紫杉醇溶解度的测定与关联

引　言紫杉醇是目前临床上治疗乳腺癌、卵巢癌最有效的化疗药物 ,也是最有前景的广谱抗癌药物之一 .在紫杉醇的生产中 ,最后步骤都是重结晶纯化操作 ,才能得到最终产品 ,但这些重结晶过程的进行尚处于经验水平上 .因此 ,要进行合理的结晶反应器的设计 ,选择合适的重结晶溶剂 ,优化重结晶工艺条件 ,溶解度数据必不可少 ,而目前紫杉醇在各种溶剂中的溶解度数据均未见报道 .另外 ,在紫杉醇常规分离纯化过程 ,如萃取、色谱过程、结晶以及注射液制剂中 ,均需了解其溶解特性 ,为溶剂的选择提供依据 .由于紫杉醇这样的天然产物分子结构复杂 ,现有的…     

咖啡因在水和乙醇中的溶解度及其关联

The solubility of caffeine in water and ethanol at 0—50 ℃ was measured using the laser method. The results were regressed with an empirical equation and simplified EOS correlation. A 2 - 2 - 1 backpropagation (BP) artificial neural network (ANN) model was selected from many other models. The prediction of interpolation and extrapolation of the data was made with trained 2 - 2 - 1 BP ANN model. The result was satisfactory.     

UNIFICATION OF FRUIT WATER SORPTION ISOTHERMS USING ARTIFICIAL NEURAL NETWORKS

The chemical composition, water activity, temperature and equilibrium moisture content (EMC) for 10 selected fruits were determined. Two methods of water sorption modeling, the GAB equation and the artificial neural network (ANN) method, were compared for their ability to predict water sorption behavior. Unlike the GAB equation, which uses only physical data for modeling, the ANN method uses both physical and chemical compositional data to make predictions. The ANN was superior, in most cases, to that of the GAB equation, in predicting EMC. This superiority was due to the availability of the additional chemical compositional information. The ANN method could predict EMC with a mean relative error of 9.85% and a standard error (S_x) of 1.59% EMC. The correlation coefficient (r²) of the relationship between the actual and predicted values of equilibrium moisture content obtained by the ANN was 0.9938. The ANN model was able to show a temperature dependent crossing of water sorption isotherms, due to the dissolution of sugar crystals in the fruit. The ANN was also able to predict the extent of crossing, depending upon differences in the individual fruit chemical composition.     

基于纯二次模型的单基发射药贮存寿命预测

利用MATLAB软件分别得到不同地区库存单基发射药贮存寿命与温湿度关系的拟合曲线,发现拟合曲线符合二次模型.利用环境特征相对典型地区的温湿度数据,建立了纯二次回归模型,得到贮存寿命与温湿度的回归方程.回归方程的剩余标准差和残差极小,表明拟合效果非常好,能够较准确预测不同温湿度条件下单基发射药的安全贮存寿命.     

UNIFICATION OF FRUIT WATER SORPTION ISOTHERMS USING ARTIFICIAL NEURAL NETWORKS

The chemical composition, water activity, temperature and equilibrium moisture content (EMC) for 10 selected fruits were determined. Two methods of water sorption modeling, the GAB equation and the artificial neural network (ANN) method, were compared for their ability to predict water sorption behavior. Unlike the GAB equation, which uses only physical data for modeling, the ANN method uses both physical and chemical compositional data to make predictions. The ANN was superior, in most cases, to that of the GAB equation, in predicting EMC. This superiority was due to the availability of the additional chemical compositional information. The ANN method could predict EMC with a mean relative error of 9.85% and a standard error (S _x ) of 1.59% EMC. The correlation coefficient (r ²) of the relationship between the actual and predicted values of equilibrium moisture content obtained by the ANN was 0.9938. The ANN model was able to show a temperature dependent crossing of water sorption isotherms, due to the dissolution of sugar crystals in the fruit. The ANN was also able to predict the extent of crossing, depending upon differences in the individual fruit chemical composition.     

