Study on the Physical and Electrical Properties of Bi2 − δZnδSr2Ca2Cu3O10 + y Glass-Ceramic Superconductors

We have fabricated a series of glass-ceramic (Bi_{2 ? δ}Zn_δ) Sr₂Ca₂Cu₃O_{10 + y} , where δ = 0.0, 0.2, 0.4, 0.6, 0.8, and 1.0, and investigated the effect of Zn ions on the glass formation, crystallization, thermal, electrical, and on the magnetic properties of the BSCCO-2223 superconductor system. The structural symmetry was found to be tetragonal in all the substitution levels. The best electrical performance was obtained from the δ = 0 sample, the T _c and T _zero was obtained at 110 K and 107 K, respectively. The J _c values of the samples were determined using the magnetization hysteresis and Bean's model. The crystallization kinetics were investigated using nonisothermal models of Augis–Bennett. The calculated activation energy, E _a, of the system was found to be in the range of 258–336 kJ/mol.     

Relationship Between Growth Speed and Magnetic Properties in Bi-2212/Ag Textured Composites

In this paper, the measured magnetic properties of Bi₂Sr₂CaCu₂O _x /3 wt.% Ag textured composite materials prepared by a LFZ melting technique at different growth speeds (5, 15, 30, and 60 mm/h) are presented. X-ray diffraction patterns have shown that Bi-2212 phase is the major one in all cases. The magnetization measurements have been carried out as a function of the magnetic field up to 10 kOe. J _c values, calculated using Bean’s model, indicate that the growth speeds have a significant effect on J _c . It has been found that the maximum critical current density, 4.42×10⁵ A/cm² at 10 K, has been obtained for the 15 mm/h grown sample.     

Nanoindentation Properties of HT c Superconducting Single-Crystalline Whiskers

In this work, superconducting and mechanical properties of BSCCO superconducting whiskers were studied in detail. Nanoindentation properties of the whiskers were analyzed by the Oliver and Pharr Method and Work of Indentation (WI) method. More accurate results using the WI method for both the hardness and the reduced modulus were obtained due to reduction of the pile-up error. The correct hardness and reduced modulus values were determined by considering the indentation size effect and the substrate (epoxy resin) effect. The modified composite hardness model and the composite reduced modulus model are the most suitable models to investigate the nanoindentation properties of the whiskers.     

传导性EMI噪声的模态分离与噪声抑制问题探讨

对传导性电磁干扰(EMI)噪声的模态分离包括基于硬件器件和基于算法的软分离方法及其优缺点进行了分析和比较，同时给出了抑制噪声的功率线滤波器设计方法，并讨论了传导性电磁干扰噪声全智能诊断和处理装置．给出了部分实验结果，对其中一些方法作了实验对比．结果表明，基于分相器／混和器的噪声分离网络具有更好的高频特性，且网络插损和噪声模态抑制特性能够满足工程精度要求，但网络制造成本较高．     

Structure of domestic hot water consumption

We analyse the time and volume distributions for hot water consumption using data acquired from seven New Zealand households. This shows that there is a high proportion of low volume draws, 50 per cent yielding less than 2–31, depending on the household. In addition draws tend to be separated by relatively short intervals, 50 per cent following the previous draw by not more than 10–20 min. By use of a dynamic model of the hot water piping we estimate that thermal losses in the piping exceed 20 per cent of the heat energy supplied by the hot water cylinder for average households. We show that the use of pipe insulation may reduce these losses by 5–7 per cent.     

Structural and Physical Properties of Nd Substituted Bismuth Cuprates Bi1.7 Pb0.3−x Nd x Sr2Ca3Cu4O12+y

