[Fe(O)OH]~⊕活化甲烷反应的理论研究

采用密度泛函理论研究了气相中[Fe(O)OH]⊕与甲烷的反应机理。用B3LYP方法优化了势能面上各反应路径的过渡态和中间体等各驻点的结构,并通过振动分析和内禀反应坐标法对过渡态和中间体进行了确认,计算提出了两条可能的反应路线,揭示了[Fe(O)OH]⊕与甲烷的反应机理,同时对反应中存在的势能面交叉现象进行了研究,确定了最低势能交叉点处的作用机制。     

[Ir(H)(OH)]~+活化甲烷的密度泛函理论研究

对[Ir(H)(OH)]+活化甲烷反应进行了详细的密度泛函理论研究,并与Pt(H)(OH)+/CH4以及Ir H+/CH4的反应体系进行了比较。计算结果表明[Ir(H)(OH)]+活化甲烷的反应是经一个过渡态通过一步协同反应来完成的。在整体反应过程中,我们根据Yoshizawa单点垂直计算的方法,并没有在高低自旋态的势能面上找到交叉缝,也就是说没有交叉点出现在反应路径中,反应是以自旋守恒的方式在基态三重态势能面上进行的。     

密度泛函理论计算在荧光材料研究中的应用

密度泛函理论是一种基于量子力学原理的电子结构计算方法,已经成为材料科学和化学领域中重要的计算工具之一。在荧光材料研究中,密度泛函理论计算可以确定晶体结构,计算材料的的能带结构、态密度等信息来帮助研究人员理解荧光材料的本质和特性,为材料的设计和优化提供依据,同时基于密度泛函理论计算的高通量计算可以快速评估大量材料的性质,加快新材料的开发过程,降低研究成本。本文介绍了密度泛函理论计算在荧光材料研究中的应用,并探讨了其未来发展前景。     

密度泛函理论计算在Fenton技术研究中的应用

Fenton技术在废水治理方面的应用日益成熟,但反应机理仍不十分明确.目前,对Fenton技术反应机理的研究中,基于密度泛函理论的量子化学计算方法发挥了重要作用.简述了密度泛函理论计算(DFT)在计算精度和速度方面的优势,介绍了可实现密度泛函理论计算的软件的功能及特点.对均相铁基催化剂、非均相铁基催化剂、多种金属混合催化剂等不同种类铁基催化剂的模拟过程进行了简要介绍,综述了密度泛函理论计算在预测类Fenton催化剂特殊结构、模拟不同污染物在不同条件下的降解过程、解释非均相催化剂表面催化机理与催化特性、揭示Fenton反应机理实质等方面的应用,展望了密度泛函理论计算在阐明不同污染物降解机理、辅助水处理新技术与新型类Fenton催化剂的开发等方面的应用前景.     

遗传算法在催化体系的全局结构优化中的应用

对催化体系进行全局结构优化,搜寻基态结构对预测催化剂结构、分析反应物的吸附特性、研究多相催化反应机理、构建实际反应路径等方面至关重要。遗传算法通过交叉、变异和选择等操作,模拟了自然淘汰进化过程,来搜索势能面上的基态结构。作为一种无偏优化算法,遗传算法的优化过程不依赖于输入结构,具有很强的全局搜索能力。对遗传算法在催化体系的全局结构优化问题中的应用进行了综述,介绍了遗传算法在实空间上进行全局结构优化的基本程序框架以及近年来结合并行计算、机器学习等技术发展的改进框架,并讨论了它们在团簇优化、负载型催化剂的结构优化问题上的相关应用,为遗传算法的进一步改进以及更广泛的应用提供理论指导。     

径向基函数神经网络在黄磷生产中的应用

利用径向基函数神经网络从已有的炉渣分析数据中通过建模训练求得炉渣中磷含量与相关因素之间的非线性关系,从而预测炉渣中的磷含量。结果表明,径向基函数神经网络神经网络有较高的预测精度,可用于黄磷生产工艺指导。     

密度泛函理论在聚合物发泡领域中的应用研究进展

阐述了在发泡领域引入密度泛函理论的原因,综述了国内外对密度泛函理论的研究现状,详细介绍了密度泛函理论在高分子体系中尤其是在发泡领域中的发展与应用研究进展;总结了基于均相成核、非均相成核方法并结合不同动力学、热力学公式的创新的密度泛函模型,并对这些模型的应用进行了综述;将密度泛函模型与其他计算模型进行比对,以体现出其在聚...     

改进BP神经网络在胎面挤出参数预测中的应用

采用隐含层数为4,每个隐含层单元数为10的改进BP神经网络预测销钉机筒冷喂料挤出机的胎面挤出参数.与传统BP神经网络相比,改进算法采用惯性冲量校正、限幅输出和步长自适应解决传统算法学习速度慢且易于陷入局部最小值的问题.Matlab仿真表明,改进算法的收敛速度和收敛效果均明显比传统BP神经网络算法好.训练好的神经网络对胎面挤出过程的参数预测和试验结果间的最大误差为1%,大部分误差小于0.5%.     

