基于PSO-SVR算法的氧乐果合成过程建模研究

针对氧乐果合成过程中温度控制具有参数时变、时滞后、非线性的特点,提出了一种基于改进粒子群算法的支持向量回归的建模方法。对于支持向量回归模型,3个参数(ε,C,γ)的选取很大程度上决定了其拟合的精度和泛化能力的好坏,采用改进的粒子群算法对参数(ε,C,γ)进行同时寻优,建立了改进的氧乐果合成过程PSO-SVR回归模型,该模型具有很好的学习能力和推广能力。实验结果表明,模型较好地体现了系统的动态特性,可用于氧乐果合成过程的模型预估控制,提高系统的控制品质。     

A group additivity methodology for predicting the thermochemistry of oxygen-containing organosilanes

A combinatorial approach was applied to devise a set of reference Si–C–O–H species that is used to derive group-additivity values (GAVs) for this class of molecules. The reference species include 62 stable single-bonded, 19 cyclic, and nine double-bonded Si–C–O–H species. The thermochemistry of these reference species, that is, the standard enthalpy of formation, entropy, and heat capacities covering the temperature range from 298 to 2000 K was obtained from quantum chemical calculations using several composite methods, including G4, G4MP2, and CBSQB3, and the isodesmic reaction approach. To calculate the GAVs from the ab initio based thermochemistry of the compounds in the training set, a multivariable linear regression analysis is performed. The sensitivity of GAVs to the different composite methods is discussed, and thermodynamics properties calculated via group additivity are compared with available ab initio calculated values from the literature.     

Probing Substrate/Catalyst Effects Using QSPR Analysis on Friedel-Crafts Acylation Reactions over Hierarchical BEA Zeolites

Attempts to optimize heterogeneous catalysis often lack quantitative comparative analysis. The use of kinetic modelling leads to rate (k) and relative sorption equilibrium constants (K), which can be further rationalized using Quantitative Structure-Property Relationships (QSPR) based on Multiple Linear Regressions (MLR). Friedel-Crafts acylation using commercial and hierarchical BEA zeolites as heterogeneous catalysts, acetic anhydride as the acylating agent, and a set of seven substrates with different sizes and chemical functionalities were herein studied. Catalytic results were correlated with the physicochemical properties of substrates and catalysts. From this analysis, a robust set of equations was obtained allowing inferences about the dominant factors governing the processes. Not entirely surprising, the rate and sorption equilibrium constants were found to be explained in part by common factors but of opposite signs: higher and stronger adsorption forces increase reaction rates, but they also make the zeolite active sites less accessible to new reactant molecules. The most relevant parameters are related to the substrates’ molecular size, which can be associated with different reaction steps, namely accessibility to micropores, diffusion capacity, and polarizability of molecules. The relatively large set of substrates used here reinforces previous findings and brings further insights into the factors that hamper/speed up Friedel-Crafts reactions in heterogeneous media.     

Artificial intelligence–based models for the qualitative and quantitative prediction of a phytochemical compound using HPLC method

Isoquercitrin is a flavonoid chemical compound that can be extracted from different plant species such as Mangifera indica (mango), Rheum nobile , Annona squamosal , Camellia sinensis (tea), and coriander ( Coriandrum sativum L.). It possesses various biological activities such as the prevention of thromboembolism and has anticancer, antiinflammatory, and antifatigue activities. Therefore, there is a critical need to elucidate and predict the qualitative and quantitative properties of this phytochemical compound using the high performance liquid chromatography (HPLC) technique. In this paper, three different nonlinear models including artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM),in addition to a classical linear model [multilinear regression analysis (MLR)], were used for the prediction of the retention time (tR) and peak area (PA) for isoquercitrin using HPLC. The simulation uses concentration of the standard, composition of the mobile phases (MP-A and MP-B), and pH as the corresponding input variables. The performance efficiency of the models was evaluated using relative mean square error (RMSE), mean square error (MSE), determination coefficient (DC), and correlation coefficient (CC). The obtained results demonstrated that all four models are capable of predicting the qualitative and quantitative properties of the bioactive compound. A predictive comparison of the models showed that M3 had the highest prediction accuracy among the three models. Further evaluation of the results showed that ANFIS–M3 outperformed the other models and serves as the best model for the prediction of PA. On the other hand, ANN–M3proved its merit and emerged as the best model for tR simulation. The overall predictive accuracy of the best models showed them to be reliable tools for both qualitative and quantitative determination.     

Validity of the quantum regression theorem for resonance fluorescence in a photonic crystal

Correlation functions of a driven two‐level system embedded in a photonic crystal are analyzed. The spectral density of the photonic bands near a gap makes this system non‐Markovian. The equations of motion for two‐time correlations are derived by two different methods, the quantum regression theorem and the fluctuation dissipation theorem, and found to be the same.     

利用回归分析拟合Duncan-Chang双曲线E-γ模型的材料参数

介绍了回归分析的基本概念及求解回归系数的方法,并分析了利用回归分析Duncan-Chang双曲线E-γ土力学模型时所建立起的方程组的特点.而基于方程组的这些特点及材料参数的数值限制,提出了在求解之前需先设定其中一个材料参数R_f的值.最后,通过一个例子的分析,总结了回归分析计算结果的特点,并对R_f值之设定作出了说明及探讨.     

Establishment of The Power—Time Curve Equation of Bacterial Growth in the Log Phase

The power-time curves of two species of bacteria, Vibro metschnikovii, Vibro bollisae were determined calorimetrically by using a 2277 bioactivity monitor. The power-time curve equation of bacterial growth in the log phase can be expressed as

Remote spectrophotometry as applied to determination of the degree of mineralization of peat soils in polesie

Regression relations between the first main component, slope of the SBC curve towards the wavelength axis, and the degree of mineralization of peat soils are established from experimental data. Errors in determination of peat soil productivity are estimated using different spectral indices. For practical use it is recommended to measure the spectral brightness coefficients of peat soils in the 0.50–0.75 μm range. B. I. Stepanov Institute of Physics, National Academy of Sciences of Belarus, 70, F. Skorina Ave., Minsk, 220072, Belarus. Translated from Zhurnal Prikladnoi Spektroskopii, Vol. 65, No. 1, pp. 102–106, January–February, 1998.