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.     

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。     

No-Reference Video Quality Assessment using novel hybrid features and two-stage hybrid regression for score level fusion

This paper presents a novel No-Reference Video Quality Assessment (NR-VQA) model that utilizes proposed 3D steerable wavelet transform-based Natural Video Statistics (NVS) features as well as human perceptual features. Additionally, we proposed a novel two-stage regression scheme that significantly improves the overall performance of quality estimation. In the first stage, transform-based NVS and human perceptual features are separately passed through the proposed hybrid regression scheme: Support Vector Regression (SVR) followed by Polynomial curve fitting. The two visual quality scores predicted from the first stage are then used as features for the similar second stage. This predicts the final quality scores of distorted videos by achieving score level fusion. Extensive experiments were conducted using five authentic and four synthetic distortion databases. Experimental results demonstrate that the proposed method outperforms other published state-of-the-art benchmark methods on synthetic distortion databases and is among the top performers on authentic distortion databases. The source code is available at https://github.com/anishVNIT/two-stage-vqa.     

A hybrid homogeneously weighted moving average control chart for process monitoring: Discussion

The sensitivity of a monitoring scheme depends on many factors including the variance of the charting statistic which is very important in the computation of the control limits. This paper discusses the computation of the variance of the recently proposed hybrid homogeneously weighted moving average (HHWMA) $\overline{X}$ scheme which was based on an incorrect assumption. The correct variance is used to evaluate the run-length characteristics of the HHWMA $\overline{X}$ scheme. It is observed that the incorrect variance has a significant impact on the sensitivity (or performance) of the HHWMA $\overline{X}$ scheme.     

Recent advances during CH4 dry reforming for syngas production: A mini review

Given the continuing issues of environment and energy, methane dry reforming for syngas production have sparked interest among researchers, but struggled with the process immaturity owing to catalyst deactivation. This review summarizes the recent advances in the development of efficient and stable catalysts with strong resistance to coking and metal sintering, including the application of novel materials, the assessment of advanced characterizations and the compatibility to improved reaction system. One feasible option is the crystalline oxide catalysts (perovskite, pyrochlore, spinel and LDHs), which feature a fine metal dispersion and surface confinement effect via a metal exsolution strategy and exhibit superior reactivity and stability. Some new materials (h-BN, clays and MOFs) also extend the option because of their unique morphology and microstructure. It also is elaborated that progresses were achieved in advanced characterizations application, leading to success in the establishment of reaction mechanisms and attributions to the formed robust catalysts. In addition, the perspective described the upgrade of reaction system to a higher reaction efficiency and milder reaction conditions. The combination of efficient reaction systems and robust catalysts paves a way for a scaling-up application of the process.     

Effect of support and reduction temperature in the hydrogenation of CO2 over the Cu–Pd bimetallic catalyst with high Cu/Pd ratio

Metal-support interaction and catalyst pretreatment are important for industrial catalysis. This work investigated the effect of supports (SiO₂, CeO₂, TiO₂ and ZrO₂) for Cu–Pd catalyst with high Cu/Pd ratio (Cu/Pd = 33.5) regarding catalyst cost, and the reduction temperatures of 350 °C and 550 °C were compared. The activity based on catalyst weight follows the order of Si > Ce > Zr > Ti when reduced at 350 °C. The reduction temperature leads to the surface reconstruction over the SiO₂, CeO₂ and TiO₂ catalysts, while results in phase transition over Cu–Pd/ZrO₂. The effect of reduction temperature on catalytic performance is prominent for the SiO₂ and ZrO₂ supported catalysts but not for the CeO₂ and TiO₂ ones. Among the investigated catalysts, Zr-350 exhibits the highest methanol yield. This work reveals the importance of the supports and pretreatment conditions on the physical-chemical properties and the catalytic performance of the Cu–Pd bimetallic catalysts.     

