Prediction Research On Open-framework Aluminophosphate Syntheses

The applications of different microporous inorganic materials have direct and close relations to their porous structures. For example, the differences of dimensionality, shape and the volume of pore will result in huge difference in applications. Microporous inorganic crystals have been widely used in the fields of catalysis, adsorption separation and ion-exchange because of their unique and regular pore structures. Therefore, the design and synthesis of microporous crystals with novel structures, and the development of new synthesis routes are always being concerned. Among them, owing to their structural diversities and potential applications, open-framework metal phosphate compounds have been extensively and deeply studied by many domestic and abroad scholars. The synthesis of microporous inorganic crystals is very complex and the crystalline of materials is affected by many factors such as the source materials, the gel composition, the PH value, the template, the solvent, the crystallization temperature and time etc. For the synthesis research and analysis of these materials, it is difficult to control the process of synthesis, understand and model their complex crystallization kinetics. In the past years, researchers have tried to establish prediction models of new synthesis methods. Specially, they applied some statistical methods to the rational designs of target materials in order to obtain good prediction model for specific structure, which were used for the synthesis of new materials. Although these statistical methods have been widely employed and obtained good predictive results in chemical material analysis, the study on analysis and prediction of open-framework aluminophosphates (AlPOs) is not enough.In view of the rich chemical structure of open-framework AlPOs, the theories and methods of machine learning based on statistics are employed to analyze and predict the structures of AlPOs molecular sieves in this thesis. The methods are mainly applied to mine the influence of synthesis parameters to predict the some resultant structures and provide rational interpretation of the formation specific structure, establish the prediction model of synthesis parameters to resultant pore ring and type for enhancing the rate of success of rational synthesis experiments. The detailed study is divided into the following two parts:Part I: A series of analysis and prediction works are done between the synthesis parameters and the resultant structures using machine learning methods based on statistics on the AlPOs database described as follows.1. On account of the severe correlation among the synthesis parameters, partial least squares (PLS) which can deal with the problem of severe correlation among variables is employed to analyze the influence of synthesis parameters to predict the resultant specific structures. Furthermore, principal component analysis (PCA) is used to extract the synthetic information of some resultant specific structures to establish the regression model of synthesis parameters to resultant specific structures.2. Under the condition of using the same template for synthesis, back propagation neural networks (BPNNs) is adopted to analyze the influence of the gel compositions and their combinations to predict the resultant type.3. Since the support vector machine (SVM) can solve the problems of nonlinear, high dimensionalities and local minimum points, it is adopted to predict the resultant pore ring and type. Also, the influence of template attributes for predicting the material with specific pore ring and type is mined. Moreover, the cross validation is adopted to further enhance the reliability of classifier.4. To avoid the limitation that variables can not be serious correlation in the multiple linear regressions (MLR), the ridge regression (RR) is used to establish the prediction model of synthesis parameters to resultant type. In addition, the effect on prediction performance for the selection of ridge parameter and threshold is studied in detail.5. A statistical method combining PLS and logistic regression (LR), named as PLS-LR, is also adopted in this thesis to accomplish the prediction of synthesis parameters to resultant type. First, the correlation among synthesis parameters is removed using PLS to obtain new low dimensional variables. Then, LR is used to predict the resultant type based on low dimensional variables. Finally, the number of components in PLS is determined through analyzing the effect on prediction results with different number of components.Extensive experiments and analysis domonstrate that the machine learning methods based on statistics can mine the influence of synthesis parameters to the specific resultant structures and establish good prediction model of synthesis parameters to resultant specific structure and type.Part II: Aiming to solve the problem of class imbalance existing in the AlPOs database, novel resampling methods are proposed.The class imbalance will degrade the classification performance of classifier. Owing to the existence of class imbalance in the predictive experiments (such as the ratio of two classes is 1: 3), this thesis proposes two guided over-sampling methods on the basis of on fuzzy c-means (FCM), named as FCMP1 and FCMP2, and two guided combined-sampling methods, named as FCMP1+Tomek and FCMP2+Tomek. These methods not only consider the inter-class imbalance but also the intra-class imbalance to overcome shortcoming of blind resampling methods. Moreover, the combined-sampling methods remove the noisy or borderline samples for both classes simultaneously, which results in the two classes more discriminative. The experimental results demonstrate the predictive results on sampled dataset are better than the results on original dataset. Furthermore, compared with some existing resampling methods, our proposed resampling methods exhibit much better predictive results.In this thesis, machine learning methods based on statistics are employed to establish a series of predictive models of synthesis parameters to resultant specific structure on AlPOs database. To solve the problem of data class imbalance effectively, novel resampling methods are proposed to improve the predictive performance. The research of this thesis will make the rational design of molecular sieves framework more straightforward and efficient, and reduce the experimental cost. In particular, this work will provide important guiding significance for rational designing the molecular sieves framework with specific structures.