BiPb-2234 bulk samples with nominal composition of the compound Bi_1.7Pb_0.3−x Nd _x Sr₂Ca₃Cu₄O_12+y (BSCCO) (0.025≤x≤0.10) have been prepared by the melt-quenching method. The effects of Nd substitution on the BSCCO system have been investigated by electrical resistance (R–T), scanning electron microscopy (SEM), X-ray diffraction (XRD) and magnetic hysteresis measurements. It has been the BSCCO (2212) low-T _c phase is formed for all the substitution levels, together with the BSCCO (2223) high-T _c phase. The results obtained suggest that with increasing Nd³⁺ doping for Pb²⁺ the (2223) phase existing in undoped BSCCO gradually transforms into the (2212) phase and hence all of the samples have a mixed phase formation. The R–T result of the samples show two-step resistance transition; first transition occurs at 100 K and second in an interval of 80–90 K, depending on the Nd concentration. We have found that the magnetization decreases with increasing temperature in agreement with the general characteristic of the high-T _c materials. The samples exhibit weak field dependence particularly after 2 T and changes on the magnetic hysteresis, M–H curve rather small compared to the conventional superconducting materials. The maximum critical current density, J _c, value was calculated to be 8.51×10⁵ at 4.2 K and J _c decreases with increasing temperature and the substitution level.      

Improvement of High T c Phase Formation in BPSCCO Superconductor by Adding Vanadium and Substituting Titanium

We have produced the (BiPb)₂V _x Sr₂Ca₃Cu_4?y Ti _y O_12+?? with x=0.1 and y=0.050,0.10,0.2 and 0.3 compounds by melt-quenching method. Structural and superconducting properties of the produced samples were investigated by Scanning Electron Microscopy, X-ray diffraction patterns, electrical resistance measurements and dc-magnetic hysteresis loop measurements. The pure high-T _c phase (2223) is nearly found with Ti substitution for x=0.05 and 0.10. The onset critical temperature (T _c._onset) of the samples increases up to 111 K with doping up to x=0.20. In addition, considerable large values of the critical current densities (J _c), calculated from the hysteresis loop measurements by using Bean??s critical state model are obtained for the samples in the same doping range. Our data have indicated that J _c decreases with increasing temperature and Ti concentration.     

传导性电磁兼容关键技术研究

随着各种电子产品的广泛问世,大量的传导性电磁兼容问题不断出现且尚未得到很好地解决．针对传导电磁兼容中的一些关键技术进行了系统研究,包括通过对传导EMI噪声分离技术对比分析,进行了EMI噪声诊断实验与仿真研究,并提供了噪声抑制与滤波器设计方案;为得到有效的噪声抑制效果,介绍采用双电流探头和希尔伯特变换获取噪声源内阻抗信息;并提出了基于技术与经济一体化的传导电磁兼容决策阐述建模,还给出了EMI噪声抑制及噪声源内阻抗测定的相关实例,以验证方法的有效性．     

Production Scheduling Using Deep Reinforcement Learning and Discrete Event Simulation

Scheduling in the process industry is a highly demanding task. Having access to optimal production schedules at short notice, for instance, after spontaneous changes, offers numerous advantages in terms of robustness, economics, and ultimately customer satisfaction, as delays are minimized. In this work, we describe our initial efforts to apply and evaluate deep reinforcement learning (DRL) for optimized scheduling in a typical fill-and-finish batch production plant in the chemical industry. Our pilot study demonstrates how DRL can be implemented using an approach based on discrete event simulation. We discuss the results and benefits of DRL, compare it to mathematical programming approaches, and outline a potential path forward. Our study suggests that the application of DRL in the chemical industry is a promising research direction and that DRL can complement established methods such as process simulation and mathematical programming.     

Control of an Industrial Distillation Column Using a Hybrid Model with Adaptation of the Range of Validity and an ANN-based Soft Sensor

Advanced control schemes such as model predictive control can be used to minimize the use of resources while guaranteeing the specified product quality. In this paper, we consider an industrial mother liquor distillation column varying flow rate and composition of the feed. There are specifications of the composition for all product streams. To address this challenging control problem, we employ a nonlinear model-predictive controller using a hybrid model, which consists of a simple phenomenological model augmented by a data-based component to compensate the plant-model mismatch. The trustworthiness of the data-based model is addressed using a domain of validity of the data-based model, which is estimated using a one-class support vector machine. During operation, it may turn out that the model is also reliable in a wider range, therefore, data of recently visited operating points is recorded and the domain of validity is extended if the model is sufficiently accurate. To improve the performance of the controller, an artificial neural network model is used to estimate the product composition from available measurements.