2,4-二氯苯酚在水中溶解度的测定及关联

在pH值为6.2或9.3条件下,采用综合法测定了2,4-二氯苯酚在水中的固液平衡数据,分别用经验方程、W ilson方程、固液平衡λH方程对实验数据进行了拟合处理。结果说明,在pH值为9.3条件下,2,4-二氯苯酚的水溶解度增大;W ilson方程、λH方程模型均能很好地关联2,4-二氯苯酚的水溶解度数据,pH=9.3时平均相对误差分别为3.00%,1.17%,其中λH方程拟合效果更好。     

An Artificial Neural Network Model for Prediction of Drying Rates

Drying rate data were generated for training of an ANN model using a liquid diffusion model for potato slices of different thicknesses using air at different velocities, humidities and temperatures. Moisture content and temperature dependence of the liquid diffusivity as well as the heat of wetting for bound moisture were included in the diffusion model making it a highly nonlinear system. An ANN model was developed for rapid prediction of the drying rates using the Page equation fitted to the drying rate curves. The ANN model is verified to provide accurate interpolation of the drying rates and times within the ranges of parameters investigated.     

苊冷却结晶动力学的间歇动态法研究

通过添加晶种的间歇冷却结晶实验研究了苊在乙醇中的结晶动力学。由苊晶体的粒数密度数据,通过矩量变换法按粒度无关生长模型求解粒数衡算方程,采用多元线性最小二乘法回归动力学数据,得到苊在乙醇中晶体生长及成核速率方程。对动力学方程的理论分析表明:搅拌速率对二次成核影响显著,随搅拌速率增加,晶体的成核速率明显增加。同时适宜的过饱和度及较低的悬浮密度,有利于苊晶体生长。该研究为苊冷却结晶特性的辨识、粒度分布的控制及工业放大提供了重要的理论指导。     

An Artificial Neural Network Model for Prediction of Drying Rates

Abstract

Drying rate data were generated for training of an ANN model using a liquid diffusion model for potato slices of different thicknesses using air at different velocities, humidities and temperatures. Moisture content and temperature dependence of the liquid diffusivity as well as the heat of wetting for bound moisture were included in the diffusion model making it a highly nonlinear system. An ANN model was developed for rapid prediction of the drying rates using the Page equation fitted to the drying rate curves. The ANN model is verified to provide accurate interpolation of the drying rates and times within the ranges of parameters investigated.     

基于神经网络-遗传算法优化生物柴油制备工艺

根据生物柴油制备的实验数据,用人工神经网络(ANN)的反向传播(BP)算法建立了生物柴油转化率神经网络预测模型,提出了适宜的人工神经网络拓扑结构,讨论了BP算法中学习速率、动量系数及过拟合现象对网络的影响。实验数据检验表明,ANN方法能准确地关联生物柴油制备工艺条件与转化率的关系,转化率预测平均相对误差为1.917%,复相关系数R为0.9996;该神经网络预测模型用遗传算法优化,得到了最佳生物柴油制备条件。     

MODELING AND OPTIMIZATION OF MEMBRANE CHEMICAL CLEANING BY ARTIFICIAL NEURAL NETWORK,FUZZY LOGIC,AND GENETIC ALGORITHM

Robust artificial neural network (ANN) and fuzzy logic (FL) models were derived for chemical cleaning of microfiltration membranes fouled by milk under a wide range of operating conditions. The accuracies of the models were compared with multiple linear regressions (MLR). The developed models are useful tools for predicting the performance of chemical cleaning. The effects of different operating conditions on cleaning performance were elucidated using the ANN developed model. Moreover, optimum cleaning condition was determined by genetic algorithm and ANN model. The current research demonstrated that fuzzy logic and an artificial neural network can quantitatively capture cumulative effects of a range of operating conditions on flux recovery and resistance removal during a cleaning process.     

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.     

人工神经网络用于 PBX 炸药装药密度的研究

比较了利用人工神经网络（ＡＮＮ）和混合物密度计算公式对一种聚合物粘结炸药（ＰＢＸ）的装药密度计算的结果。发现利用人工神经网络所得的结果可以正确反映配方组成和温度与装药密度的关系，而且对于新配方和温度，可以利用人工神经网络得到正确的密度数值预报。     

基于神经网络-遗传算法优化制氢工艺水碳比

根据某炼油厂制氢车间的生产数据,用人工神经网络(ANN)的反向传播(BP)算法建立了制氢装置转化生产中的水碳比神经网络预测模型,生产数据的检验表明,ANN方法能准确地关联和预报制氢装置转化生产中的水碳比,水碳比预测平均相对误差为2．83％;该神经网络预测模型用遗传算法优化并得到了最佳制氢工艺操作条件。     

SPSS软件在有机毒物毒性数据分析中的应用

水环境中有机污染物种类繁多但毒性资料缺乏。本文应用SPSS统计学软件,对文献报导的29个取代苯类化合物对发光菌的急性毒性数据进行线性回归,建立化合物结构与毒性的相关方程,并应用该软件对方程的稳健性和可靠性进行检验,得到了相关性和稳健性均良好的构效关系模型,用以预测同类污染物对水生生物的毒性。     

水-丙烯酸-醋酸-甲苯四元体系液液平衡数据的测定与关联

采用液液平衡釜,在常压下,测定了293.15,303.15,313.15 K时水-丙烯酸-醋酸-甲苯四元体系的液液平衡数据,为萃取-非均相共沸精馏法精制丙烯酸选择适宜的溶剂提供依据.使用Othmer-Tobias方程对实验数据进行线性拟合.分别采用非随机双液(NRTL)和通用似化学(UNlQUAC)活度系数模型对实验体...     

