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.     

Increased hydroxyl concentration by tungsten oxide modified h-BN promoted catalytic performance in partial oxidation of methane

Hexagonal boron nitride (h-BN) as a layered inorganic nonmetallic material has been widely used. Hydrogen peroxide (H₂O₂) modification can trigger exfoliation and afford abundant B–OH active sites at edge of h-BN, which can enhance methane activation ability. Introducing tungsten oxide (WO₃) to h-BN produces a similar effect, because doping WO₃ into h-BN resulted in electron transfer to N, inducing fracture of B–N bond, resulting in N vacancy (triboron center), exposing more B sites and promoting the generation of B–OH. Significantly, the introduction of WO₃ on the modified h-BN dramatically increased the concentration of B–OH compared with the unmodified h-BN, because H₂O₂ modification weakened B–N bond. By means of XRD, TEM, XPS,EPR, FT-IR, it is proved that the high concentration of B–OH active sites contributed to activating C–H bond, thus methane conversion and CO and H₂ selectivity were significantly improved.     

Effect of mesoporous carbon on oxygen reduction reaction activity as cathode catalyst support for proton exchange membrane fuel cell

In this paper, a new carbon support with a large number of mesoporous-structures is selected to prepare Pt/C catalysts. Transmission electron microscope (TEM) results show that the Pt/3# catalyst presents a sponge-like morphology, Pt particles are not only evenly distributed on the surface of carbon support, but also the smaller Pt particles are deposited in the mesoporous inside the support. The average diameter of Pt particles is only 2.8 nm. The membrane electrode assembly (MEA) based on Pt/3# catalyst also shows excellent performance. In conclusion, the 3# support is an idea carbon support for PEMFC, which helps to improve the oxygen reduction reaction (ORR) activity of the catalyst. Based on the “internal-Pt” structure of the support mesoporous, the efficient three-phase boundaries (TPBs) are construct to avoid the poisoning effect of ionomer on the nano-metal particles, reduce the activation impedance and oxygen mass transfer impedance, and improve the reaction efficiency.     

Influence of metal-support interactions on reaction pathways over Ni/CeZrOx–Al2O3 catalysts for ethanol steam reforming

This work investigates selective Ni locations over Ni/CeZrO_x–Al₂O₃ catalysts at different Ni loading contents and their influences on reaction pathways in ethanol steam reforming (ESR). Depending on the Ni loading contents, the added Ni selectively interacts with CeZrO_x–Al₂O₃, resulting in the stepwise locations of Ni over CeZrO_x–Al₂O₃. This behavior induces a remarkable difference in hydrogen production and coke formation in ESR. The selective interaction between Ni and CeZrO_x for 10-wt.% Ni generates more oxygen vacancies in the CeZrO_x lattice. The Ni sites near the oxygen vacancies enhance reforming via steam activation, resulting in the highest hydrogen production rate of 1863.0 μmol/g_cat·min. In contrast, for 15 and 20-wt.% Ni, excessive Ni is additionally deposited on Al₂O₃ after the saturation of Ni–CeZrO_x interactions. These Ni sites on Al₂O₃ accelerate coking from the ethylene produced on the acidic sites, resulting in a high coke amount of 19.1 mg_c/g_cat·h (20Ni/CZ-Al).     

Robust optimal operation of continuous catalytic reforming process under feedstock uncertainty

The continuous catalytic regenerative (CCR) reforming process is one of the most significant sources of hydrogen production in the petroleum refining process. However, the fluctuations in feedstock composition and flow rate could significantly affect both product distribution and energy consumption. In this study, a robust deviation criterion based multi-objective optimization approach is proposed to perform the optimal operation of CCR reformer under feedstock uncertainty, with simultaneous maximization of product yields and minimization of energy consumption. Minimax approach is adopted to handle these uncertain objectives, and the Latin hypercube sampling method is then used to calculate these robust deviation criteria. Multi-objective surrogate-based optimization methods are next introduced to effectively solve the robust operational problem with high computational cost. The level diagram method is finally utilized to assist in multi-criteria decision-making. Two robust operational optimization problems with different objectives are solved to demonstrate the effectiveness of the proposed method for robust optimal operation of the CCR reforming process under feedstock uncertainty.     

