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.     

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。     

Hydrogen production with an auto-thermal MSW steam gasification and direct melting system: A process modeling

Municipal solid waste steam gasification and direct melting system is proposed in this study for H₂ production and ash melting simultaneously. Part of the H₂ generated in gasification is extracted for combustion with pure oxygen in the melting zone to provide the energy necessary for auto-thermal operation. A simulation model is developed with Aspen Plus to investigate the performance and optimum conditions of the system. For the feedstock with a lower heating value of 18.91 MJ/kg used in this study, 39.8% of the generated H₂ needs to be extracted to maintain the heat balance of the system at the gasification temperature of 900 °C, melting temperature of 1400 °C, and S/M of 1. The net H₂ yield is ～77.3 kg/t-MSW with a net cold gas efficiency of 49.1% under the same operating condition. An optimum operation condition for T (850–1000 °C) and S/M (0.6–1.0) is determined considering the balance between H₂ production ability and the auto-thermal energy balance.     

Process knowledge-based random forest regression for model predictive control on a nonlinear production process with multiple working conditions

In process industry, predictive control approaches have been widely used for nonlinear production processes. Practically, the predictor in a predictive controller is extremely important since it provides future states for the optimization problem of controllers. The conventional predictive controller with precise mathematical predictors approximating the state space of physical systems is difficult and time-consuming for nonlinear production processes, and it performs poorly over a wide range of working conditions and with significant disturbances. To address the challenges, the trend of applying artificial intelligence emerges. However, the industrial process-specific knowledge is ignored in most cases. In this study, a predictive controller with a control process knowledge-based random forest (RF) model is proposed. Specifically, working data are clustered at first to handle diverse working conditions. Then, a process knowledge-based forest predictor, namely MIW-RF model with a redesigned cascading RF structure, is proposed to incorporate control process knowledge into modeling. Thus, future states of controlled variables could be more accurately acquired for the optimizer. A simplified version of the predictive model is also developed with quick model training and updating. The proposed predictive methods are finally introduced into the controller design. According to the empirical results, the proposed methods deliver a better control performance against benchmarks, including more accurate anticipated controlled-variable responses, better set-point tracking and disturbance rejection capability.     

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.     

Process Intensification of Living Cationic Polymerization of Isobutylene by a Flow Reaction System

Increasing the reaction temperature of the living cationic polymerization of isobutylene is crucial for industrial production due to the cost of refrigeration. The reaction temperature increase was achieved with an accelerated reaction rate using a flow reaction system. The polymerization conditions, including the flow reactor design, were based on the results of kinetic studies. Utilizing a milli‐scale flow reactor, polyisobutylene, which has a narrow molecular weight distribution, was obtained within a considerably short residence time at a high temperature. Furthermore, it was confirmed that the value of M_w/M_n correlates with the product of the Reynolds number and the angle of collision.     

Numerical Simulation of Sterilization Processes for Shear‐Thinning Food in Taylor‐Couette Flow Systems

The performance of the Taylor‐Couette flow apparatus as a heat sterilizer is numerically investigated. The destruction of Clostridium botulinum and thiamine (vitamin B1) was selected as model reaction. When Taylor vortices were formed in the annular space, the heat transfer significantly enhanced as compared to the case without vortex flow. As a result, the equivalent lethality calculated from the temperature field increased, which is regarded as a quantum leap. Conversely, the improvement of heat transfer induced destruction of thiamine. These results suggest that there is a trade‐off relationship between the enhancement of heat transfer and the avoidance of thermal destruction of nutritional components. In conclusion, the Taylor‐Couette flow sterilizer has the potential for process intensification in heat sterilization processes.     

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.     

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.     

Stream-Based Data Sampling Mechanism for Process Object

Process object is the instance of process. Vertexes and edges are in the graph of process object. There are different types of the object itself and the associations between object. For the large-scale data, there are many changes reflected. Recently, how to find appropriate real-time data for process object becomes a hot research topic. Data sampling is a kind of finding c hanges o f p rocess o bjects. There i s r equirements f or s ampling to be adaptive to underlying distribution of data stream. In this paper, we have proposed a adaptive data sampling mechanism to find a ppropriate d ata t o m odeling. F irst o f all, we use concept drift to make the partition of the life cycle of process object. Then, entity community detection is proposed to find changes. Finally, we propose stream-based real-time optimization of data sampling. Contributions of this paper are concept drift, community detection, and stream-based real-time computing. Experiments show the effectiveness and feasibility of our proposed adaptive data sampling mechanism for process object.