2008年度全国表面活性剂原料及产品产销统计分析

分析了烷基苯、脂肪醇、脂肪酸、脂肪胺以及甘油等表面活性剂原料的产销情况和进出口情况,对阴离子表面活性剂、非离子表面活性剂、阳离子表面活性剂和两性离子表面活性剂产品统计情况以及进出口情况进行了剖析。分析了表面活性剂原料产销情况和表面活性剂产品统计数据呈现的特点。预测了2009年表面活性剂行业的基本情况。     

环境中阴离子表面活性剂检测方法研究进展

概述了阴离子表面活性剂给水环境带来的危害,深入分析了目前阴离子表面活性剂的主要测定方法以及各优缺点,并对今后检测方法的改进方向进行了介绍,指出联用技术将成为未来水环境中阴离子表面活性剂检测的主要技术。     

一种全氟辛基两性磷酸酯氟碳表面活性剂的复配研究

对一种全氟辛基两性磷酸酯氟碳表面活性剂协同作用进行了研究,考察了该表面活性剂与无机盐、阴离子碳氢表面活性剂、阴离子氟碳表面活性剂和非离子氟碳表面活性剂的复配性能,并对结果进行了讨论。研究表明:该两性磷酸酯氟碳表面活性剂自身表面张力为24.0 mN/m;电解质氯化钠对该两性磷酸酯氟碳表面活性剂影响显著,可使表面张力下降到22.4 mN/m;阴离子碳氢表面活性剂十二烷基硫酸钠(SDS)可使表面张力降至21.4 mN/m;阴离子氟碳表面活性剂全氟丁基磺酸钾和四乙基全氟辛基磺酸铵分别使表面张力降至20.9 mN/m和20.2 mN/m;而非离子氟碳表面活性剂N-乙基-N-聚氧乙烯(9)醚-全氟辛基磺酰胺能使表面张力降至20.9 mN/m。     

表面活性剂在稠油乳化降黏中的应用

概述了国内外稠油乳化降黏用表面活性剂的研究进展,详细介绍了阴离子表面活性剂、非离子表面活性剂、阴离子一非离子两性表面活性剂及高分子表面活性剂、生物表面活性剂和氟碳表面活性剂等新型表面活性剂在稠油乳化降黏中的应用,评价了各种表面活性剂的优缺点,并提出了稠油乳化降黏用表面活性剂的发展前景。     

磺酸盐阴离子表面活性剂粘度研究

磺酸盐阴离子表面活性剂具有原料来源广、合成成本低、粘弹性能好等优点,是油田广泛使用的表面活性剂。通过分析磺酸盐阴离子表面活性剂的影响因素,研究了无机盐、浓度、温度、助剂对于该表面活性剂的影响。利用实验分析了质量分数为4%磺酸盐阴离子表面活性剂DS10-2-10溶液在不同温度下的粘度、不同浓度下表面活性剂的粘度、加入1%和4%的NaCl时表面活性剂粘度的随着温度的变化和加入不同助剂,表面活性剂的粘度变化。通过研究不同因数对磺酸盐阴离子表面活性剂粘度的影响,为矿场中使用该表面活性剂提供实验指导。     

采油用阴离子双子表面活性剂的研究进展

阴离子双子表面活性剂由于独特的物化性质,使其在三次采油中的应用具有一定的可行性和巨大的应用潜力,越来越受到重视。综述了国内外采油用阴离子表面活性剂的合成研究现状。主要介绍了羧酸盐型、硫酸盐型和磺酸盐型三类阴离子双子表面活性剂的合成及应用研究,并探讨了阴离子双子表面活性剂的应用发展前景。     

水中阴离子表面活性剂生物降解的研究进展

随着日常和工业中对阴离子表面活性剂需求的日益增加,阴离子表面活性剂对水体的污染也越来越严重,寻求有效降解途径迫在眉睫。本文对各种阴离子表面活性剂生物降解法进行了综述。     

表面活性剂复配在三次采油中的应用进展

在三次采油技术中应用最广泛的是阴离子型和非离子型表面活性剂。阴离子表面活性剂界面活性高,吸附量小,但其耐盐性能较差;非离子表面活性剂耐盐性好但不耐温;将不同的表面活性剂进行混合复配,所得到的混合物存在着协同效应,可改善单一的表面活性剂性能。本文介绍了阴离子表面活性剂与阴离子、非离子表面活性剂以及新型表面活性剂复配的应用进展,讨论了表面活性剂复配协同机理的研究进展。     

