Life cycle analysis and choice of natural gas-based automotive alternative fuels in Chongqing Municipality,China

Road transport produces significant amounts of emissions by using crude oil as the primary energy source. A reduction of emissions can be achieved by implementing alternative fuel chains. The objective of this study is to carry out an economic, environmental and energy (EEE) life cycle study on natural gas-based automotive fuels with conventional gasoline in an abundant region of China. A set of indices of four fuels/vehicle systems on the basis of life cycle are assessed in terms of impact of EEE, in which natural gas produces compressed natural gas (CNG), methanol, dimethylether (DME) and Fischer Tropsch diesel (FTD). The study included fuel production, vehicle production, vehicle operation, infrastructure and vehicle end of life as a system for each fuel/vehicle system. A generic gasoline fueled car is used as a baseline. Data have been reviewed and modified based on the best knowledge available to Chongqing local sources. Results indicated that when we could not change electric and hydrogen fuel cell vehicles into commercial vehicles on a large scale, direct use of CNG in a dedicated or bi-fuel vehicle is an economical choice for the region which is most energy efficient and more environmental friendly. The study can be used to support decisions on how natural gas resources can best be utilized as a fuel/energy resource for automobiles, and what issues need to be resolved in Chongqing. The models and approaches for this study can be applied to other regions of China as long as all the assumptions are well defined and modified to find a substitute automotive energy source and establish an energy policy in a specific region.     

降低气流粉碎过程中噪声强度的研究

主要报导了超音速气流超细粉碎过程中噪声产生的原因、影响因素及降低噪声强度所采取的技术原理途径与措施。研究结果表明 :采用CSQ消声装置完全可以将超音速气流粉碎过程中产生的噪声 130dB(A)降低到国家规定的标准 85dB(A)之下。该装置结构简单 ,既能消除噪声 ,又便于产品卸料 ,拆卸清理方便 ,综合性能良好。     

青藏高原生态资产地域划分中的SOFM网络技术

针对目前地域划分中存在的问题,论文尝试以人工神经网络技术作为区划工作的理论支撑,构建了自组织特征映射SOFM网络,以青藏高原环境与生态系统资产作为待分客体,探索了新技术和方法在生态资产地域划分中的应用。结果表明,对于自然界中广泛存在的非线性问题,SOFM网络具有比聚类分析等线性分类器更强的适应性。应用SOFM网络在对待分客体生态资产进行类型划分的基础上,使用策略性循环尺度转换(SCS)范式对其进行了区域转换,最终完成了青藏高原范围内生态资产的地域划分。     

干法烟气脱硫反应的实验研究

本文建立了干法烟气脱硫反应的实验装置 ,对以氧化钙为脱硫剂的干法烟气脱硫反应进行了实验研究 ,并利用压汞仪分析反应过程中参与反应的固体颗粒的孔隙结构的变化情况 ,对干法烟气脱硫反应过程中的固体颗粒孔隙结构特性进行动态分析 ,揭示和确定了反应过程中固体颗粒孔隙结构变化规律和脱硫转化率。研究结果表明 ,孔隙结构 ,包括孔径分布、最可几孔半径等是决定和影响干法烟气脱硫反应的因素 ,其中孔隙率决定脱硫反应的最终转化率 ,孔隙分布则控制着反应的过程特性。     

用神经网络辨识化学强化一级处理系统

运用实验室得到的数据为样本 ,选取了CODCr和絮凝剂投加量为指标 ,采用三层结构的神经网络 ,利用Matlab的神经网络工具箱中的批处理梯度下降法对CEPT系统经行辨识。辨识结果表明 :模型的预测值与实测值的平均误差在 3 .7%左右 ,具有较高的精度。同时 ,该模型还具有适应性强 ,使用方便 ,高效的特点 ,为CEPT系统的在线实时控制提供了一条有效的途径。     

