Optimization of hydrogen enrichment via palladium membrane in vacuum environments using Taguchi method and normalized regression analysis

In this study, the separation of hydrogen from gas mixtures using a palladium membrane coupled with a vacuum environment on the permeate side was studied experimentally. The gas mixtures composed of H₂, N₂, and CO₂ were used as the feed. Hydrogen permeation fluxes were measured with membrane operating temperature in the range of 320–380 °C, pressures on the retentate side in the range of 2–5 atm, and vacuum pressures on the permeate side in the range of 15–51 kPa. The Taguchi method was used to design the operating conditions for the experiments based on an orthogonal array. Using the measured H₂ permeation fluxes from the Taguchi approach, the stepwise regression analysis was also employed for establishing the prediction models of H₂ permeation flux, followed by the analysis of variance (ANOVA) to identify the significance and suitability of operating conditions. Based on both the Taguchi approach and ANOVA, the H₂ permeation flux was mostly affected by the gas mixture composition, followed by the retentate side pressure, the vacuum degree, and the membrane temperature. The predicted optimal operating conditions were the gas mixture with 75% H₂ and 25% N₂, the membrane temperature of 320 °C, the retentate side pressure of 5 atm, and the vacuum degree of 51 kPa. Under these conditions, the H₂ permeation flux was 0.185 mol s^?1 m^?2. A second-order normalized regression model with a relative error of less than 7% was obtained based on the measured H₂ permeation flux.     

Achieving superior hydrogen storage properties via in-situ formed nanostructures: A high-capacity Mg–Ni alloy with La microalloying

Mg-based hydride is a promising hydrogen storage material, but its capacity is hindered by the kinetic properties. In this study, Mg–Mg₂Ni–LaH_x nanocomposite is formed from the H-induced decomposition of Mg₉₈Ni_1·67La_0.33 alloy. The hydrogen capacity of 7.19 wt % is reached at 325 °C under 3 MPa H₂, attributed to the ultrahigh hydrogenation capacity in Stage I. The hydrogen capacity of 5.59 wt % is achieved at 175 °C under 1 MPa H₂. The apparent activation energies for hydrogen absorption and desorption are calculated as 57.99 and 107.26 kJ/mol, which are owing to the modified microstructure with LaH_x and Mg₂Ni nanophases embedding in eutectic, and tubular nanostructure adjacent to eutectic. The LaH_2.49 nanophase can catalyze H₂ molecules to dissociate and H atoms to permeate due to its stronger affinity with H atoms. The interfaces of these nanophases provide preferential nucleation sites and alleviate the “blocking effect” together with tubular nanostructure by providing H atoms diffusion paths after the impingement of MgH₂ colonies. Therefore, the superior hydrogenation properties are achieved because of the rapid absorption process of Stage I. The efficient synthesis of nano-catalysts and corresponding mechanisms for improving hydrogen storage properties have important reference to related researches.     

基于VMD和BSA-KELM的高陡边坡位移预测模型研究

边坡位移的时间序列曲线存在复杂的非线性特性,传统的预测模型精度不足以满足预测要求。为此提出了基于变分模态分解的鸟群优化-核极限学习机的预测模型,并用于河北省某水泥厂的边坡位移预测。该方法首先采用VMD把边坡位移序列分解为一系列的有限带宽的子序列,再对各子序列分别采用相空间重构并用核极限学习机预测,采用鸟群算法优化相空间重构的嵌入维度和KELM中惩罚系数和核参数三个数值,以取得最优预测模型。最后将各个子序列预测值叠加,得到边坡位移的最终预测值。结果表明：和KELM、BSA-KELM、EEMD-BSA-KELM模型相比,基于VMD的BSA-KELM预测精度更高,为边坡位移的预测提供一种有效的方法。     

Catalytic behavior in CH4 decomposition of catalysts derived from red mud: Impact of residual Na2O

Catalyst samples for CH₄ decomposition were prepared from red mud (RM) by an acid-leaching neutralization precipitation approach. Water-washing the resultant precipitates multiple times, followed by drying at 105 °C and calcination at 500 °C, resulted in a threshold of residual Na₂O, equivalent to 96% Na₂O removal. Drying the precipitate at a higher temperature of 200 °C, followed by repeated water washing, provided a deeper Na₂O removal of 99% and made the resultant samples more active for the targeted reaction. Subsequently, four catalyst samples with a simulated red mud composition and NaOH contents from 0 to 0.3 wt% were prepared and the catalytic test results revealed that the Na₂O remaining in the RM-derived catalysts did not only inhibit their activation in CH₄ but also lower their maximal activities for CH₄ decomposition. Finally, two catalysts with the same simulated red mud composition and their Na impregnated respectively on Fe₂O₃ and a mixture support of Al₂O₃-SiO₂-TiO₂ were prepared and tested to explore the effect of Na distribution on the activation behavior of RM-derived catalysts for CH₄ decomposition. The activity testing results showed that it was the Na residual dispersed on iron oxides in the RM-derived samples to significantly inhibit the activation of CH₄ decomposition.     

