Quantitative Prediction of Normal Boiling Points for Organic Compounds Using Gas Chromatographic Retention Times on Two Columns of Different Polarity

Quantitative relationships between normal b.p.s and gas chromatographic retention times of 271 organic compounds of diverse structures was studied using a multivariate regression method and statistical importance evaluation. It was found that satisfactory modelling cannot be obtained using only retention times on a single column. Even dividing the 271 compounds into four groups according to their chemical structures, only hydrocarbons on nonpolar columns gave good results with a linear model. However, if the retention times on two columns of different polarity are used to build the model, a satisfactory two-parameter model can be obtained for the whole data set with a squared correlation coefficient (R ²), standard deviation (s) of the prediction, and probability value (P) of: 0.9456, 16.1K and 1.7×10^?150, respectively. The results also show that the statistical importance of the two-parameter model is much better than that of the one-parameter model.     

Enhanced Adsorption of Hexavalent Chromium onto Magnetic Calcium Ferrite Nanoparticles: Kinetic,Isotherm, and Neural Network Modeling

Calcium ferrite nanoparticles with super-paramagnetic behavior were synthesized via simple chemical precipitation method for effective removal of hexavalent chromium from aqueous media. The properties of synthesized nanoparticles were studied by X-ray diffraction (XRD), field emission scanning electron microscope (FESEM), Fourier transform infrared (FTIR) spectroscopy, Brunauer-Emmett-Teller (BET), and vibrating sample magnetometer (VSM) measurements. The ferrite nanoparticles have shown polycrystalline nature and high BET specific surface area (229.83 m²/g) with active functional groups on the surface. The adsorption process follows second-order kinetics with the involvement of intra-particle diffusion and adsorption capacity as much as 124.11 mg/g was determined from the Langmuir isotherm. The thermodynamic analysis revealed that the adsorption process was feasible, spontaneous, and exothermic in nature. A three-layer feed-forward back-propagation artificial neural network (ANN) model was employed to predict the removal (%) of Cr(VI) ions as output. Optimal ANN network (4:8:1) shows the minimum mean squared error (MSE) of 0.00161 and maximum coefficient of determination (R²) of 0.984. The adsorption process is mostly influenced by solution pH and followed by adsorbent dosage, initial Cr(VI) concentration, and contact time as illustrated by sensitivity analysis. With small size and high surface area, biocompatibility, ecofriendly nature, easy magnetic separation, and enhanced adsorption capacity towards Cr(VI), calcium ferrite nanoparticles will find its potential application in wastewater remediation.     

Experimental investigation and modeling of thermal conductivity of CuO–water/EG nanofluid by FFBP-ANN and multiple regressions

The purpose of this study is to predict the thermal conductivity of copper oxide (CuO) nanofluid by using feed forward backpropagation artificial neural network (FFBP-ANN). Thermal conductivity of CuO nanofluid is measured experimentally using transient hot-wire technique in temperature range of 20–60 °C and in volume fractions of 0.00125, 0.0025, 0.005 and 0.01% for neural network training and modeling. In addition, in order to evaluate accuracy of modeling in predicting the coefficient of nanofluid thermal conductivity, indices of root-mean-square error, coefficient of determination (R ²) and mean absolute percentage error have been used. FFBP-ANN with two input parameters (volume fraction and nanofluid temperature) and one output parameter (nanofluid thermal conductivity) in addition to two hidden layers and one outer layer which purelin, logsig and tansig functions are used was considered as the most optimum structure for modeling with neuron number of 4–10–1. In this study, among common methods of theoretical modeling of nanofluid thermal conductivity, theoretical method of Maxwell and also multivariate linear regression model was used for explaining the importance of modeling and predicting the results using neural network. According to this research, the results of indices and predictions show high accuracy and certainty of ANN modeling in comparison with empirical results and theoretical models.     