Volatile and non-volatile compound profiles of commercial sweet pickled mango and its correlation with consumer preference

Sweet pickled mango named Ma-Muang Bao Chae-Im is a traditional preserved mango from Hat Yai, Thailand. This study investigated (I) volatile and non-volatile compound profiles of commercial Ma-Muang Bao Chae-Im and (II) their relationship to consumer preference. Untargeted metabolomics profiling was performed by gas chromatography-mass quadrupole-time of flight analysis. There were 117 volatile and 44 non-volatile compounds annotated in six commercial brands of Ma-Muang Bao Chae-Im. Furthermore, 46 volatile and 19 non-volatile compounds’ discriminant markers were found by Partial least square discriminant analysis. Among those markers, sorbic and benzoic acid were observed in several brands; moreover, the combination of both compounds altered the volatile profile, especially the ester group. Partial least square regression revealed that overall consumer liking is correlated to 1-heptanol; 1-octanol; acetoin; acetic acid, 2-phenylethyl ester; D-manitol; terpenes and terpenoids, while firmness to sucrose and L-(-)-sorbofuranose. On the other hand, most ester compounds were not related to consumer preference.     

PID Control of Planar Nonlinear Uncertain Systems in the Presence of Actuator Saturation

This paper investigates PID control design for a class of planar nonlinear uncertain systems in the presence of actuator saturation. Based on the bounds on the growth rates of the nonlinear uncertain function in the system model, the system is placed in a linear differential inclusion. Each vertex system of the linear differential inclusion is a linear system subject to actuator saturation. By placing the saturated PID control into a convex hull formed by the PID controller and an auxiliary linear feedback law, we establish conditions under which an ellipsoid is contractively invariant and hence is an estimate of the domain of attraction of the equilibrium point of the closed-loop system. The equilibrium point corresponds to the desired set point for the system output. Thus, the location of the equilibrium point and the size of the domain of attraction determine, respectively, the set point that the output can achieve and the range of initial conditions from which this set point can be reached. Based on these conditions, the feasible set points can be determined and the design of the PID control law that stabilizes the nonlinear uncertain system at a feasible set point with a large domain of attraction can then be formulated and solved as a constrained optimization problem with constraints in the form of linear matrix inequalities (LMIs). Application of the proposed design to a magnetic suspension system illustrates the design process and the performance of the resulting PID control law.      

Tabular data: Deep learning is not all you need

A key element in solving real-life data science problems is selecting the types of models to use. Tree ensemble models (such as XGBoost) are usually recommended for classification and regression problems with tabular data. However, several deep learning models for tabular data have recently been proposed, claiming to outperform XGBoost for some use cases. This paper explores whether these deep models should be a recommended option for tabular data by rigorously comparing the new deep models to XGBoost on various datasets. In addition to systematically comparing their performance, we consider the tuning and computation they require. Our study shows that XGBoost outperforms these deep models across the datasets, including the datasets used in the papers that proposed the deep models. We also demonstrate that XGBoost requires much less tuning. On the positive side, we show that an ensemble of deep models and XGBoost performs better on these datasets than XGBoost alone.     

Three-dimensional and anisotropic numerical analysis of a PEM fuel cell

In this study, the effects of cell temperature and relative humidity on charge transport parameters are numerically analyzed. In order to perform this analysis, three-dimensional and anisotropic numerical models are developed. The numerical models are integrated into the experimental values for anisotropic electrical conductivities, as depending on cell temperature and relative humidity, that were obtained from our previous study. The achieved results indicate that the values of current densities in the in-plane direction increase with increasing cell temperature and relative humidity, while the current densities reach a maximum in the rib regions for both the numerical model at the through-plane direction. The behaviors of electrolyte potentials are similar with changes in the cell temperature and relative humidity. In addition, the cathode electrical potentials in both the in-plane direction and through-plane direction do not change to a considerable amount with increasing cell temperature and relative humidity.     

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。