基于遗传算法结合神经网络的航空润滑油粘度近红外光谱分析

研究了基于遗传算法(GA)的波长选择方法结合反向传播神经网络(BP-ANN)建模用于在用航空润滑油-40℃运动粘度的近红外光谱分析。采集样品光谱经均值中心化和SavitzkyGolay平滑求导法预处理后,通过分段组合建模初选最优波段,利用遗传算法进一步筛选了对粘度预测敏感的波长点建模。该波长选择方法与相关系数法相比,所建模型预测准确度高。在建模采用的非线性BP-ANN法中,先通过主成分分析(PCA)分解光谱数据,将得分矩阵输入3层神经网络训练,通过参数优化建立最优模型。所建模型对8个在用油进行分析,各预测值与标准值的相对误差均低于2%,并且经t检验不存在显著性差异,模型预测能力较强,应用于在用润滑油质量的快速分析效果好,为油品在线监控提供了参考。     

神经网络在稀土熔盐电解槽槽压中的应用研究

采用BP神经网络算法研究了氟化物体系稀土电解槽设计时的重要参数如极距、电流密度、槽电压等对电能效率的影响,通过对多个槽体的现场取样数据模拟测试分析,研究了一种基于BP神经网络算法的稀土电解槽槽压模型,研究表明.该模型能够较为准确地预测极距、电流密度与槽电压关系,可用于新型稀土电解槽的优化设计.     

用局部组成模型对NaCl-Na2SO4-H2O体系变温下的相平衡的计算和预测

采用局部组成模型理论,建立了不同温度下Na //CI-,SO42- -H2O体系的热力学模型.利用该模型对此体系在不同温度下的溶解度进行了预测,并与实验数据对比,研究了模型对于此体系多温情况的适用性.由局部组成理论中的两离子相互作用能量参数以及不同温度下体系溶解度实验数据,通过Mattab优化函数,获得不同温度下各盐的活度积和两离子混合作用参数,并利用 Matlab非线性方程求解函数,预测了体系在0～150.C范围内的液固平衡数据,与实验数据及Pizter模型汁算结果进行对比.研究结果表明.局部组成模型的多温适用性良好,预测结果与实验数据基本一致.     

计算饱和醇异构体标准生成焓的新方法

基于极性叠加原理,在成功设计烷烃异构体和多氯代烷烃生成焓计算新方法的基础上,设计了一种计算多元醇异构体生成焓的新方法,并合理地假定任一异构体的原子化焓等于三种键(C-C、C-H和C-O-H键)的键能、极性叠加能项以及氢键能项的加和.用这一模型拟合24种原子化焓数据,得到了标准生成焓的估算公式.为了检验预测的精确性,又设计了一种预测方法,使用在排除被预测的化合物条件下回归得到的参数,预测该化合物的生成焓.按这种方法,预测了24种异构体的生成焓.通过该5参数预测的相对于实验值的各种误差(平均绝对误差、均方根误差和最大绝对误差)不仅比7参数的基团法预测的对应误差小得多,而且比相应实验数据的误差还要小.与键加和法比较,该方法的模型包含了极性叠加能和氢键能量,该两项代表了主要的非键相互作用能,表征了不同异构体的结构差异,并大大减少了参数.     

PS/SBS/CaCO_3共混物体系脆韧转变

研究了不同组成的聚苯乙烯 (PS)基三元共混物体系的缺口冲击性能、拉伸性能和断面形貌以及相形态 .实验结果表明 ,微米级碳酸钙的增韧改性效果稍好于纳米级碳酸钙 ,但增强效果却相反 .随着分散相含量的增加 ,3种共混物韧性皆明显提高 ,拉伸曲线向右下方移动 ,应变软化减弱 ,应变硬化增强 .研究发现了随分散相含量的增加 ,PS共混物出现了脆韧转变 ,而且脆韧转变以不同的形式表现了出来 ,即冲击强度、断面形貌以及拉伸曲线在同一区间同时出现了转变     