Preparation of high-activity coal char-based catalysts from high metals containing coal gangue and lignite for catalytic decomposition of biomass tar

The potential of using high metals containing coal gangue and lignite to prepare high-activity coal char-based catalysts is investigated for effective biomass tar decomposition. Loose structure and rough surface are formed for these char-based catalysts with heterogeneous distribution of a large number of inorganic particles. In the biomass tar decomposition, the performance of the coal char-based catalysts is significantly influenced by the content of the metals in the raw materials and coal gangue char (GC) with the ash content as high as 50.80% exhibits the highest activity in this work. A high biomass tar conversion efficiency of 93.5% is achieved at 800 °C along with a significant increase in the fuel gas product. During the five-time consecutive tests, the catalytic performance of GC increases a little at the second or third times reuse and remains relatively stable, showing the remarkable stability of the catalyst in biomass tar decomposition applications.     

基于改进SOA-VMD的磁声发射信号去噪算法

磁声发射（MAE）是铁磁性材料磁化过程中产生的声发射信号，在构件应力检测和微观损伤检测中有着广泛的应用。针对MAE信号非稳态、复杂性、衰减性等特点，提出海鸥算法结合变分模态分解（SOA-VMD）的去噪方法，为克服海鸥算法求解过程中易陷入局部最优解问题，利用柯西变异算子产生随机迭代过程，使改进算法即柯西变异海欧算法（CVSOA）跳出早熟收敛。采用以幅值谱熵为适应度函数，优化VMD算法中分解模态个数K和二次惩戒因子α两个参数，将含噪声的MAE信号进行VMD分解重构。经仿真信号和实际检测信号分析表明，改进后的CVSOA-VMD算法全局寻优能力和去噪性能优于传统的SOA-VMD算法，降噪后的MAE信号特征值对于不同应力下均方根、偏斜度特征值的重复性更好，可靠性更高。     

Hydrogen production via ammonia decomposition catalyzed by Ni/M–Mo–N (M = Ni,Co) bimetallic nitrides

This study demonstrates the significant improvement in NH₃ decomposition using Ni-decorated M–Mo–N-based catalysts (M = Co and Ni) compared with conventional catalysts. Catalysts are prepared using a mixture of the corresponding metal salts and hexamethylenetetramine, and the impregnation method is used to decorate the Ni-particles on the catalysts. Among all the samples, 10 wt% Ni-decorated Co₃Mo₃N exhibits the highest NH₃ conversion rate (71%) at 500 °C, and the performance remains stable for 30 h of long-term testing. According to the gas chromatography measurements, the H₂/N₂ ratio is approximately 3 in all cases, which is consistent with the theoretical value. X-ray photoelectron spectroscopy results show that Co₃Mo₃N possesses the highest NH₃ conversion efficiency because of the weaker binding energy of Mo–N. Furthermore, Co₃Mo₃N exhibits a stronger Lewis acidity and higher NH₃ decomposition, which is attributed to the easy breaking of the N–H bond on the Co₃Mo₃N surface.     

用于厚屏蔽小探测器的蒙特卡罗模拟减方差方法研究

反应堆屏蔽计算中经常出现厚屏蔽、小探测器问题，常规蒙特卡罗方法难以有效解决。基于自动重要抽样（AIS）方法，本文提出了小探测器自动重要抽样(SDAIS)方法，并针对小探测器问题，优化了AIS方法的虚粒子赌分裂算法。该方法在自主开发的蒙特卡罗屏蔽程序MCShield上进行了实现。使用NUREG/CR-6115 PWR基准题验证该方法的正确性和计算效率。结果表明，SDAIS方法可有效地解决厚屏蔽小探测器问题，相比AIS方法及传统的重要性方法，计算效率提升1~2个量级。     

Evolutionary collective behavior decomposition model for time series data mining

In this research, we propose a novel framework referred to as collective game behavior decomposition where complex collective behavior is assumed to be generated by aggregation of several groups of agents following different strategies and complexity emerges from collaboration and competition of individuals. The strategy of an agent is modeled by certain simple game theory models with limited information. Genetic algorithms are used to obtain the optimal collective behavior decomposition based on history data. The trained model can be used for collective behavior prediction. For modeling individual behavior, two simple games, the minority game and mixed game are investigated in experiments on the real-world stock prices and foreign-exchange rate. Experimental results are presented to show the effectiveness of the new proposed model.     

Transport Map sampling with PGD model reduction for fast dynamical Bayesian data assimilation

The motivation of this work is to address real-time sequential inference of parameters with a full Bayesian formulation. First, the proper generalized decomposition (PGD) is used to reduce the computational evaluation of the posterior density in the online phase. Second, Transport Map sampling is used to build a deterministic coupling between a reference measure and the posterior measure. The determination of the transport maps involves the solution of a minimization problem. As the PGD model is quasi-analytical and under a variable separation form, the use of gradient and Hessian information speeds up the minimization algorithm. Eventually, uncertainty quantification on outputs of interest of the model can be easily performed due to the global feature of the PGD solution over all coordinate domains. Numerical examples highlight the performance of the method.