Classification of the cardiotocogram data for anticipation of fetal risks using machine learning techniques

The aim of the research is evaluating the classification performances of eight different machine-learning methods on the antepartum cardiotocography (CTG) data. The classification is necessary to predict newborn health, especially for the critical cases. Cardiotocography is used for assisting the obstetricians’ to obtain detailed information during the pregnancy as a technique of measuring fetal well-being, essentially in pregnant women having potential complications. The obstetricians describe CTG shortly as a continuous electronic record of the baby's heart rate took from the mother's abdomen. The acquired information is necessary to visualize unhealthiness of the embryo and gives an opportunity for early intervention prior to happening a permanent impairment to the embryo. The aim of the machine learning methods is by using attributes of data obtained from the uterine contraction (UC) and fetal heart rate (FHR) signals to classify as pathological or normal. The dataset contains 1831 instances with 21 attributes, examined by applying the methods. In the paper, the highest accuracy displayed as 99.2%.     

Event-triggered variable horizon Supervisory Predictive control of hybrid power plants

The supervision of a hybrid power plant, including solar panels, a gas microturbine and a storage unit operating under varying solar power profiles is considered. The Economic Supervisory Predictive controller assigns the power references to the controlled subsystems of the hybrid cell using a financial criterion. A prediction of the renewable sources power is embedded into the supervisor. Results deteriorate when the solar power is unsteady, owing to the inaccuracy of the predictions for a long-range horizon of 10 s. The receding horizon is switched between an upper and a lower value according to the amplitude of the solar power trend. Theoretical results show the relevance of horizon switching, according to a tradeoff between performance and prediction accuracy. Experimental results, obtained in a Hardware In the Loop (HIL) framework, show the relevance of the variable horizon approach. Power amplifiers allow us to simulate virtual components, such as a gas microturbine, and to blend their powers with that of real devices (storage unit, real solar panels). In this case, fuel savings, reaching 15%, obtained under unsteady operating conditions lead to a better overall performance of the hybrid cell. The overall savings obtained in the experiments amount to 12%.     

Prediction of sediment,particulate nutrient and dissolved nutrient concentrations in a dry tropical river to provide input to a mechanistic coastal water quality model

A Generalised Additive Modelling (GAM) approach is applied to prediction of both particulate and dissolved nutrient concentrations in a wet-tropical river (the Fitzroy River, Queensland, Australia). In addition to covariant terms considered in previous work (i.e. flow, discounted flow and a rising-falling limb term), we considered several new potential covariates: meteorological and hydrological variables that are routinely monitored, available in near-real time, and were considered to have potential predictive power. Of the additional terms considered, only flows from three tributaries of the Fitzroy River (namely, the Nogoa, Comet and Isaac Rivers) were found to significantly improve the model. Inclusion of one or more of these additional flow terms greatly improved results for dissolved nitrogen and dissolved phosphorus concentrations, which were not otherwise amenable to prediction. In particular, the Nogoa sub-catchment, dominated by pasture for cattle, was found to be important in determining dissolved inorganic nitrogen and phosphorus concentrations reaching the river mouth. This insight may direct further research, including future refinement of processed-based catchment models. The GAMs described here are used to provide near real-time river boundary conditions for a complex coupled hydrodynamic and biogeochemical model of the Great Barrier Reef Lagoon, and can be coupled with a forecasting hydrological model to allow integrated forecasting simulations of the catchment to coast system.     

Control of the particle microstructure during the synthesis of bulk-polymerized transparent methacrylate-acrylonitrile-butadiene-styrene resin

A series of methacrylate-acrylonitrile-butadiene-styrene (MABS) resins was prepared using bulk polymerization. The polarity of the continuous phase and the compatibility of two phases were changed by adjusting the methyl methacrylate (MMA) content, choosing values that were close to styrene-butadiene rubber solubility value. The possibility of controlling the microstructure of the MABS resin by changing the polarity of the components and the compatibility of two phases was assessed. The dynamic mechanical analysis shows that the compatibility of two phases varies with the MMA content. The morphological analysis shows that increasing MMA contents results in a gradual decrease in the sub-inclusion structure with a network skeleton of rubber particles, and that all the particles become solid rubber when the MMA content reaches 75%. The sub-inclusion structure reappears but does not have a network skeleton when the MMA content is 90%. The impact strength and morphological analysis indicate that the solid rubber particles and the sub-inclusion structure with a network skeleton provide excellent toughness, while the sub-inclusion structure without a network skeleton does not. In contrast, the transmittance of the ABS resin first increased and then decreased with increasing MMA content.