Synergistic interaction of 2-amino 4-methyl benzothiazole (AMBT) and benzotriazole (BTZ) offers excellent protection to mild steel exposed in acid atmosphere at elevated temperatures: Electrochemical,computational and surface studies

The synergistic interactions and corrosion protection properties of 2-amino 4-methyl benzothiazole, (AMBT) and 1, 2, 3-benzotriazole (BTZ) have been studied for mild steel in HCl at elevated temperatures. The extent of synergistic interaction increases with temperature. The methods of study include the conventional weight loss studies, computational screening, surface characterization and electrochemical studies. Quantum chemical approach was used to calculate some electronic properties of the molecules and to ascertain the synergistic interaction, inhibitive effect and molecular structures. The corrosion inhibition efficiencies and the global chemical reactivity relate to parameters like total energy, E_HOMO, E_LUMO and gap energy (ΔE). Condensed atom Fukui functions also calculated using DFT at B3LYP/6-31G* level, and were found to be correlating with the experimental results.     

An improved cuckoo search algorithm for the problem of electric distribution network reconfiguration

Power loss reduction has an important role in operating electric distribution network system. There are a lot of methods for reduction power loss such as capacitor placement, distributed generation placement and electric distribution network reconfiguration (EDNR). Among these methods, the EDNR is an efficient technique to reduce power loss due to without taking any costs. However, the EDNR problem is a nonlinear, discrete problem and lots of extreme points. Therefore, it is necessary to have efficient methods for solving the EDNR problem. In this paper, an improved cuckoo search algorithm (ICSA) is proposed for solving the EDNR problem. In which, based on disadvantages of exploration and exploitation process of cuckoo search algorithm (CSA) for solving the EDNR problem, a local search mechanism is added to exploit candidate solutions existing around the current best solution. The calculated results on the simple distribution networks to complex distribution networks show that ICSA has ability for finding the global optimal solution with much smaller iterations and better quality of obtained solution compared with CSA and some other improved versions of CSA. The performance comparisons with other existing methods available in previous studies and the software of Power System Simulator/Advanced Distribution Engineering Productivity Tool (PSS/ADEPT) also lead to the better electric distribution network configuration with smaller total power losses. As a result, ICSA is a potential and reliable method for solving the EDNR problems.     

Digital hysteresis sliding mode control for interleaved DC–DC converters

This paper presents a digital constant frequency sliding mode control (SMC) law for interleaved DC–DC converters. Constant switching frequency and interleaving are achieved by dynamically adjusting the hysteresis of the control signals generating comparators. The interleaving method neither imposes constraints on the number of required phases to obtain a specific output voltage nor uses quasi-SMC. Hence, the control scheme achieves high flexibility, robustness, and performance. Furthermore, a sliding mode observer (SMO) for reconstructing the inductor currents is proposed. Experimental results for a two-phase buck converter are reported. The control concept accomplishes an improved dynamic performance in comparison with quasi-SMC.     

CO_2 conversion by thermal plasma with carbon as reducing agent: high CO yield and energy efficiency

A key problem in CO_2 conversion by thermal plasma is suppressing the inverse reactions,CO?+?O?→?CO_2 and CO?+?0.5O_2?→?CO_2, to simultaneously obtain high CO yield and energy efficiency. This can be done by quickly quenching the decomposed gas or rapidly taking away free oxygen from decomposed gas. In this paper, experiments of CO_2 conversion by thermal plasma with carbon as a reducing agent are presented. Carbon quickly devoured free oxygen in thermal plasma decomposed gas, and not only is the inverse reaction completely suppressed, but the discharge energy to form oxygen atoms, oxygen molecular, and thermal energy is also reused.A CO_2 conversion rate of 67%–94% and the corresponding electric energy efficiency of about 70% are achieved, both are much higher than that seen so far by other plasma implementations.     

自然循环工况下离心泵阻力特性的实验研究与数值模拟

停转后泵的阻力特性对自然循环流量影响明显。为研究低流量自然循环工况下离心泵的阻力特性，设计了实验台架，对离心泵的正向压降、反向压降和损失系数进行了测量，实验雷诺数为2.0×10⁴～1.5×10⁵。实验表明：相同雷诺数下，反向压降明显高于正向压降；雷诺数大于8×10⁴时，损失系数基本保持不变，而低雷诺数下损失系数随雷诺数的降低有增大的趋势；基于实验得到了损失系数的经验关系式。采用CFD方法对离心泵的内部流场进行了模拟，计算表明：CFD方法能较好地预测损失系数，RNG k-ε模型与实验值的相对误差不超过10%；离心泵的压力损失主要集中在叶轮、导叶等结构的交界区域；正向与反向流动的流场分布存在显著区别；低雷诺数下局部流动更加不稳定。     