Modeling tensile modulus of (polyamide 6)/nanoclay composites: Response surface method vs. taguchi‐optimized artificial neural network

Tensile modulus is an important mechanical property of polymer/nanoclay nanocomposites. In this study, response surface method (RSM) and Taguchi‐optimized artificial neural network (Taguchi‐optimized ANN) were used to model tensile modulus as a function of nanoclay content, melt temperature, screw speed, and feeding rate for (polyamide 6)/nanoclay nanocomposites prepared in a twin‐screw extruder. The comparison between Taguchi‐optimized‐ANN‐ and RSM‐generated plots showed that predictions made by both models were in agreement in general. Coefficient of determination, R², showed that the RSM model can explain the variation with the accuracy of 0.768, indicating there was no strong correlation. However, from ANOVA, the p value for the RSM model was less than 0.05, signifying that the obtained model could be considered statistically significant. In addition, further assessment in terms of data fitting and prediction capabilities demonstrated the superiority of a properly trained Taguchi‐optimized ANN model in characterizing the nonlinear behavior of a response‐factors relationship. The Taguchi‐optimized ANN model R² for training data and testing data were 0.965 and 0.902, respectively. Also, the Taguchi‐optimized ANN model was developed by using 20% less data in comparison to the RSM model. J. VINYL ADDIT. TECHNOL., 22:29–36, 2016. © 2014 Society of Plastics Engineers     

Response Surface Methodology and Artificial Neural Networks for Optimization of Catalytic Esterification of Lactic Acid

Response surface methodology (RSM) and artificial neural network (ANN) models were employed to study the esterification of lactic acid and isoamyl alcohol. A carbon-based solid acid catalyst prepared by wet impregnation was used in the esterification reaction. Experimental characterization revealed its potential to serve as catalyst for the esterification reaction. The experiments were performed based on the design of experiments provided by RSM and ANN models. Both models were compared on the basis of prediction efficacies and deviation from actual data. The prediction data results demonstrated that the ANN model gave better prediction efficiency and lower prediction deviation than the RSM model. The ANN model provided a higher coefficient of determination and lower error values than the RSM model. Moreover, the catalyst exhibited a good stability and recyclability up to four reaction cycles.     

3-氯-2-羟丙基三甲基氯化铵一步合成动力学研究

研究了以右旋环氧氯丙烷和三甲胺盐酸盐为原料的3-氯-2-羟丙基三甲基氯化铵一步法合成,提出了用旋光度监测反应进度的方法并建立旋光度与反应进度的关系曲线,建立了幂函数型表观动力学模型,进行了动力学实验,经过数学变换,利用MATLAB软件中的regress函数程序进行了参数估计和F统计检验,得到了3-氯-2-羟丙基三甲基氯化铵合成的动力学方程并验证了其合理性。     

数据挖掘在稀土掺杂纳米TiO2结构与光催化活性关系中的应用

采用溶胶-凝胶法制备了不同稀土、不同掺杂量的纳米TiO2光催化剂,对其进行了XRD分析,并在紫外光源下对其降解甲基橙的光催化活性进行了测定;并藉此建立了构效关系,进行了数据挖掘。结果表明,随着掺杂量（Ln/Ti摩尔比率,Ln=Eu、Ce、Y）的增加,稀土掺杂改性的纳米TiO2的衍射峰逐渐宽化、强度逐渐减弱;最佳掺杂量为0.05%,掺杂量过多时,光催化效果反而下降;用多元逐步回归分析挖掘数据样本间的联系,发现结构参数（晶胞参数a、晶格畸变ε、晶粒尺寸D）与光催化剂的表观速率常数k间存在良好的相关性,其关系式为：k=-57.6879＋37.5097a＋68.3791ε＋0.0193 D-29.6588ε2,相关系数大于0.99,说明该模型具有良好的稳定性和预测能力。