Dual stimuli-responsive polymers derived from α-amino acids: Effects of molecular structure,molecular weight and end-group

Four kinds of well defined poly(N-methacryloyl-L-amino acid)s were prepared via reversible addition fragmentation chain transfer polymerization. The effects of molecular structure, molecular weight, and end-group on their responsive properties in water were investigated. We found that the monomer structure is critical for the polymer to exhibit stimuli-responsive properties in water; only polymers derived from aspartic acid showed defined dual thermo- and pH-responsive properties. To study the effects of molecular weight and end group on the lower critical solution temperature behavior of poly(N-methacryloyl-L-β-isopropyl aspartic acid) (PMAIPAC), a series of PMAIPAC homopolymers with controlled molecular weights and narrow polydispersities were prepared and treated with excess radical initiators to give defined end groups. The cloud point of PMAIPAC was found to increase with the increase of solution pH, due to the ionization of carboxylic acid groups, and decrease with the increase of molecular weight. The structure of end groups also affects the cloud point of PMAIPAC particularly for low molecular weight samples at low pH.     

Surface properties of surfactants derived from natural products. Part 1: Syntheses and structure/property relationships—Solubility and emulsification

Several novel and some previously known, mostly sugar-based, surfactants have been synthesized and some of their surface properties have been characterized and compared with those of commercial nonylphenol ethoxylates. The surfactant solubility in water, ethanol, and dodecane was studied. The properties of these compounds as emulsification agents in systems composed of the surfactant with water/isopropyl myristate, water/rapeseed oil, and water/dodecane are presented. The aqueous solubility of the surfactants follows the general trend expected from their hydrophilic-lipophilic balance according to Griffin (HLBG), but it is also clear that the nature of the headgroup and the structure of the nonpolar part affect the solubility in a manner not aptured in the standard HLBG concept. An ester or amine group as the connecting unit between the hydrophile and the hydrophobe produces a more water-soluble surfactant than the corresponding amide derivative. Some effective emulsifiers were found. For instance, the surfactants with a dehydroabietic nonpolar group appear to be promosing emulsifiers. Most sugar-based surfactants were able to form macroemulsions of up to around 2 wt/vol% of oil. The stability of many of these amulsions was very high, extending for months.     

Column adsorption of Cu(Ⅱ) by polymer-supported nano-iron oxides in the presence of sulfate: Experimental and mathematical modeling

Polymer-supported hydrous iron oxides (HFOs) are promising for heavy metals removal from aqueous systems.The ubiquitous inorganic ligands,e.g.,sulfate,are expected to exert considerable impacts on pollutants removal by these hybrid sorbents.Herein,we obtained a hybrid sorbent HFO-PS by encapsulating nanosized HFO into macroporous polystyrene (PS) resin.Both batch and column sorption experiments of Cu(Ⅱ) by HFO-PS were carried out in the presence of sulfate.Obviously,the presence of sulfate is favorable for Cu(Ⅱ) sorption onto HFO-PS.The performances of column Cu(Ⅱ) removal were fitted and predicted with Adams-Bohart,Clark,Thomas and BDST models.Thomas model is suggested best-fit to predict the breakthrough curves.Besides,a linear correlation is observed between breakthrough time and column length based on BDST model,which might be useful for predicting the breakthrough time for Cu(Ⅱ) removal by HFO-PS.     

Unusual interaction extended between the pyranose ring oxygen and Zn(II) center in the complexes derived from 4,6-O-butylidene/ethylidene-N-(α-hydroxynaphthylidene/o-hydroxybenzylidene)-β-d-glucopyranosylamine: Evidence for a pseudo-bicapped tetrahedral complex of Zn(II) based on the crystal structure

The Zn(II) complexes of 4,6-O-butylidene/ethylidene-N-(α-hydroxynaphthylidene/o-hydroxybenzylidene)-β-d-glucopyranosylamine have been synthesized and characterized using spectral and analytical methods and structure for one of the products was established. The geometry of the complexes vary from unusual distorted trigonal bipyramidal to pseudo-bicapped tetrahedron depending upon the extent of binding of pyranose ring oxygens to the Zn(II) ion, as evidenced from crystal structures. Such interaction is also reflected on the optical rotation and CD spectral properties of these complexes in solution.     

单纯形-差分进化算法及其在软测量建模上的应用

针对差分进化算法（Differential Evolution Algorithm,DEA）容易陷入局部最优值且搜索效率不高的缺点,提出一种单纯形一差分进化算法（SM—DEA）。该算法在DEA中引入计算量小、搜索速度快且局部搜索能力很强的单纯形方法（Simplex Method,SM）,在迭代过程中交叉使用DEA和SM。对标准函数的测试表明,该算法具有很强的全局搜索能力和很高的搜索效率。将算法应用于乙炔加氢反应器出口乙炔浓度的软测量建模,结果表明该算法能提高模型的精确度,降低预测误差,具有较高的实用价值。     

基于工业色谱仪的精馏脱炔烃塔软测量建模

提出一种基于工业色谱仪的软测量建模方法,并针对碳五馏分分离过程中的精馏脱炔烃塔塔底成分估计问题,建立了合适的工业软测量模型。介绍了工业色谱仪在线质量检测原理和LM-BP神经网络模型的建立,并利用工业色谱仪在线检测的质量数据进行系统的在线和周期性模型更新,提高了软测量模型的在线估计精度。研究结果表明,基于工业色谱仪的LM-BP神经网络模型是一种有效的软测量建模方法。     