联吡啶双子表面活性剂的性质及构效关系

测定新型双子表面活性剂联吡啶溴代烷(BP系列)的表面活性,以及与阴离子表面活性剂SDS的复配性能。对实验数据进行方差分析,结合表面活性剂相关理论,探讨数据差异性来源,并提出分子结构设计预测,表明此类化合物若需调节联结基长度来显著改变表面活性,则长度变化不应小于0.4 nm。     

阴离子表面活性剂(AS)的测定方法研究

阴离子表面活性剂(AS)能改变两相间的表面张力,具有润湿、起泡、洗涤的作用。阴离子表面活性剂(AS)随着生活废水排放到环境中会导致水质恶化,进而对水生生物和环境造成危害,阴离子表面活性剂已成为水体污染的监测指标之一。笔者介绍了近年来阴离子表面活性剂的测定方法,如共振光散射法、流动注射分析法、色谱法、紫外分光光度法、可见分光光度法、荧光光度法和联用法,并对今后阴离子表面活性剂检测方法的研究方向提出了建议。     

基于优化神经网络的高拱坝力学参数反演

高拱坝力学性能参数变化规律复杂,使用人工智能算法进行预测已经成为反演参数的重要手段。使用遗传算法对神经网络进行优化来检验优化后算法的性能,并比较不同算法应用于参数反演中预测结果的精度。根据某高拱坝运行期变形监测数据,分别使用RBF神经网络和遗传算法优化的BP(GA-BP)神经网络对不同水位工况下的坝段分区混凝土弹性模量进行反演。基于反演结果进行有限元正分析计算,将所得结果与实测数据进行对比,检验反演精度和效率。结果表明:GA-BP网络的最大预测误差为1.8%,相比于RBF网络预测精度提高了约50%。使用神经网络进行拱坝力学参数反演实用性好,优化后的神经网络比传统BP神经网络在计算精度和效率两方面均有明显改进,且GA-BP神经网络反演比RBF神经网络反演精度更高。     

基于RBF网络模型气辅成型工艺参数的优化

针对气辅成型过程中多个工艺参数的优化配置问题,提出了气辅成型工艺参数径向基函数(RBF)网络预测模型。该模型基于均匀设计思想,以RBF网络为基础,用正交最小二乘训练法对基函数的中心、方差和RBF网络权值进行优化,提高了网络预测模型对气辅成型制品质量指标的预测可靠性。以挂式空调前面板为例,对其进行CAE仿真分析,结果表明,该优化模型可实现对制品质量指标的快速预测,为工艺参数进一步优化奠定了基础。     

粒子群优化神经网络的电镀金刚石套钻使用寿命预测

电镀金刚石套钻广泛应用各种加工领域,而套钻使用寿命受到多种因素影响,具有一定的非线性变化特点,为了准确预测电镀金刚石套钻的使用寿命,提出了基于粒子群优化神经网络的电镀金刚石套钻使用寿命预测模型。对电镀金刚石套钻使用寿命预测现状进行分析,针对BP神经网络参数优化问题,采用粒子群优化算法确定最优参数,建立电镀金刚石套钻使用寿命的预测模型,通过仿真实验对其有效性和优越性进行分析。实验结果表明,模型可以准确刻画影响参数与电镀金刚石使用寿命之间的变化关系,获得比对比模型更高的预测精度,实际应用价值更高。     

Melt index prediction by RBF neural network with an ICO‐VSA hybrid optimization algorithm

Melt index (MI) is considered as one of the most significant parameter to determine the quality and the grade of the practical polypropylene polymerization products. A novel ICO‐VSA‐RNN (RBF neural network with ICO‐VSA algorithm) MI prediction model is proposed based on radial basis function (RBF) neural network and improved chaos optimization (ICO), and variable‐scale analysis (VSA), where the ICO is first added and then combined with the VSA to overcome the defects of ICO and VSA, then the parameters of the RBF neural network are optimized with them. At last, the RBF neural network model for MI prediction model is developed. Further researches on the optimal RBF neural network model of MI prediction are carried out with the data from a real industrial plant, and the prediction results show that the performance of this prediction model is much better than the RBF neural network model without optimization. © 2012 Wiley Periodicals, Inc. J Appl Polym Sci, 2012     

基于小波神经网络的循环冷却水腐蚀预测研究

利用某石化企业40个月的循环冷却水实际生产数据,基于小神经网络进行了腐蚀预测研究。经过对比分析,得出了小波神经网络预测精度最高的网络模型为6-7-1结构。在相同输入参数向量下,对含有相同隐层节点个数的小波神经网络和BP神经网络进行腐蚀预测对比,小波神经网络比BP神经网络预测精度高。     

