多元弱酸及两性物质逐级稳定常数的pH法测定和计算机优化算法

本文由ｐＨ滴定法原理导出多元弱酸及两性物质逐级稳定常数ｋ＿ｉ的计算公式。编辑了由Ｍｏｎｔｅ－Ｃａｒｌｏ法确定初值，Ｈｏｏｋｅ－Ｊｅａｖｅｓ法精解的计算程序。由此算法测定了方酸及其衍生物的各级稳定常数。结果表明，该法实验简便，数值运算效率高，结果可靠     

分光光度法测定络合物稳定常数及摩尔吸光系数的通用算法

提出了一个用单纯形法与线性最小二乘法结合的方法计算络合物稳定的常数及摩尔吸光系 的新算法,并处理了Ｃｊ＾２＋－ＰＡＮ－Ｓ络合体系的数据,求得了该络合物的第一,二级稳定常数。     

铅和富里酸化学形态模拟计算方法的比较

在阳极溶出伏安法获限Ｐｂ和富里酸的溶出电流和电位偏移数值的基础上采用一种新的全局优化方法－遗传算法模拟计算了水体中铅和富里酸的化学形态，对３种计算络合常数的方法进行了比较，误差分析的结果表明：对所研究的体系，电流迭代－遗传算法比电位偏移－遗传算法获得的结果更可靠。     

模拟热谱曲线法（Ⅰ）——简单级数化学反应

提出模拟各种简单级数化学反应的普适热谱曲线方程△＝ａｔｅ＾－ｋβｔ，建立了一种新的热动力学研究法－－模拟热谱曲线法，导出了简单级数化学反应的动力学参数Ｋｎ和速率常数ｋｎ的计算式。实验说明该方法对研究慢反应及较快的反应均适用，还能用于求热动力学体系的冷却常数。     

渐进因子分析—分光光度法测定Fe^3＋—SCN^—络合物的稳定常数

本文利用渐进因子分析与化学平衡相结合的方法迭代求解逐级生成络合物的各级稳定常数，对Ｆｅ＾３＋－ＳＣＮ＾－体系在水溶液及５０％丙酮溶液中的配体浓度－波长的两维双线性吸光度数据矩阵进行处理，求得水溶液中该络合物第一、二级稳定常数为１ｇｋ１＝２．２６，１ｇｋ２＝１．３０。同时该法还可确定各种组分的存在区间及吸收光谱，本文还对丙酮的存在对Ｆｅ＾３＋－ＳＣＮ＾－络合物的影响作了研究，结果表明丙酮，对高配位络     

MATLAB语言在光谱定量分析中的应用

利用MATLAB语言实验紫外-可见吸收光谱法和近红外漫反射光谱法的定量分析数据的处理，着重阐述了偏最小二乘法的多元校正过程。该方法简便、实用，简化并优化了计算过程，效率高，数值稳定性好。     

化学反应动力学研究法

由完全非线性函数ｆ（ｔ）＝Ａ＋Ｂｅ＾－ｋ１ｔ＋Ｃｅ＾－ｋ２ｔ经变换得到自函数递推方程，根据回归分析理论，建立了一种化学反应动力学研究法－－自函数回归法，应用该法在２５℃水溶液中研究了乙酸乙酯和丁酸乙酯的皂化反应的动力学常数。     

正交递归选择法及其应用

本文提出一种新的变量筛选法－正交递归选择法，该法可以得到预报能力较强的模型，即ＰＲＥＳＳ（预报残差平方和）值较低的模型。用该法处理构效关系问题，并与逐步回归正向选择法及ＰＬＳ回归法进行了比较，得到满意的结果。     

用于流化床燃烧脱硫的石灰石的反应活性评价和测试研究

对用于燃煤流化床燃烧脱硫的脱硫剂石灰石的反应活性进行了研究，提出了易于进行数学处理的石灰石硫盐化模型，得出了评价石灰石反应活性的两个指标－最大转化率和反应速率常数。研究方法除采用了传统的热天平法和鼓泡流化床外，还根据循环流化床燃烧技术的特点，发展了提出了湍流床法和石英棉法，并进行了不同试验条件下的实验研究，对不同反应活性温度技术进行了试验比较。     

