Eogenetic karst and its control on reservoirs in the Ordovician Majiagou Formation, eastern Sulige gas field, Ordos Basin, NW China

To further ascertain the origin of the Ordovician Majiagou Formation reservoirs in the Ordos Basin, the M54-M51 sub-members of the Ordovician Majiagou Formation in the eastern Sulige gasfield of Ordos Basin were taken as examples to analyze the vertical development characteristics of eogenetic karst and to discover the dissolution mechanism and its control on reservoirs through observation of a large number of cores and thin sections. According to detailed analysis of petrologic characteristics, the reservoir rock types include micritic dolomite, grain dolomite and microbialite which have mainly moldic pore, intergranular (dissolved) pore, and (dissolved) residual framework pore as main reservoir space respectively. The study area developed upward-shallowing sequences, with an exposure surface at the top of a single upward-shallowing sequence. The karst systems under the exposure surface had typical exposure characteristics of early dissolution and filling, indicating these reservoirs were related to the facies-controlled eogenetic karstification. With the increase of karstification intensity, the reservoirs became worse in physical properties

An Application of Artificial Neural Network for Predicting Threshing Performance in a Flexible Threshing Device

Rice is a widely cultivated food crop worldwide, and threshing is one of the most important operations of combine harvesters in grain production. It is a complex, nonlinear, multi-parameter physical process. The flexible threshing device has unique advantages in reducing the grain damage rate and has already been one of the major concerns in engineering design. Using the measured test database of the flexible threshing test bench, the rotation speed of the threshing cylinder (RS), threshing clearance of the concave sieve (TC), separation clearance of the concave sieve (SC), and feeding quantity (FQ) are used as the input layer. In contrast, the crushing rate (YP), impurity rate of the threshed material (YZ), and loss rate (YS) are used in the output layer. A 4-5-3-3 artificial neural network (ANN) model, with a backpropagation learning algorithm, was developed to predict the threshing performance of the flexible threshing device. Next, we explored the degree to which the inputs affect the outputs. The results showed that the R of the threshing performance model validation set in the hidden layer reached 0.980, and the root mean square error (RMSE) and the average absolute error (MAE) were less than 0.139 and 0.153, respectively. The built neural network model predicted the performance of the flexible threshing device, and the regression determination coefficient R2 between the prediction data and the experimental data was 0.953. The results showed revealed that the data combined with the ANN method is an effective approach for predicting the threshing performance of the flexible threshing device in rice. Moreover, the sensitivity analysis showed that RS, TC, and SC were crucial factors influencing the performance of the flexible threshing device, with an average relative importance of 15.00%, 14.89%, and 14.32%, respectively. FQ had the least effect on threshing performance, with an average threshing relative importance of 11.65%. Our findings can be leveraged to optimize the threshing performance of future flexible threshing devices

Online Adaptive Model Identification and State of Charge Estimation for Vehicle-Level Battery Packs

Accurate state of charge (SOC) estimation of traction batteries plays a crucial role in energy and safety management for electric vehicles. Existing studies focus primarily on cell battery SOC estimation. However, numerical instability and divergence problems might occur for a large-size lithium-ion battery pack consisting of many cells. This paper proposes a high-performance online model identification and SOC estimation method based on an adaptive square root unscented Kalman filter (ASRUKF) and an improved forgetting factor recursive least squares (IFFRLS) for vehicle-level traction battery packs. The model parameters are identified online through the IFFRLS, where the conventional method might encounter numerical stability problems. By updating the square root of the covariance matrix, the divergence problem in the traditional unscented Kalman filter is solved in the ASRUKF algorithm, where the positive semi-definiteness of the covariance matrix is guaranteed. Combined with the adaptive noise covariance matched filtering algorithm and real-time compensation of system error, the proposed method solves the problem of ever-degrading estimation accuracy in the presence of time-varying noise with unknown statistical characteristics. Using a 66.2-kWh vehicle battery pack, we experimentally verified that the proposed algorithm could achieve high estimation accuracy with guaranteed numerical stability. The maximum error of SOC estimation can be bounded by 1%, and the root-mean-square error is as low as 0.47% under real-world vehicle operating conditions

Sorption induced structural deformation of sodium hexa-titanate nanofibres and their ability to selectively trap radioactive Ra(II) ions from water