基于启发式方法和支持向量机方法预测有机物的热导率

构建147个有机物分子结构与其热导率值之间的定量结构-性质关系(QSPR)模型, 探讨影响有机物热导率的结构因素. 以147个化合物作为样本集, 随机选择118个作为训练集, 29个作为测试集. 应用CODESSA软件计算了组成、拓扑、几何、静电和量子化学等描述符, 通过启发式方法(HM)筛选得到5个结构参数并建立线性回归模型; 用所选5个结构参数作为支持向量机(SVM)的输入, 建立非线性的支持向量机回归模型. 预测结果表明: 支持向量机回归模型的性能(复相关系数R²=0.9240)虽略低于启发式回归模型的性能(R²=0.9267), 但是支持向量机方法预测性能(R²=0.9682)高于启发式方法的预测性能(R²=0.9574), 对于QSPR模型来说, 预测性能更重要. 因此, 总体来说支持向量机方法优于启发式方法. 支持向量机方法和启发式方法的提出为工程上提供了一种根据分子结构预测有机物热导率的新方法.     

Artificial intelligence techniques for modeling and optimization of the HDS process over a new graphene based catalyst

A Co-Mo/graphene oxide (GO) catalyst has been synthesized for the first time for application in a defined hydrodesulfurization (HDS) process to produce sulfur free naphtha. An intelligent model based upon the neural network technique has then been developed to estimate the total sulfur output of this process. Process operating variables include temperature, pressure, LHSV and H₂/feed volume ratio. The three-layer, feed-forward neural network developed consists of five neurons in a hidden layer, trained with Levenberg–Marquardt, back-propagation gradient algorithm. The predicted amount of residual total sulfur is in very good agreement with the corresponding experimental values revealing a correlation coefficient of greater than 0.99. In addition, a genetic algorithm (GA) has been employed to optimize values of total sulfur as well as reaction conditions.     

Quantitative Structure�CRetention Relationship Study on Nitrogen-Containing Polycyclic Aromatic Compounds by Using Molecular Electronegativity Interaction Vector

The molecular structures of 117 nitrogen-containing polycyclic aromatic compounds (N-PACs) were described by a method of molecular structural characterization (MSC) called molecular electronegativity interaction vector (MEIV). The samples were divided into a training set and a test set. For the training set, a quantitative structure?Cretention relationship (QSRR) model was built up by multiple linear regression (MLR) and the model was evaluated by performing the cross validation with the leave-one-out (LOO) procedure. The correlation coefficient (R) and the cross-verification correlation coefficient (R _CV) of the model were 0.992 and 0.991, respectively. Moreover, the model was evaluated by the test set and satisfactory results with a correlation coefficient (R _test) of 0.993 were obtained. The results suggested good stability and predictability of the model.     

Prediction of anti-HIV activity and cytotoxicity of pyrimidinyl and triazinyl amines: A QSAR study

A QSAR study on a series of pyrimidinyl and triazinyl amines was performed to explore the physico-chemical parameters responsible for their anti-HIV activity and cytotoxicity. Physico-chemical parameters were calculated using WIN CAChe 6.1. Stepwise multiple linear regression analysis was carried out to derive QSAR models which were further evaluated for statistical significance and predictive power by internal and external validation. The selected best QSAR models showed correlation coefficient R of 0.914 and 0.901, and cross-validated squared correlation coefficient Q ² of 0.685 and 0.691 for anti-HIV activity and cytotoxicity, respectively. The developed significant QSAR model indicates that hydrophobicity of the whole molecule plays an important role in the anti-HIV activity and cytotoxicity of pyrimidinyl and triazinyl amine derivatives. When hydrophobicity is increased, anti-HIV activity of the present series of compounds is decreased leading to high cytotoxicity.     