Surface Properties and Solubility Enhancement of Anionic/Nonionic Surfactant Mixtures Based on Sulfonate Gemini Surfactants

As a class of novel surfactants, Gemini surfactants usually exhibit fairly excellent interfacial properties in aqueous solutions on account of the unique structure. They have significant application and development potential for industrial production. However, the mixing properties of Gemini surfactants with conventional surfactants are the key to their application. The equilibrium surface tension curves of anionic/nonionic surfactant mixtures based on the sulfonate Gemini surfactant (SGS-12) were measured using the Wilhelmy Plate method. The parameters of surface adsorption, the interaction parameters between anionic and nonionic surfactants, and the thermodynamic parameters of micelle formation were calculated from the corresponding equations. In addition, the dynamic surface tension (DST) curves of anionic/nonionic surfactant mixtures were examined through bubble profile analysis, and the diffusion performance parameters were acquired from empirical formulas. The solubilization of pyrene in micelle solutions was studied using UV–vis absorption spectroscopy. The results show that the interaction parameters of all anionic/nonionic surfactants are negative, indicating that there is a synergistic effect on reducing the surface tension. For the SGS-12/OP-10, SGS-12/Tween 80, SGS-12/AEO9, and SGS-12/APG0810 mixtures, the optimum mixing ratios are 6:4, 7:3, 7:3, and 8:2, respectively. The thermodynamic data of micelles show that the formation of mixed micelles for SGS-12/APG0810 mixtures is an enthalpy-driven process. The tendency of DST curves of the SGS-12/APG0810 mixture is similar to that of SGS-12. In comparison with single-surfactant solutions, the anionic/nonionic surfactant mixtures show stronger solubilization capacity toward pyrene.     

Prediction of Timber Kiln Drying Rates by Neural Networks

The purpose of this exploratory work was to apply artificial neural network (ANN) modeling to the prediction of timber kiln drying rates based on species and basic density information for the hem-fir mix that grows along the local coastal areas. The ANN models with three inputs (initial moisture content, basic density, and drying time) were developed to predict one output, namely, average final moisture content. The back-propagation algorithm, the most common neural network learning method, was implemented for testing, training, and validation. Optimal configuration of the network model was obtained by varying its main parameters, such as transfer function, learning rule, number of neurons and layers, and learning runs. Accurate prediction of the experimental drying rate data by the ANN model was achieved with a mean absolute relative error less than 2%, thus supporting the powerful predictive capacity of this modeling method.     

Prediction of Timber Kiln Drying Rates by Neural Networks

The purpose of this exploratory work was to apply artificial neural network (ANN) modeling to the prediction of timber kiln drying rates based on species and basic density information for the hem-fir mix that grows along the local coastal areas. The ANN models with three inputs (initial moisture content, basic density, and drying time) were developed to predict one output, namely, average final moisture content. The back-propagation algorithm, the most common neural network learning method, was implemented for testing, training, and validation. Optimal configuration of the network model was obtained by varying its main parameters, such as transfer function, learning rule, number of neurons and layers, and learning runs. Accurate prediction of the experimental drying rate data by the ANN model was achieved with a mean absolute relative error less than 2%, thus supporting the powerful predictive capacity of this modeling method.     

Characterization of radical polystyrene gels

This study deals with the characterization of the structural parameters of radical polystyrene gels by comparing their physicochemical behaviour with ideal polymeric networks prepared by anionic means. To this end, we have assumed that two gels which have the same number of active elastic chains must have the same swelling rate. So as to make the comparison of radical and anionic networks coherent, we have described a two phase model in which a fraction of the network chains is formed by active chains, and the other part is made up of areas with high crosslinking density and low porosity.     

基于人工神经网络的大型浇注型聚氨酯弹性体收缩规律的模拟

收缩率的选取是决定大型浇注型聚氨酯弹性体（CPUE）模具设计是否成功的关键因素之一.以大量的实验为基础,系统地研究了各种工艺参数对大型聚氨酯弹性体制品收缩率的影响,并建立了制品的结构模型以及基于BP网的神经网络模型.通过对实验数据的学习,利用该神经网络模型可以实现以工艺参数为输入,制品收缩率为输出的大型聚氨酯弹性体收缩率的预测.预测结果和实验数据的对比表明此方法可以较为准确地对不同工艺条件下的弹性体收缩率进行预测,从而减少修模次数,降低生产成本.