基于主成分分析和小波神经网络的近红外多组分建模研究

将小麦叶片原始光谱经过预处理后,采用主成分分析(PCA)对数据进行降维,取前3个主成分输入小波神经网络,建立了基于主成分分析和小波神经网络的近红外多组分预测模型(WNN);进一步研究了小波基函数个数的选取(WNN隐层节点数)对小波神经网络模型性能的影响,并将WNN模型与偏最小二乘法(PLS)和传统的反向传播神经网络(BPNN)模型进行了比较.结果表明,所建立的WNN模型能用于同时预测小麦叶片全氮和可溶性总糖两种组分含量,其预测均方根误差(RMSEP)分别为0.101%和0.089%,预测相关系数(R)分别为0.980和0.967.另外,在收敛速度和预测精度上,WNN模型明显优于BPNN和PLS模型,从而为将小波神经网络用于近红外光谱的多组分定量分析奠定了基础.     

基于麻雀搜索算法结合深度前馈神经网络的近红外模型转移方法研究

该文提出了一种基于麻雀搜索算法结合深度前馈神经网络（SSA-DFN）的近红外光谱模型转移方法。使用深度前馈神经网络拟合不同仪器采集到的光谱之间的非线性函数映射,并将麻雀搜索算法用于网络各层连接权值和阈值的初始化,通过种群中个体位置的迭代更新,求得连接权值和阈值的最优初始值;通过多次调整深度前馈神经网络模型的超参数,使网络拟合效果趋于最优,最终确定转移函数。为验证方法的有效性,分别从烟叶近红外光谱谱图、主成分投影和预测结果的角度,将SSA-DFN方法与分段直接校正算法（PDS）、典型相关性分析算法（CCA）转移前后的效果进行了对比。结果表明SSA-DFN方法转移后的从机光谱与原主机光谱重合度最高,转移后主、从机总糖、烟碱含量的预测结果差异不显著,预测平均误差从8.32%、9.15%分别降至4.65%、4.82%,预测均方根误差（RMSEP）和决定系数（R2）等指标均优于PDS和CCA,取得了最佳的转移效果,可满足企业需求。结果表明该方法是一种有效的模型转移方法。     

级联神经网络用于单扫描示波极谱信号的测定

将改进小波神经网络与BP神经网络相结合,提出一种新的混级联神经网络结构,用于单扫描示波极谱信号的同时测定。通过对网络结构的优化和网络参数的调整,加快了训练速度,提高了预测的准确度。用该法对邻、间硝基氯苯混合样进行了预测,结果满意。对级联神经网络法与单一BP神经网络法的预测结果进行了比较,表明级联神经网络优于单一BP神经网络。     

两相聚合物共混体系的相逆转及其力学模型的研究——Ⅰ.模量与组成关系

本文从连续相变理论的角度,提出一个与共混组成相关的权重因子,应用对数加和规则,建立了能预测共混体系相逆转的力学模型,该模型对于描述共混体系的模量与组成关系具有良好的准确性和普适性。     

聚氨酯(PU)/聚乙烯醇(PVA)共混体系的有关性能及其模型研究

本文研究了聚氨酯(PU)-聚乙烯醇(PVA)共混体系的动态力学性质、相逆转现象和透气性能.发现当PVA重量含量为30-50％时,共混体系发生了连续相与分散相的逆转.进一步,通过力学模型描述了该共混体系的模量-组成关系、动态力学温度谱以及透气系数与PVA含量的关系曲线.所有实验数据都落在理论预测的范围内,并分别与改进的Kerner模型和组合Kerner模型相符合.     

