偏最小二乘回归法用于UHMWPE溶胀试验的研究

采用均匀设计的方法对超高相对分子质量聚乙烯（UHMWPE）树脂的溶胀试验进行系统分析,并用偏最小二乘法建立二级回归模型,对溶胀温度、溶胀时间和溶胀比的决定系数R2,由一级回归的0.967 9、0.935 9和0.944 8分别增加到二级回归的0.974、0.944 9和0.945 7。结果表明,回归模型得到优化的试验条...     

基于三维荧光的污水处理过程中COD的检测

采用三维荧光光谱分析方法研究了某污水处理厂处理过程中的初沉池、二沉池和终沉池出水的化学耗氧量（COD）的检测方法,并对采用主成分回归（PCA）、偏最小二乘（PLS）、平行因子法（PARAFAC）及多维偏最小二乘等方法所建立校正模型的性能进行了比较,结果表明：对于工业污水COD值的检测,常规PLS要好于平行因子法及多维偏最小二乘法等三维建模方法.     

紫外光谱结合偏最小二乘法监测氯化1-羟乙基-3-甲基咪唑离子液体合成反应进程

采用紫外吸收光谱结合偏最小二乘回归建立一种同时快速测定氯化1-羟乙基-3-甲基咪唑离子液体合成反应体系中N-甲基咪唑(IM1)和1-羟乙基-3-甲基咪唑氯化物(IM2)的方法。配制25个样本作为校正集,采用偏最小二乘回归方法和留一交互验证法分别建立IM1和IM2的回归模型,并采用5个样本作为独立验证集评价模型的预测性能。校正模型用于预测氯化1-羟乙基-3-甲基咪唑离子液体合成体系中IM1和IM2含量。结果表明紫外光谱法结合偏最小二乘回归可实现氯化1-羟乙基-3-甲基咪唑离子液体合成过程的近实时监测。     

支持向量机回归在印染废水混凝试验中的应用

支持向量机是从结构风险最小化的角度保证了模型的最大泛化能力。文中运用支持向量机回归对非线性小样本印染废水混凝试验数据进行回归分析研究。研究结果表明最佳联合参数的支持向量机回归对混凝试验数据预测精度明显高于非线性偏最小二乘法和主成分回归法。     

偏最小二乘回归神经网络模型在燃煤锅炉结渣预测中的应用

偏最小二乘回归方法能较好地解决自变量之间的严重相关性问题,笔者将偏最小二乘回归与神经网络耦合,建立了克拉玛依市油田公司某燃煤供热锅炉结渣预测模型。利用偏最小二乘法对影响锅炉结渣的诸多因素进行分析,提取对因变量影响强的成分,从而克服了变量之间的多重相关性问题,降低了神经网络的输入维数。同时,利用神经网络建模可以较好地解决非线性问题。结果表明,预测值与实际值很接近,耦合模型的拟合和预报精度均优于独立使用偏最小二乘回归或神经网络建模的精度。模型对于提高燃煤锅炉的安全运行具有重要的指导意义。     

偏最小二乘-神经网络光度法同时测定水中的NO3-和NO2-

应用偏最小二乘-神经网络直接解析硝酸根和亚硝酸根的紫外光谱,不经分离紫外吸光光度法同时测定硝酸根和亚硝酸根。在BP算法上,引用改进的误差传递函数,并采用均匀试验设计法确定了最佳网络运行参数。用于水样中硝酸根和亚硝酸根的同时测定,回收率分别为99.0%,105.0%。     

偏最小二乘回归方法在橡胶配方设计中的应用

分析采用回归分析方法在建立橡胶配方试验中考察因素和试验数据的二次多项式回归模型时存在的局限性,介绍应用偏最小二乘（PLS）回归方法建立二次多项式回归模型的技术,并通过实例演示PLS回归方法在橡胶配方设计中的应用。结果表明,PLS回归方法适应多因变量对多自变量的回归建模分析,结论可靠,整体性强。     

湍球塔床层液相含率预测的数学模拟

利用搭建的湍球塔实验装置,考察了操作气速、静床高度、喷淋液量、支承网开孔率和湍球直径等参数对湍球塔床层压降和液相含率的影响特性;运用因次分析π定理和偏最小二乘法,得到了液相含率的回归模型。引入前人Gel和V-Noakovic模型,并基于文献实验数据对各模型预测效果作了对比分析。结果表明,偏最小二乘法处理小容量液相含率样本和自变量强相关问题行之有效,用液相含率新模型预测两组文献实验数据的均方百分比误差分别为2.5%和3.1%,预测的精确度优于Gel和V-Noakovic模型,且新模型适用范围更大。偏最小二乘法用于湍球塔床层液相含率预测建模切实可行。     