Sodium hexa-titanate (Na2Ti6O13) nanofibers, which have microporous tunnels, were prepared by heating sodium tri-titanate nanofibers with a layered structure at 573 K. The void section of the tunnels consist of eight linked TiO6 octahedra, having a quasi-rectangular shape and the sodium ions located in these tunnel micropores are exchangeable. The exchange of these sodium ions with divalent cations, such as Sr2+ and Ba2+ ions, induces moderate structural deformation of the tunnels due to the stronger electrostatic interactions between di-valent ions Sr2+ and Ba2+ and the solid substrate. However, as the size of Ba2+ ions (0.270 nm) is larger than the minimum width (0.240 nm) of the tunnel, the deformation can lock the Ba2+ ions in the nanofibers, whereas Sr2+ ions (0.224 nm) are smaller than the minimum width so the fibers can release the Sr2+ ions exchanged into the channels instead. Therefore, the hexa-titanate (Na2Ti6O13) nanofibers display selectivity in trapping large divalent cations, since the deformed tunnels cannot trap smaller cations within the fibers. The fibers can be used to selectively remove radioactive Ra2+ ions, which have a similar size and ion-exchange ability to Ba2+ ions, from wastewater for safe disposal.No Full Tex

Operating conditions combination analysis method of optimal water management state for PEM fuel cell

ABSTRACT: The water content of proton exchange membrane fuel cells (PEMFCs) affects the transport of reactants and the conductivity of the membrane. Effective water management measures can improve the performance and extend the lifespan of the fuel cell. The water management state of the stack is influenced by various external operating conditions, and optimizing the combination of these conditions can improve the water management state within the stack. Considering that the stack's internal resistance can reflect its water management state, this study first establishes an internal resistance-operating condition model that considers the coupling effect of temperature and humidity to determine the variation trend of total resistance and stack humidity with single-factor operating conditions. Subsequently, the water management state optimization method based on the ANN-HGPSO algorithm is proposed, which not only quantitatively evaluates the influence weights of different operating conditions on the stack's internal resistance but also efficiently and accurately obtains the optimal combination of five operating conditions: working temperature, anode gas pressure, cathode gas pressure, anode gas humidity, and cathode gas humidity to achieve the optimal water management state in the stack, within the entire range of current densities. Finally, the response surface experimental results of the stack also validate the effectiveness and accuracy of the ANN-HGPSO algorithm. The method mentioned in this article can provide effective strategies for efficient water management and output performance optimization control of PEMFC stacks

Mechanism of supported gold nanoparticles as photocatalysts under ultraviolet and visible light irradiation

Gold nanoparticles strongly absorb both visible light and ultraviolet light to drive an oxidation reaction for a synthetic dye, as well as phenol degradation and selective oxidation of benzyl alcohol under UV light

Supported silver nanoparticles as photocatalysts under ultraviolet and visible light irradiation

The significant activity for dye degradation by silver nanoparticles (NPs) on oxide supports was better than popular semiconductor photocatalysts. Moreover, silver photocatalysts can degrade phenol and drive oxidation of benzyl alcohol to benzaldehyde under ultraviolet light. We suggest that surface plasmon resonance (SPR) effect and interband transition of silver NPs can activate organic molecules for oxidation under ultraviolet and visible light irradiation

Spatial variation and controls of soil microbial necromass carbon in a tropical montane rainforest

Altres ajuts: Fundación Ramón Areces project CIVP20A6621.Soil microbial necromass carbon is an important component of the soil organic carbon (SOC) pool which helps to improve soil fertility and texture. However, the spatial pattern and variation mechanisms of fungal- and bacterial-derived necromass carbon at local scales in tropical rainforests are uncertain. This study showed that microbial necromass carbon and its proportion in SOC in tropical montane rainforest exhibited large spatial variation and significant autocorrelation, with significant high-high and low-low clustering patterns. Microbial necromass carbon accounted for approximately one-third of SOC, and the fungal-derived microbial necromass carbon and its proportion in SOC were, on average, approximately five times greater than those of bacterial-derived necromass. Structural equation models indicated that soil properties (SOC, total nitrogen, total phosphorus) and topographic features (elevation, convexity, and aspect) had significant positive effects on microbial necromass carbon concentrations, but negative effects on its proportions in SOC (especially the carbon:nitrogen ratio). Plant biomass also had significant negative effects on the proportion of microbial necromass carbon in SOC, but was not correlated with its concentration. The different spatial variation mechanisms of microbial necromass carbon and their proportions in SOC are possibly related to a slower accumulation rate of microbial necromass carbon than of plant-derived organic carbon. Geographic spatial correlations can significantly improve the microbial necromass carbon model fit, and low sampling resolution may lead to large uncertainties in estimating soil carbon dynamics at specific sites. Our work will be valuable for understanding microbial necromass carbon variation in tropical forests and soil carbon prediction model construction with microbial participation