聚2,5-呋喃二甲酸乙二醇酯/聚丁二酸丁二醇酯共混物的制备与表征

通过熔融共混制备了聚2,5-呋喃二甲酸乙二醇酯(PEF)/聚丁二酸丁二醇酯(PBS)共混物,探究了制备PEF/PBS共混物的影响因素,考察了共混温度、共混时间、螺杆转速、共混比例对PEF/PBS共混物力学性能的影响因素,并用示差扫描量热仪、热失重、扫描电子显微镜等技术手段对其热性能和相容性进行了表征。 结果表明,当PBS的含量为15%、共混温度为230 ℃,共混时间为90 s、螺杆转速为150 r/min时,为最佳共混制备条件,此时相容性最好,热性能良好,冲击强度和拉伸强度最大,冲击强度相对纯PEF提高了6倍,拉伸强度提高了近20%,从而大幅提高了PEF的冲击强度,有效地增强了PEF的抗冲击韧性。 这些工作为这一生物基聚酯材料的应用提供了可能。     

聚乳酸/纳米SiO_2共混物的热性能

以两种含有不同功能团的纳米SiO2对聚左旋乳酸(PLLA)进行了共混改性,从而提高了PLLA的性能。在纳米SiO2的质量分数为0.5~5%范围内,PLLA与之是相容的,其中含有—NH2功能团的RNS型纳米对PLLA的Tg有一定的影响,加入质量分数为5%的RNS型纳米SiO2后,PLLA的Tg降至52℃。由于RNS纳米SiO2中的—NH2与PLLA分子链中的—C=O形成了弱的氢键,因此在相同质量分数下,对PLLA热稳定性的影响要高于含—CH3功能团的DNS纳米SiO2,质量分数为5%的纳米SiO2与PLLA共混后,PLLA/RNS纳米SiO2共混物的热失重起始温度为370℃,而PLLA/RNS纳米SiO2共混物的则为365℃。     

Blending Technology Based on HPLC Fingerprint and Nonlinear Programming to Control the Quality of Ginkgo Leaves

The breadth and depth of traditional Chinese medicine (TCM) applications have been expanding in recent years, yet the problem of quality control has arisen in the application process. It is essential to design an algorithm to provide blending ratios that ensure a high overall product similarity to the target with controlled deviations in individual ingredient content. We developed a new blending algorithm and scheme by comparing different samples of ginkgo leaves. High-consistency samples were used to establish the blending target, and qualified samples were used for blending. Principal component analysis (PCA) was used as the sample screening method. A nonlinear programming algorithm was applied to calculate the blending ratio under different blending constraints. In one set of calculation experiments, the result was blended by the same samples under different conditions. Its relative deviation coefficients (RDCs) were controlled within ±10%. In another set of calculations, the RDCs of more component blending by different samples were controlled within ±20%. Finally, the near-critical calculation ratio was used for the actual experiments. The experimental results met the initial setting requirements. The results show that our algorithm can flexibly control the content of TCMs. The quality control of the production process of TCMs was achieved by improving the content stability of raw materials using blending. The algorithm provides a groundbreaking idea for quality control of TCMs.     

试验设计与优化方法

本文介绍了作者所在研究小组多年来在试验优化方法方面的研究工作，内容涉及正交试验设计、单纯形优化法、均匀设计法、回归正交设计和模糊正交设计、人工神经网络等多种试验优化设计方法的应用，优化效果的综合评价，多种试验优化设计方法的综合应用等     

Influence of chlorosulfonated polyethylene network structure on poly(acrylonitrile-styrene-acrylate)/poly(α-methylstyrene-acrylonitrile) blends: Mechanical properties,morphologies, glass transition behavior and heat resistance

Acrylonitrile-styrene-acrylate terpolymer and poly(α-methylstyrene-acrylonitrile) (ASA/α-MSAN, 25/75) with different loadings of chlorosulfonated polyethylene (CSM) were prepared via melt blending, with goals of toughening modification of ASA/α-MSAN blends and maintaining the heat resistance simultaneously. The results revealed CSM had excellent toughening effect at room temperature. At 0 °C, impact strength increased linearly with CSM content. However, toughening effect of CSM was undesirable at −30 °C. The temperature-dependent toughening efficiency of CSM was significantly related to its glass transition behavior. Scanning electron microscope analysis on cryo-fractured surfaces revealed the toughening mechanism was the formation of CSM toughening network in matrix, which was further confirmed by selective extraction tests. The formation of CSM network could lead to increased glass transition temperature of the blends at the low temperature region according to dynamic mechanical thermal analysis. Different from other toughening agents, CSM network uncompromised the heat resistance of ASA/α-MSAN blends.     

