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.     

Deep Learning Based Process Analytics Model for Predicting Type 2 Diabetes Mellitus

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.     

稀土灼烧过程温湿度场仿真分析

影响稀土灼烧工艺的因素十分复杂,关系产品质量稳定及能耗,现行工艺存在优化空间。通过剖析灼烧窑中温度和湿度分布状况,运用κ-ε双方程湍流模型、流体传热、多孔介质传热等理论,按特定组分运输模式,建立灼烧过程质量、动量和能量耦合传递数学模型。设置不同边界导入Fluent环境对数学模型进行仿真试验,完成数据处理实现工艺参数优化。结果表明所建模型能准确反映灼烧窑中温湿度场分布及变化,且最终仿真结果与实际灼烧后的产品湿度含量相符合。     

Research advances in residual thermal stress of ceramic/metal brazes

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.     

Novel nanometer alumina-silica insulation board with ultra-low thermal conductivity

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.     

生产工艺多方案并行与快速转换方法研究

生产系统复杂多变,在生产管理过程中存在多变性和不均衡性。对此,实际生产中需要实现生产工艺多方案并行与快速转换。分析了生产过程变化因素,介绍了主工艺+子工艺这一生产工艺多方案并行与快速转换方法,并通过实例验证了这一方法的有效性。     

Laplacian regularized robust principal component analysis for process monitoring

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.     

探索电工电子课程多元化的过程性考核方式

考试是高校实现人才培养目标的落脚点,探索基于实践项目制作、基于信息化手段、基于非标考题等多元化的过程性考核方式改革,加大过程性考核的分值比重,增加过程性考核方式的趣味性和创新性,优化电类课程考核模式,进而达到全面提升电工电子课程的高阶性、创新性和挑战度的终极目标。     

The relation between BPM culture,BPM methods,and process performance: Evidence from quantitative field studies

Business process management (BPM) research conceptualizes BPM culture as a type of organizational culture that supports BPM. No quantitative fieldwork has so far examined how such a supporting role manifests itself. We study the relationship between BPM culture, BPM methods, and process performance empirically. Our analysis of multiple survey data sets from a total of 581 practitioners of multiple industries suggests that BPM methods indirectly contribute to process performance by establishing a BPM culture. This finding updates the prevalent assumption that the correct application of methods yields direct performance benefits. We discuss several implications for theory and practice.     

Weighted preliminary-summation-based principal component analysis for non-Gaussian processes

For industrial chemical process, preliminary-summation-based principal component analysis (PS-PCA), an amended PCA method was recently provided for coping with both Gaussian and non-Gaussian characteristics. By summing the training and monitoring data respectively, PS-PCA is capable of resolving the issue of non-Gaussian processes and achieves higher fault detection rate than the traditional PCA. However, in the PS-PCA summation operation, all data samples are regarded as the same weight, which results in the fault information of newly-samples may be diluted, leading to significant detection delays. To address this challenge, in this paper, we propose a novel weighted PS-PCA (WPS-PCA) method that employs an exponential weighting scheme to put more emphasis on recent information. Subsequently, a mathematical argument demonstrates that when the number of variables is enough plentiful, the obtained summation combined with the generalized central limit theorem conforms to approximately a Gaussian distribution. The kurtosis relationships indicate this conversion will bring out well-pleasing feasibility for conventional PCA. Ultimately, the proposed technique verifies detection performance using the Tennessee Eastman process, which is compared with the existing PCA and PS-PCA schemes, in terms of the fault detection time and fault detection rate. The simulation studies reveal that the proposed method is efficient and superior.