Effect of O2 and temperature on the catalytic performance of Ni/Al2O3 and Ni/MgAl2O4 for the dry reforming of methane (DRM)

Al₂O₃ and MgAl₂O₄ supported 10% (w/w) Ni catalysts having a dispersion of 1.5 and 2.0% are active for DRM at 600 and 750 °C. High temperature reduction of both the calcined catalysts resulted in metallic Ni being formed, suggesting strong support metal interactions. The CH₄ and CO₂ conversion during DRM are relatively constant with time-on-stream, and are higher for Ni/MgAl₂O₄ than Ni/Al₂O₃. Carbon-whiskers are also detected on both catalysts. O₂ co-feed of 2.6% (v/v) and increasing reaction temperature to 750 °C helped in decreasing the amount of carbon deposited, except for Ni/MgAl₂O₄ at 600 °C. Furthermore, higher conversions and H₂/CO ratios are achieved. It appears that on spent Ni/MgAl₂O₄ a different type of carbon species was formed, and this carbon species was difficult to remove by oxygen at 600 °C. Thus, co-feeding O₂, using an appropriate temperature, and choosing a suitable support can reduce the carbon present on the nickel catalysts during DRM.     

In-line characterization of dispersion uniformity evolution during a twin-screw blending extrusion based on near-infrared spectroscopy

Properties of an immiscible polymer blend have been proved to be closely related to dispersion uniformity of the minor phase. At present, dispersion uniformity is difficult to evaluate during the blending process, resulting in hysteretic feedback. Aiming at this problem, this work utilized near-infrared (NIR) spectroscopy to in-line characterize dispersion uniformity evolution during a twin-screw extrusion. A multichannel NIR measurement system was set up and applied to evaluate the blending process of polypropylene and polyolefin elastomer (POE). Based on the NIR spectra collected at different positions of the extruder, five prediction models of POE content were established using the light gradient boosting machine algorithm. Dispersion uniformity was characterized through the fluctuation of the predicted content. The evolution of dispersion under such processing parameters was consistent with scanning electron microscopy.     

基于SARIMA-GRNN-SVM的短期商业电力负荷组合预测方法

针对短期商业电力负荷预测准确性与周期难以满足现有电力现货市场的问题,提出了一种基于SARIMAGRNN-SVM(seasonal autoregressive integrated moving average-generalized regression neural network-support vector machine)的商业电力负荷组合预测模型。首先,对商业电力负荷变化的周期规律与随机因素的复杂影响进行了分析;然后,结合以上分析,选用SARIMA和GRNN为单一预测模型对商业电力负荷进行预测,并利用SVM进行组合,实现日前商业电力负荷预测;最后,通过某商业综合体的电力负荷数据进行验证。所提组合预测模型较单一预测模型拥有更优的预测精度与鲁棒性,可以为短期商业电力负荷预测提供借鉴。     

Design and analysis of a class of redundant collaborative manipulators with 2D large rotational angles

The parallel spindle heads with high rotational capability are demanded in the area of multi-axis machine tools and 3D printers. This paper focuses on designing a class of 2R1T (R: Rotation; T: Translation) parallel spindle heads and the corresponding collaborative 5-axis manipulators with 2-dimension (2D) large rotational angles. In order to construct 2D rotational degrees of freedom (DOFs), a platform with 2D revolute joints is proposed first. Based on the constraint screw theory, the feasible limbs that can be connected in the platform are synthesized. In order to provide constant rotational axis for the platform, a class of redundant limbs are designed. A class of redundant 2R1T parallel spindle heads is obtained by connecting the redundant limbs with the platform and the redundant characteristics are verified by the modified Grübler-Kutzbach criterion. The corresponding 5-axis collaborative manipulators are presented by constructing a 2-DOF series translational bottom moving platform. The inverse kinematics and the orientation workspace as well as the decoupling characteristics of this type of 2R1T parallel spindle heads are analyzed. The results show that these manipulators have large 2D rotational angles than the traditional A3/Z3 heads and can be potentially used in the application of multi-axis machine tools and the 3D printers.     