Hopfield neural network model for calculating the potential energy function from second virial data

The calculation of the intermolecular potential from the second virial coefficient is treated here by using a Hopfield neural network model. From simulated data for the prototype system HeNe, the repulsive potential was obtained with a desired accuracy. The algorithm used here is general, as it can handle noise in the experimental data and, a neural network of higher dimension can be easily constructed. Although the inversion of the short-range part of the potential was obtained in the present work, the Hopfield neural network under consideration can equally be used to invert virial data to give the long-range part of the potential. The convergence of the states of the neuron and the accuracy of the inverted potential is also discussed.     

Thermogravimetric Analysis of Blends Based on Nylon 6 and a Thermotropic Liquid Crystal Copolyester

The thermal stabilities of blends between Nylon 6 and the liquid crystal copolyester Vectra A were investigated by thermogravimetric analysis under dynamic and isothermal conditions. The theoretical mass loss curves were compared with the experimental ones in order to establish the influence of blending on the thermal stabilities of the pure polymers. The characteristic degradation temperatures and the relative values of the volatilized matter percentages were obtained from the dynamic curves, and the effect of the blend composition was analysed. The resulting data, together with the analysis of the activation energies, demonstrated that blending has an important effect on the thermal stability of Vectra A, which undergoes a considerable destabilization in the presence of Nylon 6. This revised version was published online in July 2006 with corrections to the Cover Date.     

Modification of Polylactic Acid by Reactive Blending with Functionalized Imidazolium-based Ionomers and Epoxy-containing Additives北大核心CSCD

Aiming to tackle the serious brittleness problem of polylactic acid（PLA）,PLA-based multiphase blends are prepared by melting reactive blending with hydroxyl functionalized ionomer as the toughening agent and compatibilizer containing epoxy groups. The structures and properties of the blends are characterized by scanning electron microscopy（SEM）,Fourier transform infrared spectrometer（FT-IR）,differential scanning calorimetry（DSC）and mechanical properties tests. The synergistic compatibilization and toughening effects of epoxy soybean oil （ESO）,polyethylene glycol diglycidyl ether （PEGDE）and ethylene-methyl acrylate-glycidyl methacrylate （AX8900） terpolymer are compared and analyzed. The results show that the compatibilization effect is closely related to the content and position of the epoxy group in the additives,which presenting different toughening effects. The addition of AX8900 can effectively improve the toughness of the blend system to obtain balanced mechanical properties. However,ESO tends to lead to crosslinking,which limits the toughening efficiency. PEGDE mainly shows a plasticizing effect,leading to the reduction of tensile strength. The results demonstrate modification of the PLA blends by reactive blending with different epoxy additives and hydroxy-containing polymers is an effective strategy for the development of high-performance biobased PLA materials. © 2022, Science Press (China). All rights reserved.     

Pressure dependence of the demixing behaviour in polymer blends: I. Experimental procedure

The most common way to influence the liquid-liquid phase behaviour in partially miscible (co-)polymer blends is changing the blending temperature. Since most extruders can handle pressures, up to 300 bar, pressure may also be used to influence the miscibility of polymers during blending. We have developed equipment and an experimental procedure to study the pressure dependence of the liquid-liquid demixing behaviour of high-viscous polymer blends under equilibrium conditions. Small amounts (1–4 grams) of specially made polymers are blended in the ‘DSM MINI EXTRUDER’. After a chosen mixing time, a small portion of the blend is injected into a small capillary tube and kept at the blending temperature. The phase behaviour of the blends as a function of temperature and pressure is studied via laser light scattering (at a scattering angle of 90°) in a specially made 400 bar/250°C window autoclave, where the capillary cell is placed in a high temperature grade silicon oil.