循环流动固定床光催化反应器动力学数学模拟

以甲基橙为模型反应物,研究了连续循环固定床光催化反应器的动力学过程.根据光催化氧化过程特点,分析并建立了准一级反应动力学方程,对该反应系统的动力学过程进行动态数学模拟,用四阶Runge-Kutta法进行数值计算,结果表明数学模拟与实验数据相吻合.在该光催化反应体系中,处理量增加时实际反应速率常数k基本不变,而表观反应速率常数kapp变小,二者之间关系与反应器体积对处理量体积比（γ）密切相关;反应速率常数受起始浓度影响很大,在15～150 μmol•L－1浓度范围内,lnk=－0.48ln[c0]＋1.42;反应速率常数与光强的关系为k∝I0.5;反应速率常数受溶液pH值的影响也很大.     

用遗传算法优化的约束背景双线性化方法处理含未知背景组分干扰的色谱二维谱图体系

数值遗传算法是全局优化方法, 本文将其引入约束背景双线性化问题的优化求解过程, 以避免陷入局部最优。用本方法处理了模拟数据和两个实际含未知背景干扰的色谱二维谱图体系, 并探讨了如何提高遗传算法在优化平台区域的寻优速度,结果令人满意。     

用数值遗传算法改进非线性PLS法进行构效关系研究

将数值遗传算法同非线性ＰＬＳ结合，改进和完善了非线性ＰＬＳ，推导了指数，对数，倒数和Ｓｉｇｍｏｉｄ函数的公式，构造了可以处理多种非线性函数关系的算法，可用于解决复杂的结构与性能相互关系。     

用数值遗传算法同时求解配合物稳定常数和各型体的纯光谱

用数值遗传算法同时求解配合物稳定常数和各型体的纯光谱张众杰，李通化，朱仲良，丛培盛，孙云平（同济大学化学系，上海，２０００９２）关键词数值遗传算法，二维数据，稳定常数利用滴定或光度法的测定数据，求解酸的离解常数和配合物的稳定常数是化学工作者十分熟悉并...     

Global optimization of binary Lennard-Jones clusters using three perturbation operators

Global optimization of binary Lennard-Jones clusters is a challenging problem in computational chemistry. The difficulty lies in not only that there are enormous local minima on the potential energy surface but also that we must determine both the coordinate position and the atom type for each atom and thus have to deal with both continuous and combinatorial optimization. This paper presents a heuristic algorithm (denoted by 3OP) which makes extensive use of three perturbation operators. With these operators, the proposed 3OP algorithm can efficiently move from a poor local minimum to another better local minimum and detect the global minimum through a sequence of local minima with decreasing energy. The proposed 3OP algorithm has been evaluated on a set of 96 × 6 instances with up to 100 atoms. We have found most putative global minima listed in the Cambridge Cluster Database as well as discovering 12 new global minima missed in previous research.     

An improved optimization strategy and its application to clustering analysis.

In this paper, a new optimization strategy is put forward which locates as many potential unimodal regions as possible in the search space. The potential optima can be further explored by a global optimization method for searching in the identified unimodal regions. The proposed strategy was evaluated by the optimization of test functions. The results obtained by this approach are comparable with those achieved by variable step size generalized simulated annealing (VSGSA) and a genetic algorithm (GA). Finally, we used this strategy in a clustering analysis of a tobacco data set.     

A wavelength selection method based on random decision particle swarm optimization with attractor for near‐infrared spectral quantitative analysis

In this paper, we proposed a wavelength selection method based on random decision particle swarm optimization with attractor for near‐infrared (NIR) spectra quantitative analysis. The proposed method was incorporated with partial least square (PLS) to construct a prediction model. The proposed method chooses the current own optimal or the current global optimal to calculate the attractor. Then the particle updates its flight velocity by the attractor, and the particle state is updated by the random decision with the new velocity. Moreover, the root‐mean‐square error of cross‐validation is adopted as the fitness function for the proposed method. In order to demonstrate the usefulness of the proposed method, PLS with all wavelengths, uninformative variable elimination by PLS, elastic net, genetic algorithm combined with PLS, the discrete particle swarm optimization combined with PLS, the modified particle swarm optimization combined with PLS, the neighboring particle swarm optimization combined with PLS, and the proposed method are used for building the components quantitative analysis models of NIR spectral datasets, and the effectiveness of these models is compared. Two application studies are presented, which involve NIR data obtained from an experiment of meat content determination using NIR and a combustion procedure. Results verify that the proposed method has higher predictive ability for NIR spectral data and the number of selected wavelengths is less. The proposed method has faster convergence speed and could overcome the premature convergence problem. Furthermore, although improving the prediction precision may sacrifice the model complexity under a certain extent, the proposed method is overfitted slightly. Copyright © 2015 John Wiley & Sons, Ltd.     

