Hybrid hydrate processes for CO2/H2 mixture purification: A techno-economic analysis

In this work, hydrate based separation technique was combined with membrane separation and amine-absorption separation technologies to design hybrid processes for separation of CO₂/H₂ mixture. Hybrid processes are designed in the presence of different types of hydrate promoters. The conceptual processes have been developed using Aspen HYSYS. Proposed processes were simulated at different flow rates for the feed stream. A comprehensive cost model was developed for economic analysis of novel processes proposed in this study. Based on the results from process simulation and equipment sizing, the amount of total energy consumption, fixed cost, variable cost, and total cost were calculated per unit weight of captured CO₂ for various flow rates of feed stream and in the presence of different hydrate promoters. Results showed that combination of hydrate formation separation technique with membrane separation technology results in a CO₂ capture process with lowest energy consumption and total cost per unit weight of captured CO₂. As split fraction and heat of hydrate formation increases, the share of hydrate formation section in total energy consumption increases. When TBAB is applied as hydrate promoter, due to its higher hydrate separation efficiency, more amount of CO₂ is captured in hydrate formation section and consequently the total cost for process decreases considerably. Hybrid hydrate-membrane process in the presence of TBAB as hydrate promoter with 29.47 US$/ton CO₂ total cost is the best scheme for hybrid hydrate CO₂ capture process. Total cost for this process is lower than total cost for single MDEA-based absorption process as the mature technology for CO₂ capture.     

Use of Fourier-Transform Infrared Spectroscopy (FT-IR) for Monitoring Experimental Helicobacter pylori Infection and Related Inflammatory Response in Guinea Pig Model

Infections due to Gram-negative bacteria Helicobacter pylori may result in humans having gastritis, gastric or duodenal ulcer, and even gastric cancer. Investigation of quantitative changes of soluble biomarkers, correlating with H. pylori infection, is a promising tool for monitoring the course of infection and inflammatory response. The aim of this study was to determine, using an experimental model of H. pylori infection in guinea pigs, the specific characteristics of infrared spectra (IR) of sera from H. pylori infected (40) vs. uninfected (20) guinea pigs. The H. pylori status was confirmed by histological, molecular, and serological examination. The IR spectra were measured using a Fourier-transform (FT)-IR spectrometer Spectrum 400 (PerkinElmer) within the range of wavenumbers 3000–750 cm⁻¹ and converted to first derivative spectra. Ten wavenumbers correlated with H. pylori infection, based on the chi-square test, were selected for a K-nearest neighbors (k-NN) algorithm. The wavenumbers correlating with infection were identified in the W2 and W3 windows associated mainly with proteins and in the W4 window related to nucleic acids and hydrocarbons. The k-NN for detection of H. pylori infection has been developed based on chemometric data. Using this model, animals were classified as infected with H. pylori with 100% specificity and 97% sensitivity. To summarize, the IR spectroscopy and k-NN algorithm are useful for monitoring experimental H. pylori infection and related inflammatory response in guinea pig model and may be considered for application in humans.     

Evaluation of the localized corrosion on Mg electrode surface by electrochemical noise technique

The symmetrical and asymmetrical electrodes made of Mg were studied in 0.1-M NaCl electrolyte adjusted at pH 12. The statistical and wavelet methods were employed for analyzing the electrochemical current noise (ECN) signals. The asymmetric configuration was used for electrochemical detection of filiform corrosion on Mg electrode. The real time scale of the dominant transients of the asymmetric electrodes was detected on the basis of the maximum peak in the SDPS plots. The SDPS values of the real time scale crystals of the ECN signals resulting from asymmetrical electrodes increased with the increase in immersion time due to the onset of filiform corrosion.     

A novel adaptive sampling based methodology for feasible region identification of compute intensive models using artificial neural network

Identification of feasible region of operations in multivariate processes is a problem of interest in several fields. This is particularly challenging when the process model is black-box in nature and/or is computationally expensive, as analytical solutions are not available and the number of possible model evaluations is limited. An efficient methodology is required to identify samples where the model is evaluated for developing a computationally efficient surrogate model. In this work, an artificial neural network based surrogate model is proposed which is integrated with a statistical-based approach (Jack-knifing) to estimate the variance of the surrogate model prediction. This allows implementation of an adaptive sampling approach where new samples are identified close to the feasible region boundary or in regions of high prediction uncertainty. The proposed approach performs better than a previously published kriging based method for different dimensionality case studies.     