径向基函数神经网络在砂体厚度预测中的应用

目前,人工智能神经网络在地震储层参数的预测方面具有广泛的应用,最常用的为BP神经网络,但是效果并不是十分理想。径向基函数神经网络(RBFN)是一种前馈神经网络,其在函数逼近、模式识别方面都优于BP网络,已经在岩性识别、孔渗预测方面取得了较好的应用效果。本文首次将此方法运用于预测砂体厚度,利用地震属性信息和神经网络的学习,基于实际数据计算,最后计算出相应的砂体厚度值,并与实测值进行误差分析。实例分析表明,利用径向基函数神经网络进行砂体厚度预测具有一定的可行性和实用价值。     

The electronic structure effect in heterogeneous catalysis

Using a combination of density functional theory calculations and X-ray emission and absorption spectroscopy for nitrogen on Cu and Ni surfaces, a detailed picture is given of the chemisorption bond. It is suggested that the adsorption bond strength and hence the activity of transition metal surfaces as catalysts for chemical reactions can be related to certain characteristics of the surface electronic structure.     

Reflections on applications of NMR in heterogeneous catalysis and surface chemistry

The use of NMR to characterise heterogeneous catalytic systems and processes is assessed, critically, in overview. The generally wide scope and applicability of NMR is placed in the context of the constraints of NMR experimental requirements and the general goal of studying catalytic systems under conditions relevant to their actual use. In particular, the issues of sensitivity, resolution and dynamic processes in NMR are considered alongside the aims of surface selectivity, sample environment control and the desirability of associated characterisation of the catalytic systems by methods complementary to NMR.     

A neural network approach to predict activity coefficients

Artificial neural networks (ANNs) and a group‐contribution approach were used to develop an algorithm to predict activity coefficients for binary solutions. The Levenberg–Marquardt algorithm was used to train the ANN and to predict the parameters of the Margules equation. The ANN was trained using phase‐equilibrium database from DECHEMA. The selected systems include alcohols, phenols, aldehydes, ketones, and ethers. The trim mean based on 20% data elimination was selected as the best representation of the Margules‐equation parameters. The algorithm was validated with 121 VLE systems and results show that the ANN provides a relative improvement over the UNIFAC method.     

基于正则化的函数连接神经网络研究及其复杂化工过程建模应用

在化工过程的建模中,由于过程数据的高维度和高非线性,导致计算量大幅提升和建模难度加大。为了解决这一问题,提出了一种基于正则化方法的函数连接神经网络模型(regularization based functional link neural network, RFLNN)。所提出的RFLNN方法里,通过使用正则化的方法对函数连接神经网络的权值进行优化,一方面大幅降低网络计算复杂度和计算量,另一方面极大程度上克服网络局部极值和过拟合的问题,以提高函数连接神经网络的学习速度和精度。为了验证所提出方法的有效性,首先采用UCI数据中Real estate valuation数据对其性能进行测试;随后将所提的方法应用于高密度聚乙烯(high density polyethylene,HDPE)复杂生产过程进行建模。UCI标准数据与工业数据的仿真结果表明,与传统FLNN对比,RFLNN在处理高维复杂化工过程数据时具有收敛速度快、建模精度高等特点。     

Control valve stiction detection using Markov transition field and deep convolutional neural network

Control valve stiction is an industrial problem that often causes oscillations in process control loops. Oscillating control loops are not capable of maintaining key process variables near or at their desired values, thus yielding low-quality products, inducing economic loss, and increasing environmental impacts. Therefore, it is of vital importance to detect stiction in industrial control valves. In this regard, the present work proposes a new method based on the Markov transition field and convolutional neural network (CNN) to identify sticky control valves in industrial control loops. The Markov transition field is employed to convert process variable (PV) and controller output (OP) into two-dimensional images, which are then utilized by CNN to learn to distinguish stiction induced oscillations from oscillations brought out by a non-stiction condition. A transfer learning strategy is adopted to improve the stiction detection capability of the proposed method. Its performance is evaluated via its application to benchmark control loops taken from the chemical, paper, mining, and metal industries. Results demonstrate that the proposed method obtains the correct verdict for the majority of the control loops studied.     

