNA 4mC sites across entire genomes. In this study, we focus on the identification of DNA 4mC sites in the mouse genome. Although there are already some computational methods that can predict DNA 4mC sites in the mouse genome, there is still significant room for improvement in accurately predicting them due to their inability to fully capture the multifaceted characteristics of DNA sequences. To address this issue, we propose a new deep learning predictor called Mus4mCPred, which utilizes multi-view feature learning and deep hybrid networks for accurately predicting DNA 4mC sites in the mouse genome. The predictor Mus4mCPred firstly employed different encoding methods to extract the feature vectors of DNA sequences, then input these features generated by different encoding methods into various hybrid deep learning models for the learning and extraction of more sophisticated representations of these features, and finally fused the extracted multi-view features to serve as the final features for DNA 4mC site prediction in the mouse genome. Multi-view features enabled the more comprehensive capture of data characteristics, enhancing the feature representation of DNA sequences. The independent test results showed that the sensitivity (Sn), specificity (Sp), accuracy (Acc), and Matthews’ correlation coefficient (MCC) were 0.7688, 0.9375, 0.8531, and 0.7165, respectively. The predictor Mus4mCPred outperformed other state-of-the-art methods, achieving the accurate identification of 4mC sites in the mouse genome. Full article

With the increasing popularity of clean energy, the use of micro turbines to recover surplus energy in the water supply pipelines of high-rise buildings has attracted more attention. This study adopts a predictor model based on Radial Basis Function Neural Network (RBFNN) to optimize the draft tube shape for micro-Francis turbines. The predictor model is formed on a dataset provided by numerical simulations, which are validated by lab tests. Specifically, numerical investigations are carried out in the shape of a draft tube to determine an optimal model. Additionally, the superiority of the RBFNN model in nonlinear optimization is verified by comparing it with other models under the same date sets. After that, the design parameters are optimized using RBFNN and sequential quadratic programming algorithm (SQPA). Finally, the turbine prototype is fabricated and tested on a lab test rig. The experimental results indicate that the numerical method adopted in this research is accurate enough for such a micro-Francis turbine performance prediction. Under the design conditions, the proposed micro-Francis turbine produces a power of 147 W with an efficiency of over 29%, which shows a considerable improvement compared to the initial prototype. Full article

Green supply chain management is critical for driving sustainable development and addressing escalating environmental challenges faced by companies. However, due to the multidimensionality of cost–benefit analysis and the intricacies of supply chain operations, strategic decision-making regarding green supply chains is inherently complex. This paper proposes a green supply chain optimization framework based on a two-stage heuristic algorithm. First, anchored in the interests of intermediary core enterprises, this work integrates upstream procurement and transportation of products with downstream logistics and distribution. In this aspect, a three-tier green complex supply chain model incorporating economic and environmental factors is developed to consider carbon emissions, product non-conformance rates, delay rates, and transportation costs. The overarching goal is to comprehensively optimize the trade-off between supply chain costs and carbon emissions. Subsequently, a two-stage heuristic algorithm is devised to solve the model by combining the cuckoo search algorithm with the brainstorming optimization algorithm. Specifically, an adaptive crossover–mutation operator is introduced to enhance the search performance of the brainstorming optimization algorithm, which caters to both global and local search perspectives. Experimental results and comparison studies demonstrate that the proposed method performs well within the modeling and optimization of the green supply chain. The proposed method facilitates the efficient determination of ordering strategies and transportation plans within tight deadlines, thereby offering valuable support to decision-makers in central enterprises for supply chain management, ultimately maximizing their benefits. Full article

Healthcare professionals wearing personal protective equipment (PPE) during outbreaks often experience heat strain and discomfort, which can negatively impact their work performance and well-being. This study aimed to evaluate the physiological and psychological effects of a newly designed wearable cooling and dehumidifying system (WCDS) on healthcare workers wearing PPE via a 60 min treadmill walking test. Core temperature, mean skin temperature, heart rate, and subjective assessments of thermal sensation, wetness sensation, and thermal comfort were measured throughout the test. Additionally, ratings of wearing comfort and movement comfort were recorded during a wearing trial. The results showed that the WCDS significantly reduced core temperature, improved thermal sensation, and reduced wetness sensation compared to the non-cooling condition. The microclimatic temperature within the PPE was significantly lower in the cooling condition, indicating the WCDS’s ability to reduce heat buildup. The wearing trial results demonstrated general satisfaction with the wearability and comfort of the WCDS across various postures. These findings contribute to the development of enhanced PPE designs and the improvement in working conditions for healthcare professionals on the frontlines during outbreaks. Full article

