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Study On Modeling And Optimization For Heterogeneous Gatalysis Based On Chaos And Support Vector Regression

Posted on:2011-03-17Degree:DoctorType:Dissertation
Country:ChinaCandidate:X X HanFull Text:PDF
GTID:1118360305471350Subject:Circuits and Systems
Abstract/Summary:PDF Full Text Request
Modeling and optimization are ones of the central issues in engineering and technological research, which can be employed as a significant prerequisite for scientific decision-making and planning. However, prediction is the weakness for all technologies including heterogeneous catalysis. With the development of economy, particularly the requirements for new energy and materials, the heterogeneous catalysis faces new challenges. During the development of new efficient catalysts, besides economic, safety and multi-function, the study of catalysts properties from a whole chemical process is also required. Machine learning and data depth mining based on history data has become the most important problem to be solved in the area of chemical engineering information. The difficulty and three major problems to be solved in the area of catalysis are the kinetic model, the structure-activity relation model, as well as design and optimization of catalysts. It is of great practical importance not only in the investigation of catalyst characters, but in the whole procedure of the control, optimization and modeling of chemical production.In this study, chaos and support vector regression (SVR) were employed to the modeling and optimization of heterogeneous catalysis. The major research contents includes modeling prediction based on the adaptive chaos particle swarm optimization and support vector regression (ACPSO-SVR), the design of optimal optimization algorithms based on ACPSO-SVR, and the modeling prediction of chaotic time series based on phase space reconstruction and support vector regression (PSR-SVR). The present study crosses the boundaries of numerous scientific fields of information, automation and chemical engineering etc.The main contents and basic findings are as follows:(1) An effective relevance prediction algorithm based on ACPSO-SVR was presented. A heuristic optimization method was introduced to automatic selection of hyper-parameters in SVR. The forest fires standard data set of UCI machine learning database was selected to test. The experimental results showed that the new method has high relatively precision and good generalization ability with a wide range of parameter values, better than that of mesh searching algorithm. It could be used as an effective method to solve the problems of multivariate regression predication. The method was applied to modeling of the Cu-Zn-Al-Zr based catalysts for the synthesis of dimethyl ester (DME). Under the condition of with unknown kinetic model, the catalyst composition model and the kinetic model were obtained and gave good prediction results was produced.(2) A new optimization framework was built by combining multi-objective chaotic particle swarm optimization algorithm with SVR. Considering that it is very difficult to obtain the catalytic performance due to mutli-stage process and strong relevance in heterogeneous catalysis, the trained SVR Model was used as the fitness approximation model, through using multi-object ACPSO algorithm optimizing the space of input variable for searching catalyst with optimal catalytic performance. The optimum strategy was employed in the exploring of the Cu-Zn-Al-Zr based catalysts for DME synthesis. The two new types of catalysts obtained through ACPSO-SVR optimized scheme show catalytic performance close to the experimental value. In conclusion, this optimization method shorts time and reduces cost in catalyst exploration, and is exactly an effective method for catalyst development in laboratory.(3) A novel method for nonlinear time series forecasting of the catalyst deactivation based on PSR-SVR was presented. Considering the complicated mechanisms of catalysts deactivation multi-factors influencing the catalyst performance; moreover, the limitation of getting time series data during the deactivation process of catalysts leads to the modeling efficiency and prediction precision. The data presentation method of high dimensions phase space reconstruction was used to assessment of the deactivation data and the regularity of data recomposition is attained. Finally, the aim of disclosing the nature of complicated character of catalyst performance was realized. The novel method was applied to predict the deactivation process of the Cu-Si-Al based catalysts for the synthesis of dimethyl carbonate (DMC). The simulation results showed that the prediction error of catalyst deactivation model is in a range of tolerance. The prediction space time yield (STY) value of DMC could provide important information for the design and operation of reactors as well as the optimization of the reaction conditions.
Keywords/Search Tags:support vector regression, phase space reconstruction, chaos particle swarm optimization, modeling, optimization, heterogeneous catalysis
PDF Full Text Request
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