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1.
《International Journal of Hydrogen Energy》2022,47(18):10137-10155
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. 相似文献
2.
《Current Opinion in Solid State & Materials Science》2022,26(6):101024
One of the main challenges in the laser powder bed fusion (LPBF) process is making dense and defect-free components. These porosity defects are dependent upon the melt pool geometry and the processing conditions. Power-velocity (PV) processing maps can aid in visualizing the effects of LPBF processing variables and mapping different defect regimes such as lack-of-fusion, under-melting, balling, and keyholing. This work presents an assessment of existing analytical equations and models that provide an estimate of the melt pool geometry as a function of material properties. The melt pool equations are then combined with defect criteria to provide a quick approximation of the PV processing maps for a variety of materials. Finally, the predictions of these processing maps are compared with experimental data from the literature. The predictive processing maps can be computed quickly and can be coupled with dimensionless numbers and high-throughput (HT) experiments for validation. The present work provides a boundary framework for designing the optimal processing parameters for new metals and alloys based on existing analytical solutions. 相似文献
3.
Process analytics is one of the popular research domains that advanced in the recent years. Process analytics encompasses identification, monitoring, and improvement of the processes through knowledge extraction from historical data. The evolution of Artificial Intelligence (AI)-enabled Electronic Health Records (EHRs) revolutionized the medical practice. Type 2 Diabetes Mellitus (T2DM) is a syndrome characterized by the lack of insulin secretion. If not diagnosed and managed at early stages, it may produce severe outcomes and at times, death too. Chronic Kidney Disease (CKD) and Coronary Heart Disease (CHD) are the most common, long-term and life-threatening diseases caused by T2DM. Therefore, it becomes inevitable to predict the risks of CKD and CHD in T2DM patients. The current research article presents automated Deep Learning (DL)-based Deep Neural Network (DNN) with Adagrad Optimization Algorithm i.e., DNN-AGOA model to predict CKD and CHD risks in T2DM patients. The paper proposes a risk prediction model for T2DM patients who may develop CKD or CHD. This model helps in alarming both T2DM patients and clinicians in advance. At first, the proposed DNN-AGOA model performs data preprocessing to improve the quality of data and make it compatible for further processing. Besides, a Deep Neural Network (DNN) is employed for feature extraction, after which sigmoid function is used for classification. Further, Adagrad optimizer is applied to improve the performance of DNN model. For experimental validation, benchmark medical datasets were used and the results were validated under several dimensions. The proposed model achieved a maximum precision of 93.99%, recall of 94.63%, specificity of 73.34%, accuracy of 92.58%, and F-score of 94.22%. The results attained through experimentation established that the proposed DNN-AGOA model has good prediction capability over other methods. 相似文献
4.
5.
Ruixiang Yi Chao Chen Chen Shi Yuxiang Li Haijun Li Yibo Ma 《Ceramics International》2021,47(15):20807-20820
Brazing, as a common method of bonding ceramic and metal, has been applied in microelectronics, aerospace, machinery and other domains extensively. The residual thermal stress in the brazed joint has direct effects on the mechanical properties of the joint, so how to control the generation of residual thermal stress has become the vital point. In this paper, the methods of reducing residual thermal stress in the brazing process in recent years are reviewed. The generation and effects of residual thermal stress in the brazed joint are introduced. Besides, the methods of detecting residual thermal stress are discussed, and different methods of reducing residual thermal stress in brazed joints are also analyzed. Finally, the future development directions of reducing residual thermal stress in the brazed joint are proposed. 相似文献
6.
《Ceramics International》2022,48(8):10480-10485
Advanced nano-porous super thermal insulation materials are widely used in spacecraft, soler-thermal shielding, heat exchangers, photocatalytic carriers due to their low thermal conductivity. In this work, adopting dry preparation technology, nano-Al2O3, nano-SiO2, SiC and glass fibers as raw materials, novel nanometer alumina-silica insulation board (NAIB) were prepared. The preparation process was simple, safe, and reliable. In addition, the NAIB exhibited a high porosity (91.3–92.3%), small pore size (39.83–44.15 nm), low bulk density (0.22–0.26 g/cm3), better volumetric stability, and low thermal conductivity (0.031–0.050 W/(m·K) (200–800 °C)), respectively. The as-prepared NAIB could render them suitable for application as high-temperature thermal insulation materials. 相似文献
7.
8.
An analytical model is presented to study the dynamics of wavelength division multiplexing (WDM) networks with waveband switching (WBS). The reduced load approximation method is considered to compute approximated network blocking probabilities in WBS-based WDM networks. The analytical model considers the link blocking probability due to insufficient link capacity and an impact of the waveband granularity (G). The analytical model also considers the node blocking probability due to unavailability of a switch port at the wavelength cross connect (WXC) layer of an Hierarchical cross connect (HXC) switch node. The set of nonlinear equations is obtained with the link independence assumption and solved using repeated substitutions. The accuracy of the analytical model is examined by comparing with simulation results considering the random-fit algorithm for waveband and wavelength assignments in different network scenarios. Lightpaths are routed between source and destination (s-d) HXC switch nodes using shortest path first (SPF) routing. An impact of the switch parameter to limit the input and the output WXC switch ports of an HXC switching node is also being investigated using the analytical model as well as through simulation results. 相似文献
9.
Vivek Kant 《Theoretical Issues in Ergonomics Science》2018,19(2):195-212
Analysis of everyday work practices in sociotechnical systems for eliciting design/intervention requirements involves appropriate work analysis frameworks. The current article provides an extension to one such framework — Cognitive Work Analysis (CWA) — by scrutinising its sociotechnical basis. CWA's forte depends on its ‘design for adaptation’, system related operations, and operators. In contrast, sociotechnical work systems require not only operators and adaptation, but also a significant emphasis on ‘users’ and ‘appropriation’. The current article extends CWA (based on Rasmussen's original concepts) for users; subsequently allowing for system flexibility and possibilities of ‘appropriation’ within acceptable boundaries of the system's correct functioning. To this end, the first phase of Work Domain Analysis is extended by adding a new layer to the abstraction hierarchy (AH), based on Rasmussen's original insights. 相似文献
10.
Principal component analysis (PCA) is one of the most widely used techniques for process monitoring. However, it is highly sensitive to sparse errors because of the assumption that data only contains an underlying low-rank structure. To improve classical PCA in this regard, a novel Laplacian regularized robust principal component analysis (LRPCA) framework is proposed, where the “robust” comes from the introduction of a sparse term. By taking advantage of the hypergraph Laplacian, LRPCA not only can represent the global low-dimensional structures, but also capture the intrinsic non-linear geometric information. An efficient alternating direction method of multipliers is designed with convergence guarantee. The resulting subproblems either have closed-form solutions or can be solved by fast solvers. Numerical experiments, including a simulation example and the Tennessee Eastman process, are conducted to illustrate the improved process monitoring performance of the proposed LRPCA. 相似文献