一种基于可预测偏最小二乘法的故障检测方法

将可预测元分析(Fore CA)与偏最小二乘法(PLS)结合用于故障检测,在选取合适的可预测元的基础上,运用偏最小二乘回归,进一步提高模型对系统的预测能力,克服了偏最小二乘回归方法无法反映系统动态时序特性的缺陷,并构造CUSUM统计量和SPE统计量以检测故障是否发生。最后通过TE模型上的仿真实验结果表明:Fore PLS方法能有效检测慢漂移等故障。     

板式换热器冷却水污垢热阻预测的偏最小二乘回归法

搭建板式换热器冷却水污垢热阻实验台,测得不同时间、流速和温度下天然循环冷却水（松花江水）中铁离子、氯离子、细菌总数、pH值、溶解氧、浊度、电导率等水质参数,随机取一组实验的水质参数作为输入变量,建立换热器冷却水污垢热阻预测的偏最小二乘回归模型,对板式换热器的污垢热阻进行预测。整个实验过程中,热水进口温度为43.5～44.5℃,冷却水进口温度为21.5～22.5℃,流速为0.104 m·s-1,当温度和流速发生变化时,则重新采取数据。经过计算,确立本模型应提取4个潜变量,由此建立了板式换热器冷却水污垢热阻预测模型。预测结果和实验结果最大相对误差在5.11%以内。结果表明偏最小二乘回归算法的污垢模型预测精度高,所建预测模型是合理可行的。     

A novel chemometric method for the prediction of human oral bioavailability

Orally administered drugs must overcome several barriers before reaching their target site. Such barriers depend largely upon specific membrane transport systems and intracellular drug-metabolizing enzymes. For the first time, the P-glycoprotein (P-gp) and cytochrome P450s, the main line of defense by limiting the oral bioavailability (OB) of drugs, were brought into construction of QSAR modeling for human OB based on 805 structurally diverse drug and drug-like molecules. The linear (multiple linear regression: MLR, and partial least squares regression: PLS) and nonlinear (support-vector machine regression: SVR) methods are used to construct the models with their predictivity verified with five-fold cross-validation and independent external tests. The performance of SVR is slightly better than that of MLR and PLS, as indicated by its determination coefficient (R(2)) of 0.80 and standard error of estimate (SEE) of 0.31 for test sets. For the MLR and PLS, they are relatively weak, showing prediction abilities of 0.60 and 0.64 for the training set with SEE of 0.40 and 0.31, respectively. Our study indicates that the MLR, PLS and SVR-based in silico models have good potential in facilitating the prediction of oral bioavailability and can be applied in future drug design.     

均匀设计在优化测定重金属镉实验条件中的应用

均匀设计是由中国科学院应用数学研究所王元教授和方开泰教授共同提出一种新的试验设计方法,具有分布点均匀和试验次数少的优点。在原子荧光法测定土壤中镉的过程中,运用均匀设计软件UD3.0设计试验方案,建立数学模型,并进行回归分析。结果表明:荧光强度(Y)与光电倍增管负高压(X1)、空心阴极灯电流(X2)、载气流量(X3)、还原剂KBH4浓度(X4)、流动介质HCl(X5)和样品进液量(X6)显著相关,当X1在300～320V,X2在30～60mA范围内及X3=400mL/min,X4=3.80%,X5=10.0%,X6=0.5mL的条件下,镉荧光强度值达到最大。将该试验条件应用于土壤中镉含量的测定,结果满意。     

Synthesis and Physicochemical Properties of Silicon-Terminated Octyl/Decyl Polyethylene Oxide as Novel Low-Foam Penetrating Agents

Low-foam penetrating agents have been sought after for many industrial applications. In this paper, we were aiming at a low-foam penetrating agent with excellent wetting ability and hydrolysis resistance, various silicon-terminated penetrating agents were designed and synthesized starting from a popular commercial penetrating agent, octyl/decyl polyethylene oxide (OEO). All the products present much better low-foam performances than that of the precursor OEO. Statistically, multi-OEO substituted products present the best low-foam performance but a slightly weaker wetting ability. Except octyl/decyl polyethylene oxide trimethyl silicane, all the other five products maintain excellent wetting ability and show good hydrolysis resistance at neutral pH and ambient temperature. Di(octyl/decyl polyethylene oxide) dimethyl silicane is the preferred low-foam penetrating agent based on its structure–performance and cost, which reduced the foam volume of OEO from 75 to 4 mL (on the 100 mL volume base) at neutral pH and ambient temperature with a similar wetting ability.     