Identification of uncertain MIMO Wiener and Hammerstein models

Several approaches can be found in the literature to perform the identification of block oriented models (BOMs). In this sense, an important improvement is to achieve robust identification to cope with the presence of uncertainty.In this work, two special and widely used BOMs are considered: Hammerstein and Wiener models. The models herein treated are assumed to be described by parametric representations. The approach introduced in this work for the identification of the multiple input-multiple output (MIMO) uncertain model is performed in a single step. The uncertainty is described as a set of parameters which is found through the solution of an optimization problem.A distillation column simulation model is presented to illustrate the robust identification approach. This process is an interesting benchmark due to its well-known nonlinear dynamics. Both Hammerstein and Wiener models are used to represent this plant in the presence of uncertainty. A comparative study between these models is established.     

免疫算法在焦化装置软测量中的应用

提出了一种自适应免疫算法，其很好地解决了原始免疫算法中收敛精度低和寻优速度慢的缺点。通过对比分析标准测试函数的计算结果，自适应免疫算法的优良性得到充分地证明。然后，免疫算法被用于优化BP神经网络的结构和参数。结果表明，不但网络结构得到较好地控制，而且泛化性能也有较大地提高。最后，算法在优化神经网络上的有效性也在焦化装置精馏塔汽油干点软测量建模中得到很好地证实。     

Nonlinear system identification using Wiener type Laguerre-Wavelet network model

Compact nonlinear black box models, capable of representing highly nonlinear systems, are of high demand both in industry and academia. In this paper it has been shown that the use of Laguerre basis filters coupled with a wavelet network in Wiener type model structure are capable of modeling highly nonlinear systems with acceptable accuracy. Although individual merits of orthonormal basis functions (especially Laguerre filters) and multiresolution wavelet decompositions and/or wavelet network have been well documented in various literatures on system identification in the past, their combinational advantages in nonlinear system identification has never been explored. Laguerre filter models have the ability to approximate linear systems (even with time delay) with a model order lower than the traditional ARX (e.g., FIR, AR) modeling. Use of Laguerre models for mildly nonlinear system is possible only with a piece-wise linear models. Wavelet basis functions have the property of localization in both time and frequency and they can approximate even severe nonlinearities with appreciable accuracy with fewer model terms. But wavelet approximations fail miserably, in terms of model parsimony, if used for approximating linear or mildly nonlinear systems. In the present work a Laguerre-Wavelet network model has been proposed which combines the Laguerre and Wavelet approximations. In the said model, merits of both these approximations are retained whereas their demerits are suppressed.     

基于PLS快速剪枝法的RBF神经网络软测量模型建模方法和应用

提出一种从RBF神经网络隐含层的输出信息出发，通过PLS快速剪枝法，一次性剪去多余节点，生成最优规模的数学解析模型的方法。并用该方法建立了某化工企业精对苯二甲酸(PTA)晶体平均粒径的软测量模型，针对实际对象进行仿真研究，结果表明，该方法计算速度快，建立的模型精度高，适合实际工程应用的需求。     

一种基于支持向量机增量学习的软测量建模方法

针对软测量模型在现场的失效问题,提出一种基于支持向量增量学习的软测量建模方法,将增量样本中违背Karush-Kuhn-Tucker条件的样本引入到工作样本集中,同时将非支持向量中到特征空间超球球心距离较小的样本剔除出工作样本集。并将提出的方法用于对二甲苯吸附分离过程中产品纯度的预测中。     

基于仿射聚类、高斯过程和贝叶斯决策的多模型软测量建模

Presented is a multiple model soft sensing method based on Affinity Propagation(AP),Gaussian process(GP) and Bayesian committee machine(BCM).AP clustering arithmetic is used to cluster training samples according to their operating points.Then,the sub-models are estimated by Gaussian Process Regression(GPR).Finally,in order to get a global probabilistic prediction,Bayesian committee machine is used to combine the outputs of the sub-estimators.The proposed method has been applied to predict the light naphtha end point in hydrocracker fractionators.Practical applications indicate that it is useful for the online prediction of quality monitoring in chemical processes.     

化工过程软测量建模方法研究进展

软测量仪表是解决化工过程中质量变量难以实时测量的重要手段。软测量仪表的核心问题是软测量建模。阐述了软测量建模与辨识和非线性建模的关系:质量变量和易测变量的动态关系存在于增量之间,辨识模型依赖于增量数据,软测量建模则是依赖于实测变量数据来获取这个动态关系;非线性建模建立了变量间的静态关系,忽略了对象动态特性,而软测量建模要兼顾对动态特性的表征。随着人们对过程特性的认识加深,软测量建模方法不断发展,经历了从机理建模到数据驱动建模,从线性建模到非线性建模,从静态建模到动态建模的过程。详细讨论了软测量建模的发展过程,众多建模方法的优缺点及适用情况和现在建模的热点,最后对软测量建模方法进行了总体展望。     

基于最小二乘支持向量机的软测量方法在精对苯二甲酸生产中的应用

研究了一种基于改进型的最小二乘支持向量机的软测量方法在精对苯二甲酸(PTA)生产中晶体粒径大小估计中的应用。对最小二乘支持向量机采取了稀疏化与实时滚动改进,改进后的算法样本数量大大降低,同时保持了原有水平的预测能力。结果表明该方法对精对苯二甲酸的颗粒具有较高的预测能力。