基于多尺度双线性卷积神经网络的多角度下车型精细识别

针对多角度下车辆出现一定的尺度变化和形变导致很难被准确识别的问题，提出基于多尺度双线性卷积神经网络（MS-B-CNN）的车型精细识别模型。首先，对双线性卷积神经网络（B-CNN）算法进行改进，提出MS-B-CNN算法对不同卷积层的特征进行了多尺度融合，以提高特征表达能力；此外，还采用基于中心损失函数与Softmax损失函数联合学习的策略，在Softmax损失函数基础上分别对训练集每个类别在特征空间维护一个类中心，在训练过程中新增加样本时，网络会约束样本的分类中心距离，以提高多角度情况下的车型识别的能力。实验结果显示，该车型识别模型在CompCars数据集上的正确率达到了93.63%，验证了模型在多角度情况下的准确性和鲁棒性。     

基于神经网络算法的井下裂缝诊断与堵漏技术

在钻井过程中，常常钻遇不同宽度的井下地层裂缝。钻遇裂缝时容易发生钻井液漏失现象，甚至发生钻井液失返现象，严重影响了安全、高效钻井。目前裂缝封堵的方法常存在封堵成功率不高、堵漏承压能力低的问题，其中一个重要的原因是对井下地层的裂缝宽度等特征认识不清。基于地层裂缝产生的岩石力学机理，确定影响裂缝宽度关键的6个力学和工程因素，并利用神经网络计算的非线性、大数据特点建立了井下地层裂缝宽度的分析模型，模型包含输入层、输出层和3个隐藏层。通过该模型诊断井下裂缝宽度，提高了计算精度，平均误差仅为2.09%，最大误差为5.88%，解决钻井现场仅凭经验判断裂缝误差较大和依靠成像测井成本较高的问题。同时根据神经网络模型诊断得到的裂缝宽度优化堵漏材料的粒径配比，提高了裂缝内的架桥封堵强度和架桥的稳定性，封堵层的承压能力达到12.8 MPa，反向承压能力达到4.5 MPa。现场堵漏试验最高憋压10 MPa，经过封堵作业后大排量循环不漏，达到了裂缝性地层高效堵漏的目的，堵漏一次成功。      

原位固氮剂在污泥堆肥过程中保氮机制

高温好氧堆肥技术虽然可有效处理有机固体废物，但常常会释放大量的氨气和造成氮素损失。本文以硫酸镁、磷酸二氢钾、硫酸镁+磷酸二氢钾（分别记为MgSO₄、KP和KPM处理组）为原位固氮剂，污泥和木屑为原料，进行高温好氧堆肥试验，研究以上原位固氮剂对污泥堆肥过程中的氨气挥发和氮素损失的影响。结果表明，硫酸镁（MgSO₄）对物料pH有显著的降低效应，添加硫酸镁使堆体高温期pH降低至8.17，低于硫酸镁+磷酸二氢钾（KPM）组（8.55）和对照（CK）组（8.90）。与CK相比，MgSO₄和KPM的NH₃累积释放量分别降低了34%和28%，反而KP组增加了35%。对比不同条件下XRD图谱可知，MgSO₄的固氮效应并不是由于MgNH₄PO₄·6H₂O的生成，而是由于其对pH的降低效应造成的。堆肥结束后，除了KP组，各处理条件下的种子发芽率值（germination index，GI）均不会对后续使用带来不利影响。     

Microwave-assisted metal-catalyzed pyrolysis of low-rank coal: Promising option towards obtaining high-quality products

Microwave-assisted catalytic pyrolysis is considered to be a promising technology for coal-staged conversion due to its high efficiency and selectivity. This work was undertaken to investigate the pyrolysis behavior and products quality of microwave-assisted pyrolysis of low rank coal catalyzed by metallic catalysts (K, Ca and Fe) with both dielectric response and catalytic effect via a microwave tube furnace. The mechanism of metallic catalysts on catalytic cracking tar under microwave radiation was also investigated. The dielectric properties and physicochemical structure of coal chars were characterized by a vector network analyzer, XRD, FT-IR, SEM, EDS, and Raman. The chemical structure characteristics of generated tars were determined by FT-IR and GC-MS. Results manifested that microwave interacted preferentially with metal catalysts by polarization and conductivity loss could efficiently induce the occurrence of catalytic pyrolysis reactions to generate high yield syngas (CO + H₂). Specifically, the dielectric loss factor of resultant chars was considerably improved with the introduction of metallic catalysts especial for Ca and Fe. Furthermore, it is found that metal catalysts dramatically enriched the amorphous carbon structure in produced chars whereas in favour of suppressing the trend of carbon graphitization. Additionally, the transformation of larger polycyclic aromatic compounds into lighter tar species was catalytically accelerated, resulting in the large proportion of single-ring aromatics in tar under the synergistic effect between microwave and metal catalysts.