Determining Frying Oil Degradation by Near Infrared Spectroscopy Using Chemometric Techniques

A rapid method for the determination of some important physicochemical properties in frying oils has been developed. Partial least square regression (PLS) calibration models were applied to the physicochemical parameters and near infrared spectroscopy (NIR) spectral data. PLS regression was used to find the NIR region and the data pre-processing method that give the best prediction of the chemical parameters. Calibration and validation were appropriated by leave one out cross validation and test set validation techniques for predicting free fatty acids (FFA), total polar materials (cTPM; measured by chromatographic method and iTPM measured by an instrumental method), viscosity and smoke point of the frying oil samples. For PLS models using the cross validation techniques, the best correlations (r) between NIR predicted data and the standard method data for iTPM in oils were 93.79 and root mean square error of prediction (RMSEP) values were 5.53. For PLS models using the test set validation techniques, the best correlations (r) between NIR predicted data and standard method data for FFA, cTPM, viscosity and smoke point in oils were 92.58, 94.61, 81.95 and 84.07 and RMSEP values were 0.121, 3.96, 22.30 and 8.74, respectively. In conclusion, NIR technique with chemometric analysis was found very effective in predicting frying oil quality changes.     

邻苯二甲酸酯类化合物生物降解的电性拓扑研究

计算了9种邻苯二甲酸酯的两类拓扑指数值:(1)基于原子类型特征的电性拓扑状态指数(En);(2)自分子拓扑图的邻接矩阵衍生的分子连接性指数(mXtv).这些拓扑指数被用于关联9种邻苯二甲酸酯的生物降解速率常数(Kb)及其半衰期(lnt1/2),经向后逐步回归与偏最小二乘法,建立了定量结构-生物降解相关(QSBR)模型,其四元最佳方程的相关系数(R2)依次为0.995、0.986.这两个方程的平均估算误差接近实验误差,并通过Jackkn ife的逐一剔除法证明有良好的可靠性与稳定性.结果表明,这些模型能够较好地解释邻苯二甲酸酯的生物降解性的递变规律.     

Kao系列桶混助剂的增效作用及其增效机制

室内外试验结果证明,桶混助剂KAOADJUVANTA-134和KAOADJUVANTA-145分别对不同除草剂和杀虫剂具有显著的增效作用,平均可以减少农药使用量三分之一以上。同时,使用助剂后可显著提高耐雨水冲刷的能力。比较添加助剂前后各药剂润湿性能和渗透性能,发现添加这类桶混助剂能显著降低药剂的表面张力和界面张力,使药剂在叶面的铺展性和润湿性更好。添加助剂后,药剂在植物角质膜和气孔渗透方面的性能均显著增加。同时提高润湿性和渗透性是该系列助剂具有显著增效作用的主要原因。     

Pollutant monitoring in tail gas of sulfur recovery unit with statistical and soft computing models

In this article, data-driven models are developed for real time monitoring of sulfur dioxide and hydrogen sulfide in the tail gas stream of sulfur recovery unit (SRU). Statistical [partial least square (PLS), ridge regression (RR) and Gaussian process regression (GPR)] and soft computing models are constructed from plant data. The plant data were divided into training and validation sets using Kennard-Stone algorithm. All models are developed from the training data set. PLS model is designed using SIMPLS algorithm. Three different ridge parameter selection techniques are used to design the RR model. GPR model is designed using four hyper parameter selection methods. The soft computing models include fuzzy and neuro-fuzzy models. Prediction accuracy of all models is assessed by simulation with validation dataset. Simulation results show that the GPR model designed with marginal log likelihood maximization method has good prediction accuracy and outperforms the performance of all other models. The developed GPR model is also found to yield better prediction accuracy than some other models of the SRU proposed in the literature.     

Prediction of the surface tension of binary systems based on the partial least squares method

To predict the surface tension of binary liquid systems, an empirical model is proposed using the partial least squares (PLS) based on the multivariate statistical analysis method. Required parameters for the PLS method to predict the surface tension of binary systems are composed of the thermophysical properties of only pure substances such as critical temperature, critical pressure, critical volume, molar volume, viscosity and vapor pressure for input data block (X) and the reported experimental surface tension data for output data block (Y). The data set for the experimental surface tension of binary liquid systems is divided into the training set for regression and the test set for predicting. An average relative error (%) results of regression and prediction indicate that the PLS method can be a useful tool for predicting the surface tension of liquid binary systems.