全电动注塑机研究进展及在汽配电子行业中的应用

介绍了全电动注塑机的特色，综述了国内外全电动注塑机在精密注塑、智能注塑、高效注塑和绿色注塑方面的研究进展，并对全电动注塑机在汽配电子行业中的应用作了简单介绍。对双电机同步控制技术的逐渐成熟为全电动注塑机实现大型化提供了助力，全电动注塑机高效、精密、智能、节能、环保的优势在汽配电子行业显示了很好的应用前景。     

COB LED自动在线点胶测试补粉机的研制

在COB(Chip On Board)LED光组件制造领域,点胶、测试和补粉关键工艺基本以手工或半自动为主。为了保证COB LED产品质量一致性、提高生产效率,研制了一种COB自动在线点胶测试及补粉一体机,实现点胶、测试、补粉生产工序的自动化生产。阐述了其工艺流程及设计总体方案,提出了荧光粉量修补方法,修正初次点胶量,实现动态调节,减少人工干预。经实验验证,该一体机可以提高点胶工艺的自动化程度,具有运行速度快、可靠性好、性能稳定等特点。     

Multilayer thermal object identification in frequency domain using IR thermography and vector fitting

This paper deals with the identification of the thermal parameters of multilayer objects using the concept of thermal impedance. In order to perform such identification, temperature evolution in time is obtained by an infrared camera after power excitation is applied in the investigated structure. Infrared thermography offers the advantage of being a noncontact temperature detection and measurement method. In many practical cases, it is impossible to use contact temperature measurements. Typically, the power in the form of a step function is applied. In order to calculate the thermal impedance of an object, temperature and power are converted into the frequency domain using the Laplace transform for s = jω. Then, the poles of the thermal impedance are identified using vector fitting, which allows calculating the thermal impedance as a sum of partial fractions. This corresponds directly to the Foster network of a thermal object. In addition, the vector fitting method offers much better convergence in comparison with other methods using the polynomial rational approximation of thermal impedance. A considerable improvement of the numerical Laplace transform in high frequency range is proposed. In this approach, the variable s = jω is replaced by , and then, the integration result is corrected by the Taylor series. It leads to a kind of filtering of the temperature signal.     

Calculation of the Characteristics of Salient‐Pole Synchronous Machines Assisted by Permanent Magnets on the Basis of the Operating Principle

In this study we investigate a method for accurately calculating the characteristics of salient‐pole synchronous machines assisted by permanent magnets. First, the operating principle of the machines is investigated by using both finite element analysis and a simple magnetic circuit. Then, a theoretical representation of the assist effect on the permanent magnets is derived based on the magnetic circuit. Finally, the measured and calculated results are compared in order to confirm the validity of the proposed calculation method. We show that the load characteristics of the proposed machine can be accurately estimated from the no‐load and short‐circuit characteristics of the conventional machine without permanent magnets, and the size and magnetization of the inserted permanent magnets.     

Effects of Winding Attachment Positions on Output Characteristics of Flux‐Modulating Synchronous Machines

The flux‐modulating synchronous machine (FMSM) is a new type of multipole SM with nonoverlapping concentrated armature and field windings on the stator. This paper compares the output characteristics of two FMSMs through finite element analysis (FEA) and experiments. In both of the FMSMs, the attachment positions of the armature and field windings are swapped. To determine the reason for the discrepancies in their output characteristics, unsaturated inductances were calculated using a d‐‐q equivalent circuit. In addition, the calculated results of the inductances were confirmed through a visualization of the leakage fluxes using FEA. The results of the study show that the synchronous inductance can be reduced by attaching the armature winding to the air‐gap side of the stator teeth and that the reduction leads to an increase in output power.     

Modeling and optimization of biodiesel engine performance using kernel-based extreme learning machine and cuckoo search

This study presents the optimization of biodiesel engine performance that can achieve the goal of fewer emissions, low fuel cost and wide engine operating range. A new biodiesel engine modeling and optimization framework based on extreme learning machine (ELM) is proposed. As an accurate model is required for effective optimization result, kernel-based ELM (K-ELM) is used instead of basic ELM because K-ELM can provide better generalization performance, and the randomness of basic ELM does not occur in K-ELM. By using K-ELM, a biodiesel engine model is first created based on experimental data. Logarithmic transformation of dependent variables is used to alleviate the problems of data scarcity and data exponentiality simultaneously. With the K-ELM engine model, cuckoo search (CS) is then employed to determine the optimal biodiesel ratio. A flexible objective function is designed so that various user-defined constraints can be applied. As an illustrative study, the fuel price in Macau is used to perform the optimization. To verify the modeling and optimization framework, the K-ELM model is compared with a least-squares support vector machine (LS-SVM) model, and the CS optimization result is compared with particle swarm optimization and experimental results. The evaluation result shows that K-ELM can achieve comparable performance to LS-SVM, resulting in a reliable prediction result for optimization. It also shows that the optimization results based on CS is effective.