Establishing structure-property correlations and classification of base oils using statistical techniques and artificial neural networks

The present paper describes various classification techniques like cluster analysis, principal component (PC)/factor analysis to classify different types of base stocks. The API classification of base oils (Group I-III) has been compared to a more detailed NMR derived chemical compositional and molecular structural parameters based classification in order to point out the similarities of the base oils in the same group and the differences between the oils placed in different groups. The detailed compositional parameters have been generated using and nuclear magnetic resonance (NMR) spectroscopic methods. Further, oxidation stability, measured in terms of rotating bomb oxidation test (RBOT) life, of non-conventional base stocks and their blends with conventional base stocks, has been quantitatively correlated with their NMR and elemental (sulphur and nitrogen) data with the help of multiple linear regression (MLR) and artificial neural networks (ANN) techniques. The MLR based model developed using NMR and elemental data showed a high correlation between the ‘measured’ and ‘estimated’ RBOT values for both training (R=0.859) and validation (R=0.880) data sets. The ANN based model, developed using fewer number of input variables (only NMR data) also showed high correlation between the ‘measured’ and ‘estimated’ RBOT values for training (R=0.881), validation (R=0.860) and test (R=0.955) data sets.     

A neuro-fuzzy system for X-ray spectra interpretation

A decision scheme for the interpretation of spectra from wavelength dispersive X-ray fluorescence spectrometry is described that encompasses elements from three areas of artificial intelligence: fuzzy logic, rule based expert systems and neural net technology.After transforming the recorded spectra to line spectra by appropriate background correction a reasoning scheme is applied that takes into account not only the observed spectra, but also the recording conditions and prior spectroscopic information regarding the relative emission probabilities and the usefulness of the different lines for the purpose of element identification. The latter is done on the basis of a previously described scheme to compute conditional a posteriori Bayes probabilities for a mean matrix. These different pieces of information are then assembled into a battery of fuzzy rules. The importance of the rules as well as the importance of the X-ray lines is determined in a training process, similar to the one in a feedforward back-propagation network.To further stabilize the results this network is pruned in a second training cycle. This, however, had little effect on the quality of interpretation.The advantages of this approach to the interpretation of X-ray spectra over older ones are numerous: the system adapts itself to better interpret spectra that are of greater importance to a laboratory as these are better represented in the training set; the fuzzy logic is capable of working with incomplete and uncertain knowledge, and the neural network results based on these fuzzy rules is readily interpretable by the X-ray spectroscopist as every rule can be expressed also in natural language as in any classical rule based system.On leave from Silesian University, Katowice, Poland     

Irreversible deformation of macromolecular networks

Based upon a thermodynamical approach, the generalized Onsager type of relaxation of van der Waals networks is presented. By linearly and identically coupling the set hidden variables to the network, the memory function of the system can be related to the equilibrium strain-energy function. The relaxation behavior of real networks on stretching can quantitatively be described by means of a distribution of relaxation times known from small strain experiments. Some new and interesting conclusions are discussed as to how the macroscopically non-linear visco-elastic response might be interpreted.     

Phenomenological and neural-network modeling of cephalosporin C production bioprocess

Cephalosporin C production process withCephalosporium acremonium ATCC 48272 in synthetic medium was investigated and the experimental results allowed the development of a mathematical model describing the process behavior. The model was able to explain fairly well the diauxic phenomenon, higher growth rate during the glucose-consumption phase, and the production occurring only in the sucrose-consumption phase. Moreover, the process was simulated utilizing the neural-networks technique. Two feed-forward neural-networks with one hidden layer were employed. Both models, phenomenological and neural-networks based, satisfactorily describe the bioprocess. The difficulties in determining kinetic parameters are avoided when neural networks are utilized.     

EQUILIBRIUM SWELLING AND MUTUAL ENTANGLEMENTS IN POLY(VINYL ACETATE)/POLY(METHYL ACRYLATE) IPNS

Based on Thiele-Cohen model, a swelling equation was derived by introducing the interaction parameters of a three-component system of polymer Ⅰ-polymer Ⅱ-solvent, which is a reasonable representation of the swelling behavior of general homogeneous IPNs. The degrees of equilibrium swelling of polyvinyl acetate/polymethyl actylate (PVAc/PMA) IPNs were compared between the theoretical and experimental values. The obvious effects of mutual entanglements were observed.     

ANN原子参数法预报合金相晶型及晶格常数

运用人工神经网络方法，以二元合金组成元素的电子结构为基本特征，对二元合金相晶型作了区分，进一步对其晶格常数作了预报，结果令人满意。     

Simulation of aerated lagoon using artificial neural networks and multivariate regression techniques

The aim of this study was to develop an empirical model that provides accurate predictions of the biochemical oxygen demand of the output stream from the aerated lagoon at International Paper of Brazil, one of the major pulp and paper plants in Brazil. Predictive models were calculated from functional link neural networks (FLNNs), multiple linear regression, principal components regression, and partial least-squares regression (PLSR). Improvement in FLNN modeling capability was observed when the data were preprocessed using the PLSR technique. PLSR also proved to be a powerful linear regression technique for this problem, which presents operational data limitations.     

加氢脱氮催化研究的新进展

化石燃料的加氢脱氮有利于改善油品质量及其稳定性，同时避免燃烧时NO_x的排放。本文介绍了不同化石燃料中有机氮化物的含量及类型，对不同加氢脱氮催化剂及其催化活性位的本质进行了探讨，同时论述了C—N键断裂机理及燃料中主要有机氮化物的HDN反应网络。着重概述了传统金属硫化物催化剂的改性方法，新型的金属碳化物、金属氮化物和金属磷化物催化剂的研究现状。     

Three-dimensional structural features and the toxicity of aminobenzenes and phenols

There are many organic pollutants in the environment, such as polychlorinated biphenyl, polycyclic aromatic hydrocarbons, dichlorodiphenyl-trichloroethane (DDT), and polychlorinated naphthalene. These organic pollutants are persistent,liposoluble and easily cumulated in organism; consequently, the potential toxicity will be high. Risk assessment of industrial chemicals is currently carried out using scanty experimental data, because many of these chemicals have very little or no test data. S…