基于数据特征提取的AANN-ELM研究及化工应用

针对极限学习机不能有效解决化工过程中高维数据建模的问题,本文将其与自联想神经网络结合,通过自联想神经网络过滤输入数据中存在的冗余信息、提取特征分量,并对所提取的特征分量采用极限学习机进行训练,由此形成了一种基于数据特征提取的AANN-ELM(auto-associative neural network-extreme learning machine)神经网络。同时,以UCI标准数据集进行测试,以精对苯二甲酸(PTA)溶剂系统进行验证,结果表明,AANN-ELM在处理高维数据时具有学习速度快、网络稳定性强、建模精度高的特点,为神经网络在复杂化工生产中的应用提供了新思路。     

A theoretical explanation of solvent effects in zeolite catalysis

In a previous study of solid acid catalysis (Nature (1998) 389, 832) we showed that the catalytic activity of zeolites could be increased by the coadsorption of “solvent” molecules, such as nitromethane. These coadsorbates do not participate directly in the reaction, but alter the environment within the zeolite such that reactivity is increased. In this work we provide further theoretical explanation of the increased reactivity observed upon coadsorption. We first use density functional theory (DFT) to study the proton affinity of acetone, and complexes of acetone with propane, bromomethane, nitromethane, nitroethane, nitropropane, and acetonitrile. We find that the proton affinity of acetone in the complexes is much higher than for acetone alone. Optimizations and frequency calculations at the B3LYP/6–311++G** level predict proton affinity increases that range from 0.9 kcal/mol for the acetone/propane complex to 12.8 kcal/mol for the acetone/acetonitrile complex. The increase in proton affinity due to the coadsorbed molecules is one of the causes of the increased reactivity observed experimentally. We also used DFT (B3LYP/DZVP2) to optimize the geometry of acetone and the acetone–nitromethane complex in contact with a cluster model of HZSM-5. There is greater proton transfer from the zeolite to acetone when nitromethane is present, as is reflected in the shorter distance between the acidic zeolite proton and the carbonyl carbon of acetone. Predictions of ¹H and ¹³C NMR isotropic chemical shifts also indicate increased proton transfer to acetone in the presence of nitromethane. This further demonstrates how coadsorbates promote reactivity. This revised version was published online in June 2006 with corrections to the Cover Date.     

Convolutional neural nets in chemical engineering: Foundations,computations, and applications

In this article, we review the mathematical foundations of convolutional neural nets (CNNs) with the goals of: (i) highlighting connections with techniques from statistics, signal processing, linear algebra, differential equations, and optimization, (ii) demystifying underlying computations, and (iii) identifying new types of applications. CNNs are powerful machine learning models that highlight features from grid data to make predictions (regression and classification). The grid data object can be represented as vectors (in 1D), matrices (in 2D), or tensors (in 3D or higher dimensions) and can incorporate multiple channels (thus providing high flexibility in the input data representation). CNNs highlight features from the grid data by performing convolution operations with different types of operators. The operators highlight different types of features (e.g., patterns, gradients, geometrical features) and are learned by using optimization techniques. In other words, CNNs seek to identify optimal operators that best map the input data to the output data. A common misconception is that CNNs are only capable of processing image or video data but their application scope is much wider; specifically, datasets encountered in diverse applications can be expressed as grid data. Here, we show how to apply CNNs to new types of applications such as optimal control, flow cytometry, multivariate process monitoring, and molecular simulations.     

基于数据属性划分的递阶ELM研究及化工应用

针对极限学习机（ELM）不能有效处理化工过程中强耦合、带噪声的高维数据建模问题,提出了一种基于数据属性划分的递阶ELM神经网络DHELM。该神经网络采用数据属性划分（DAD）方法对高维输入进行聚类、建立自联想子网,并将自联想子网所提取的特征分量作为极限学习机的输入进行建模。同时,利用UCI标准数据集进行了测试,通过工业应用实例进行了验证,并进行了模型对比。结果表明,DHELM网络在处理复杂高维数据时具有收敛速度快、建模精度高、网络稳定性强的特点,为神经网络发展及其化工应用提供了新思路。     

Experimental and artificial neural network modeling study on soot formation in premixed hydrocarbon flames

The formation of soot in premixed flames of methane, ethane, propane, and butane was studied at three different equivalence ratios. Soot particle sizes, number densities, and volume fractions were determined using classical light scattering measurement techniques. The experimental data revealed that the soot properties were sensitive to the fuel type and combustion parameter equivalence ratio. Increase in equivalence ratio increased the amount of soot formed for each fuel. In addition, methane flames showed larger particle diameters at higher distances above the burner surface and propane, ethane, and butane flames came after the methane flames, respectively. Three-layer, feed-forward type artificial neural networks having seven input neurons, one output neuron, and five hidden neurons for soot particle diameter predictions and seven hidden neurons for volume fraction predictions were used to model the soot properties. The network could not be trained and tested with sufficient accuracy to predict the number density due to a large data range and greater uncertainty in determination of this parameter. The number of complete data set used in the model was 156. There was a good agreement between the experimental and predicted values, and neural networks performed better when predicting output parameters (i.e. soot particle diameters and volume fractions) within the limits of the training data.