Enhancement of pyrolysis gasoline hydrogenation over Zn- and Mo-promoted Ni/γ-Al2O3 catalysts

Alumina-supported nickel catalysts modified by Zn and/or Mo were prepared and characterized using several techniques (XRF, BET, XRD, XPS, and TPR). The effect of Zn and Mo on catalytic hydrogenation activity was evaluated in hydrogenation of pyrolysis gasoline (PyGas) using styrene conversion for monitoring the activity trend in a fixed-bed reactor under the following conditions: temperature of 70–120 °C, H₂:PyGas volume ratio of 200:1, pressure of 2.8 MPa, and volume space velocity of 3.0 h^− 1. The promoted Ni-based catalysts exhibited excellent catalytic activity for the hydrogenation of PyGas. The styrene conversion doubled after the introduction of Zn and Mo.     

谷物配餐食品研究

根据营养学理论对谷物的营养搭配进行了研究。通过谷物搭配，使各谷物氨基酸评分得到不同程度的提高。小麦粉与大豆粉以８０∶２０比例搭配后，其氨基酸评分由５３和３１提高到８２；小米与大豆以７６∶２４搭配后，其氨基酸评分由３４和３１提高到７１；玉米与大豆以８８∶１２搭配后，其氨基酸评分由５７和３１提高到７４；籼稻与大豆以９４∶６比例搭配后，其氨基酸评分由６９和３１提高至７８。同时根据营养的需要，对钙、铁、维生素等营养强化量进行了计算。为谷物配餐食品的研究提供了理论依据。     

Multi-fault classification based on support vector machine trained by chaos particle swarm optimization

A novel method of training support vector machine (SVM) by using chaos particle swarm optimization (CPSO) is proposed. A multi-fault classification model based on the SVM trained by CPSO is established and applied to the fault diagnosis of rotating machines. The results show that the method of training SVM using CPSO is feasible, the proposed fault classification model outperforms the neural network trained by chaos particle swarm optimization and least squares support vector machine, the precision and reliability of the fault classification results can meet the requirement of practical application.     

Prediction of silicon content in hot metal using support vector regression based on chaos particle swarm optimization

The prediction of silicon content in hot metal has been a major study subject as one of the most important means for the monitoring state in ferrous metallurgy industry. A prediction model of silicon content is established based on the support vector regression (SVR) whose optimal parameters are selected by chaos particle swarm optimization. The data of the model are collected from No. 3 BF in Panzhihua Iron and Steel Group Co. of China. The results show that the proposed prediction model has better prediction results than neural network trained by chaos particle swarm optimization and least squares support vector regression, the percentage of samples whose absolute prediction errors are less than 0.03 when predicting silicon content by the proposed model is higher than 90%, it indicates that the prediction precision can meet the requirement of practical production.     

某水电站坝址区地应力场分布特征研究

以现场实测地应力资料为依据,结合震源机制解方法,对某水电站右岸实测地应力场进行了分析,拟合了地应力场的数学表达式,并推演研究了工程区域地应力场的分布特征,结果表明：某水电站工程区域位于高山峡谷地带,经历了多次构造运动,地应力场主要受区域构造运动的影响,这种影响对水平方向的构造应力尤为剧烈;工程区域地应力场总体表现为竖直应力略小于自重应力,最大水平主应力以构造应力为主,其值约为20 MPa。     

隧道衬砌受力特性研究与结构安全性评价

采用等效截面法将隧道衬砌初期支护中的喷射混凝土和钢拱架等效为均质梁截面,基于有限元法,计算出衬砌结构的轴力、剪力和弯矩;根据承载力包络图法对隧道衬砌喷射混凝土、钢拱架以及二次衬砌的结构安全性进行评价。以某铁路隧道复合衬砌工程实例为研究背景,基于上述所提出的方法建立有限元数值计算模型,进行隧道衬砌受力特性研究与结构安全性评价。计算结果表明,二次衬砌混凝土强度等级不足是导致衬砌破坏的可能原因之一,计算所示的衬砌裂缝位置与实际工程一致,提高混凝土强度等级可有效控制复合衬砌结构的开裂。     

基于条件经验模式分解和串并行CNN的脑电信号识别

针对运动想象脑电信号(EEG)的非线性、非平稳特点,该文提出一种结合条件经验模式分解(CEMD)和串并行卷积神经网络(SPCNN)的脑电信号识别方法。在CEMD过程中,采用各阶固有模式分量(IMF)与原始信号的相关性系数作为第1个IMF筛选条件,在此基础上,提出各阶IMF之间的相对能量占有率作为第2个IMF筛选条件。此外,为了考虑脑电信号各个通道之间的特征和突出每个通道内的特征,该文提出SPCNN网络模型对进行CEMD过程后的脑电信号进行分类。实验结果表明,在自行采集的脑电数据集上平均识别率达到94.58%。在公开数据集BCI competition IV 2b上平均识别率达到82.13%,比卷积神经网络提高了3.85%。最后,在自行设计的智能轮椅脑电控制平台上进行了轮椅前进、左转和右转在线控制实验,验证了该文算法对脑电信号识别的有效性。     

降低油炸方便面含油量的研究

本文阐述了原料面粉的灰分、油炸时间及添加剂对油炸方便面含油量的影响,找出了降低油炸方便含油量的有效途径.     

低转速数控绕线机的研制

针对传统通用型数控绕线机在绕制细微丝材时易断丝、无法整齐排线等缺点,研制了一种新型低转速数控绕线机.阐述了其主要技术指标、线圈工艺要求、设备结构组成、复合排线原理和设计思想,确保研制出来的机器适用于微细丝材.实践证明,用它绕制的产品张力稳定、排线整齐,取得了理想的使用效果.     

高压脉冲介质阻挡放电降解土壤中芘的研究

针对我国土壤多环芳烃的污染问题,通过自研的介质阻挡脉冲反应器处理土壤中的芘,研究了放电电压、放电频率、土壤湿度、土壤厚度、空气流速和土壤pH等条件对降解效果的影响。结果表明,随着放电电压和放电频率增加,芘的降解率提高,空气流速和土壤湿度在适宜的范围内,有利于芘的降解,增加土壤厚度和降低pH会降低芘的降解率。最适降解条件为：放电电压12.6 kV、放电频率1.0 kHz、土壤湿度3.0%、土壤厚度1 mm、空气流速2 L/min、土壤pH≥7,芘的降解率为97.04%。通过HPLC-MS和FT-IR分析,间接证明放电产生的活性物质O₃、·OH、NO _x 等导致了芘的降解,提出了芘可能的降解途径。