A comprehensive understanding of the mechanical characteristics of deep coalbed methane reservoir rocks (DCMRR) is crucial for the safe and efficient development of deep coalbed gas resources. In this study, the microstructural and mechanical features of the coal seam roof, floor, and the coal seam itself were analyzed through laboratory experiments. The impact mechanisms of drilling fluid and fracturing fluid hydration on the mechanical properties and failure behavior of coal seam rocks were investigated. The experimental results indicate that the main minerals in coal seams are clay and amorphous substances, with kaolinite being the predominant clay mineral component in coal seam rocks. The rock of the coal seam roof and floor exhibits strong elasticity and high compressive strength, while the rock in the coal seam section shows a lower compressive capacity with pronounced plastic deformation characteristics. The content of kaolinite shows a good correlation with the mechanical properties of DCMRR. As the kaolinite content increases, the strength of DCMRR gradually decreases, and deformability enhances. After immersion in drilling fluid and slickwater, the strength of coal seam rocks significantly decreases, leading to shear fracture zones and localized strong damage features after rock compression failure. The analysis of the mechanical properties of DCMRR suggests that the horizontal well trajectory should be close to the coal seam roof, and strong sealing agents should be added to drilling fluid to reduce the risk of wellbore collapse. Enhancing the hydration of slickwater is beneficial for the formation of a more complex fracture network in deep coalbed methane reservoir. Full article

In recent years, with the rapid development of salt cavern gas storage reservoir construction in China, the characteristics of salt rock reservoirs with strong non-homogeneity and many interlayers have brought challenges to the dynamic prediction of water solution cavity construction. Aiming to solve this problem, this paper constructs a three-dimensional non-homogeneous salt cavern reservoir water-soluble cavity building prediction model, which takes into full consideration the non-homogeneity of salt rock reservoirs, interlayers, reservoir temperatures, and water injection process parameters, among other factors. By comparing the calculation results of the software compiled by the model with those of other numerical simulation software, we show that the model can accurately reflect the influence of geological parameters on the cavity morphology under the condition of non-uniform geological parameters, with higher simulation accuracy, and ultimately analyze individual examples. It can provide important theoretical support and practical guidance for the construction of a salt cavern gas storage reservoir. Full article

Developing thermal storage materials is crucial for the efficient recovery of thermal energy. Salt-based phase-change materials have been widely studied. Despite their high thermal storage density and low cost, they still face issues such as low thermal conductivity and easy leaks. Therefore, a new type of NaCl-Al₂O₃@SiC@Al₂O₃ macrocapsule was developed to address these drawbacks, and it exhibited excellent rapid heat storage and release capabilities and was extremely stable, significantly reducing the risk of leakage at high temperatures for industrial waste heat recovery and in concentrated solar power systems above 800 °C. Thermal storage macrocapsules consisted of a double-layer encapsulation of silicon carbide and alumina and a self-standing core of NaCl-Al₂O_3. After enduring over 1000 h at a high temperature of 850 °C, the encapsulated phase-change material exhibited an extremely low weight loss rate of less than 5% compared with NaCl@Al₂O₃ and NaCl-Al₂O₃@Al₂O₃ macrocapsules, for which the weight loss rate was reduced by 25% and 10%, respectively, proving their excellent leakage prevention. The SiC powder layer, serving as an intermediate coating, further prevented leakage, while the use of Al₂O₃ ceramics for encapsulation enhanced the overall mechanical strength. It was innovatively discovered that the Al₂O₃ particles formed a network structure around the molten NaCl, playing an important role in maintaining the shape and preventing leakage of the composite thermal storage phase-change material. Furthermore, the addition of Al₂O₃ significantly enhanced the rapid heat storage and release rate of NaCl-Al₂O₃ compared to pure NaCl. This encapsulated phase-change material demonstrated outstanding durability and rapid heat storage and release performance, offering an innovative approach to the application of salt phase-change materials in the field of high temperature rapid heat storage and release and encapsulating NaCl as a high-temperature thermal storage material in a packed bed system. Compared with conventional salt-based phase-change materials, the developed product is expected to significantly improve the reliability and thermal efficiency of thermal storage systems. Full article

The proliferation of Sargassum biomass in various coastal areas has led to environmental and socio-economic problems. However, due to their unique composition, these biomasses offer versatile applications, prompting research into their potential in third-generation biorefineries. In this study, the hydrothermal processing of Sargassum sp. was evaluated under specific conditions at 190 °C/50 min and 150 °C/30 min. The resulting hydrolysates (liquid phase) were used as alternative culture media for cultivation. Nine treatments for the cultivation of Arthrospira platensis were assessed, varying the concentration of hydrothermal hydrolysates (HH) at 190 °C/50 min: T1 (5% v/v), T2 (10% v/v), and T3 (15% v/v). T4 (5% v/v), T5 (10% v/v), and T6 (15% v/v), maintaining the same HH conditions, and with the addition of 0.7 g/L NaNO₃; and treatments T7, T8, and T9 had concentrations of 5%, 10%, and 15% of HH, respectively, at 150 °C/30 min with the addition of 0.7 g/L NaNO₃, respectively. Each treatment was inoculated with 15% (v/v) of A. platensis. Growth kinetics were performed by sampling every three days for 24 days. Quantification of soluble proteins was performed for the best conditions of biomass production. The microalgae demonstrated the ability to grow under mixotrophic medium conditions and to utilize the available carbon sources in the culture medium. Treatment 4 has the highest biomass, with an Xmax (g/L) of 1.94 ± 0.06 and a protein production of 24.17 ± 0.86% (w/w). Therefore, this microalgal biomass can be used in the food matrix according to the biorefinery concept. Full article