用SPME测定珠江河口水体中的PCBs

报道了利用SPME技术结合电子捕获检测气相色谱 (ECD)测定珠江入海河口水体中PCBs有机污染物的含量 ,并讨论了SPME装置萃取PCBs的主要影响因素。结果表明 :在选取一定的萃取纤维头 (10 0 μmPDMS)后 ,影响萃取效果的主要因素是萃取时间的长短 ,其次为萃取的温度和pH值。在各个影响因素及不同水平处理中 ,以萃取时间 30min、萃取温度 35℃、pH值 7.2、搅拌速度 10 0 0r/min为最理想。在该种条件下测定了珠江入海四大口门虎门、横门、蕉门和斗门河口水体中PCBs含量分别为 2 .70 1、0 .999、2 .82 8和 1.16 1ng/L。     

无铅汽油尾气颗粒物组分及其毒性研究

为研究无铅汽油的降铅效果。及其排出尾气颗粒物的有机提取物组分对小鼠细胞免疫功能的影响，采用大流量采样仪采集无铅汽油尾气颗粒物，用石墨炉原子吸收光谱法检测了颗粒物的铅含量，并采用气相色谱和质谱方法分析了颗粒物有机提取物的组分，同时，用凝胶电泳方法比较无铅汽油颗粒物的有机提取物在加铅和不加铅的条件下，对小鼠巨噬细胞的凋亡影响，结果显示，无铅汽油尾气排出颗粒物上仍有微量的铅，约为0.11mg/g。颗粒物有机提取物中含有大量的有害物质，如多环芳烃，杂环化合物等，通过凝胶电泳法显示，无论是有铅还是无铅，颗粒物有机提取物都能引起小鼠腹腔巨噬细胞的凋亡，在有铅的情况下，对巨噬细胞的凋亡影响更为明显，通过研究可以认为，无铅汽油对减轻汽车尾气的铅污染具有积极作用。但颗粒物上仍吸附大量的有害物质，对机体的免疫功能仍具有危害。     

燃煤烟气脱硫技术分析

介绍了国内目前正在使用或即将使用的燃煤烟气脱硫技术的工艺特点、操作参数和适用范围。这些技术包括湿式石灰石(石灰)-石膏法、炉内喷钙后部烟气增湿脱硫法、海水洗涤法、除尘脱硫一体化技术等。     

石化企业烟气脱硫技术选择和产业化建议

结合石化企业自备电厂锅炉的运行特点，分析当前各种脱硫工艺技术的适应性，提出发展循环镁法脱硫技术的必要性和迫切性，并对石化脱硫产业的健康发展提出建议。     

Application of Bayesian regularized BP neural network model for analysis of aquatic ecological data-A case study of chlorophyll-a prediction in Nanzui water area of Dongting Lake

Bayesian regularized BP neural network（BRBPNN） technique was applied in the chlorophyll-α prediction of Nanzui water area in Dongting Lake. Through BP network interpolation method, the input and output samples of the network were obtained. After the selection of input variables using stepwise/multiple linear regression method in SPSS i1.0 software, the BRBPNN model was established between chlorophyll-α and environmental parameters, biological parameters. The achieved optimal network structure was 3-11-1 with the correlation coefficients and the mean square errors for the training set and the test set as 0.999 and 0.000？8426, 0.981 and 0.0216 respectively. The sum of square weights between each input neuron and the hidden layer of optimal BRBPNN models of different structures indicated that the effect of individual input parameter on chlorophyll- α declined in the order of alga amount 〉 secchi disc depth（SD） 〉 electrical conductivity （EC）. Additionally, it also demonstrated that the contributions of these three factors were the maximal for the change of chlorophyll-α concentration, total phosphorus（TP） and total nitrogen（TN） were the minimal. All the results showed that BRBPNN model was capable of automated regularization parameter selection and thus it may ensure the excellent generation ability and robustness. Thus, this study laid the foundation for the application of BRBPNN model in the analysis of aquatic ecological data（chlorophyll-α prediction） and the explanation about the effective eutrophication treatment measures for Nanzui water area in Dongting Lake.