The fractal dimension as a measure for characterizing genetic variation of the human genome

Motivated by the characteristics of highly clustered single nucleotide polymorphism (SNP) across the human genome, we propose a set of chromosome-wise fractal dimensions as a measure for identifying an individual for human polymorphism. The fractal dimension quantifies the degree of clustered distribution of SNPs and represents parsimoniously the genetic variation in a chromosome. In this sense, the proposed scheme projects the SNP genotype data into a new space which is simpler and lower in dimension. As an illustrative example, we estimate the chromosome-wise fractal dimensions of SNPs that are extracted from the HapMap of Phase III data set. To determine the validity of the proposed measure, we apply principal component analysis (PCA) to the set of estimated fractal dimensions and demonstrate that the set more or less described the population structure of 11 global populations. We also use multidimensional scaling to relate the genetic distances based on PCA to the geographical distances between global populations. This shows that, similar to the SNP genotype data, the fractal dimensions also has a role in genetic distance in the population structure. In addition, we apply the proposed measure to a signature for the classification of global populations by developing a support vector machine model. The selected feature model predicts the global population with a balanced accuracy of about 77%. These results support that the fractal dimension is an efficient way to describe the genetic variation of global populations.     

Global optimization for parameter estimation of differential-algebraic systems

The estimation of parameters in semi-empirical models is essential in numerous areas of engineering and applied science. In many cases, these models are described by a set of ordinary-differential equations or by a set of differential-algebraic equations. Due to the presence of non-convexities of functions participating in these equations, current gradient-based optimization methods can guarantee only locally optimal solutions. This deficiency can have a marked impact on the operation of chemical processes from the economical, environmental and safety points of view and it thus motivates the development of global optimization algorithms. This paper presents a global optimization method which guarantees ɛ-convergence to the global solution. The approach consists in the transformation of the dynamic optimization problem into a nonlinear programming problem (NLP) using the method of orthogonal collocation on finite elements. Rigorous convex underestimators of the nonconvex NLP problem are employed within the spatial branch-and-bound method and solved to global optimality. The proposed method was applied to two example problems dealing with parameter estimation from time series data.     

胺类化合物Kováts指数的拓扑研究

 一种新的连接性指数被定义为 :mQ =∑ (ti·tj·tk·… ) -0 5,其中的0 Q ,3 Q与 2 2种胺类化合物在 3种固定相(OV 10 1,OV 2 2 5和NGA)下的Kov偄ｔｓ指数 (I)显著相关。它们的线性方程如下 :IOV 10 1=118 34 1+197 85 4×0 Q +4 48 773×3 Q ,r=0 9733;IOV 2 2 5=2 49 2 18+1815 76 0×3 Q +34 3 2 2 2×1Q ,r =0 9746 ;INGA=382 196 +2 0 0 4 2 77×3 Q +318 416×1Q ,r =0 9734。这些模型较好地解释了胺类化合物Kov偄ｔｓ指数的递变规律 ,并用Jackknife方法对模型的稳健性进行了检验。     

The role of CH/pi interaction in the stabilization of less-soluble diastereomeric salt crystals

Enantiopure 2-naphthylglycolic acid (NGA) and cis-1-aminobenz[f]indan-2-ol (ABI) were rationally designed as new resolving agents on the model of mandelic acid (MA) and cis-1-aminoindan-2-ol (AI), respectively. As expected, NGA and ABI showed superior chiral recognition ability to racemates, compared with MA and AI. In order to clarify any factors governing the chiral recognition abilities of NGA and ABI, the crystal structures of their less- and more-soluble diastereomeric salts were determined by X-ray crystallographic analyses and revealed that CH/pi interactions play an intrinsic role in chiral recognitions. A theoretical investigation was also performed with the periodic ab initio method by using the X-ray crystal structures of the less-soluble salt crystals with AI and ABI to find the unique properties of CH/pi interaction in the crystalline state, which largely contributed to the stabilization of the crystals.