改性聚乳酸发泡技术研究进展

综述了近几年国内外改性聚乳酸（PLA）发泡技术的研究进展，针对PLA在发泡方面熔体强度和结晶性能的不足，介绍了通过加入扩链剂、交联剂、成核剂、纤维和其他聚合物等物质来改善PLA发泡性能的方法、效果和机理；最后，对改性聚乳酸发泡技术的未来发展进行了展望。     

贵长猕猴桃皮HPLC指纹图谱及抗氧化谱效关系研究

目的:研究贵长猕猴桃皮HPLC指纹图谱与抗氧化活性的关系。方法:采用高效液相色谱法建立10批不同来源地贵长猕猴桃皮的指纹图谱,采用SPSS 13.0进行样品聚类分析和主成分分析;并通过DPPH和ABTS法测定其抗氧化活性,以EC50与共有峰峰面积数据为基础,应用皮尔逊相关分析法和逐步回归分析法研究谱效关系。结果:建立了10批贵长猕猴桃皮HPLC指纹图谱,确定了14个共有峰,相似度均>0.90。采用对照品比对方法指认了其中6个峰:2号峰没食子酸,5号峰原儿茶酸,9号峰表儿茶素,10号峰二氢槲皮素,11号峰4-香豆酸,12号峰落新妇苷,样本可聚为3类。皮尔逊相关分析与逐步回归分析结果显示:5号峰(原儿茶酸)和9号峰(表儿茶素)峰面积变化与贵长猕猴桃皮抗氧化活性呈显著正相关,对其抗氧化活性贡献度较大。结论:贵长猕猴桃皮具有较高的体外抗氧化活性,抗氧化活性的物质基础初步确定为贵长猕猴桃皮开发利用、质量评价提供依据。     

Feature selection methods for root-cause analysis among top-level product attributes

Manufacturing companies not only strive to deliver flawless products but also monitor product failures in the field to identify potential quality issues. When product failures occur, quality engineers must identify the root cause to improve any affected product and process. This root-cause analysis can be supported by feature selection methods that identify relevant product attributes, such as manufacturing dates with an increased number of product failures. In this paper, we present different methods for feature selection and evaluate their ability to identify relevant product attributes in a root-cause analysis. First, we compile a list of feature selection methods. Then, we summarize the properties of product attributes in warranty case data and discuss these properties regarding the challenges they pose for machine learning algorithms. Next, we simulate datasets of warranty cases, which emulate these product properties. Finally, we compare the feature selection methods based on these simulated datasets. In the end, the univariate filter information gain is determined to be a suitable method for a wide range of applications. The comparison based on simulated data provides a more general result than other publications, which only focus on a single use case. Due to the generic nature of the simulated datasets, the results can be applied to various root-cause analysis processes in different quality management applications and provide a guideline for readers who wish to explore machine learning methods for their analysis of quality data.     

Long-term performance analysis of chemical filters in clean rooms based on a prediction model

Chemical filters are the most important devices for removing gas-phase pollutants in clean rooms. However, the testing concentration of chemical filters is too high for reflecting their performance in a real clean room environment. This study tested the adsorption performance of chemical filters in the two most commonly used shapes at different concentrations. Then, the Langmuir equation and Wheeler-Jonas kinetic equation were combined to establish an adsorption performance prediction model of chemical filters under actual conditions. The predicted values of the model were in good agreement with the experimental results, which indicated the high accuracy of the prediction model. The model does not need to test the microscopic parameters of the adsorbent and can maintain high accuracy at low concentrations. A fast method for calculating the service life of chemical filters was also presented. Based on this model, the total cost of using a chemical filter with a high carbon content in microelectronic clean rooms could be decreased by 45% due to decreasing the number of filter replacements over 3 months. So a chemical filter with a high carbon content should be preferred over a filter with low resistance in microelectronic clean rooms.     

Seismic stability of embankments with different densities and upstream conditions related to the water level

Modeling an embankment by subjecting it to different upstream conditions in a centrifuge is challenging. However, the response of an embankment to shaking under different upstream conditions needs to be studied to ensure that the necessary precautions are taken during its construction and maintenance. Herein, the influence of different upstream conditions and embankment densities are investigated. The pore water pressure values at different locations in an embankment were recorded during seepage and under induced shaking. Accelerometers recorded the response of the embankment to shaking, and a linear variable differential transformer (LVDT) measured the settlement at the top of the embankment as a result of the shaking. An image analysis was used to trace the resulting deformation of the embankment due to the shaking. The results indicate that the upstream conditions of the embankment determine the failure mechanism of the embankment in cases of lower density. In addition, the distribution of the water content within the embankment was found to have a greater influence on its response to shaking than the water level height on its upstream side.     

The dynamic effect of Micro-MHD convection on bubble grown at a horizontal microelectrode

Relatively low efficiency is the biggest obstacle to the popularization of water electrolysis, which is a particularly feasible way to produce super-pure hydrogen. Imposing a magnetic field can increase the hydrogen production efficiency of water electrolysis. However, the enhancement's detailed mechanism still lacks an insightful understanding of the bubbles' micro vicinity. Our recent work aims to understand why the micro-magnetohydrodynamic (MHD) convection hinders single bubbles' detachment on the microelectrode. A water electrolysis experiment by microelectrode is performed under an electrode-normal magnetic field, and dynamic analysis of the single bubble growing on microelectrodes is performed. The variation of bubble diameter with time in the presence or absence of the magnetic field was measured, and the forces acting on the bubble were quantified. The result shows that the micro-MHD convection, induced by Lorentz force, can give rise to a downward hydrodynamic pressure force that will not appear in large-scale MHD convection. This force can be of the same magnitude as the surface tension, so it dramatically hinders bubbles' detachment. Besides, the Kelvin force provides a new potential way for further improving the efficiency of water electrolysis.