Cytotoxicity and 2D-QSAR study of some heterocyclic compounds

Herein we have studied the cytotoxicity and quantitative structure–activity relationship (QSAR) of heterocyclic compounds containing cyclic urea and thiourea nuclei. A set of 22 hydantoin and thiohydantoin related heterocyclic compounds were investigated with respect to their LC₅₀ values (Log of LC₅₀) against brine shrimp lethality bioassay in order to derive the 2D-QSAR models using MLR, PLS and ANN methods. The best predictive models by MLR, PLS and ANN methods gave highly significant square correlation coefficient (R²) values of 0.83, 0.81 and 0.91 respectively. The model also exhibited good predictive power confirmed by the high value of cross validated correlation coefficient Q² (0.74).     

Artificial Neural Network (ANN) Method for Modeling of Sunset Yellow Dye Adsorption Using Nickel Sulfide Nanoparticle Loaded on Activated Carbon: Kinetic and Isotherm Study

In this paper, the NiS nanoparticles are prepared and characterized by x-ray powder diffraction and scanning electron microscopy. The NiS nanoparticles showed the excellent adsorption properties toward sunset yellow (UA) dye. The effect of solution pH, adsorbent dosage (0.005–0.020 g), contact time (0.5–30 minutes), and initial UA concentration (5–40 mg L^?1) on the extent of adsorption was investigated and modeled by artificial neural network. The experimental equilibrium data was analyzed by Langmuir, Freundlich, Tempkin, and D–R isothermal models. It was seen that the data was well presented by Langmuir model with a maximum adsorption capacity of 333.3 mg g^?1 at 26°C. Kinetic studies at various adsorbent dosages and initial UA concentrations show that high removal percentage (>90%) was achieved within 15 minutes. The adsorption of UA follows the pseudo-second-order rate model. The experimental data were applied to train the multilayer feed-forward neural network with three inputs and one output with Levenberg–Marquart algorithm and different numbers of neurons in the hidden layer. The minimum mean square error of 0.0003 and determination coefficient of (R²) 0.99 were found.     

Prediction of CO2 Solubility in Polymers by Radial Basis Function Artificial Neural Network Based on Chaotic Self-adaptive Particle Swarm Optimization and Fuzzy Clustering Method

To replace costly and time-consuming experimentation in laboratory, a novel solubility prediction model based on chaos theory, self-adaptive particle swarm optimization （PSO）, fuzzy c-means clustering method, and radial ba- sis function artificial neural network （RBF ANN） is proposed to predict CO2 solubility in polymers, hereafter called CSPSO-FC RBF ANN. The premature convergence problem is overcome by modifying the conventional PSO using chaos theory and self-adaptive inertia weight factor. Fuzzy c-means clustering method is used to tune the hidden centers and radial basis function spreads. The modified PSO algorithm is employed to optimize the RBF ANN connection weights. Then, the proposed CSPSO-FC RBF ANN is used to investigate solubility of CO2 in polystyrene （PS）, polypropylene （PP）, poly（butylene succinate） （PBS） and poly（butylene succinate-co-adipate） （PBSA）, respec- tively. Results indicate that CSPSO-FC RBF ANN is an effective method for gas solubility in polymers. In addition, compared with conventional RBF ANN and PSO ANN, CSPSO-FC RBF ANN shows better performance. The values of average relative deviation （ARD）, squared correlation coefficient （R2） and standard deviation （SD） are 0.1071, 0.9973 and 0.0108, respectively. Statistical data demonstrate that CSPSO-FC RBF ANN has excellent prediction capability and high-accuracy, and the correlation between prediction values and experimental data is good.     

Application of Artificial Neural Networks for Analysis of 13C NMR Spectra of Fucoidans

A new approach for structure determination of native and O-desulfated fucoidans by the analysis of their ¹³C NMR spectra by artificial neural networks (ANNs) is described. Two ANN models were studied: the simple three-layer feed-forward network, which employs supervised learning, and the adaptive resonance theory (ART) network with unsupervised learning. Training sets for the networks were constructed using chemical shifts of synthetic oligofucosides. The results obtained demonstrate that both models worked better in the case of desulfated fucoidans, while the ART-type networks gave better results in sulfated (native) fucoidan structure elucidation.