Modeling and optimization strategy for heterogeneous catalysis based on support vector regression and genetic algorithm
This paper presents a soft computing based heterogeneous catalysis modeling and optimization strategy, namely SVR-GA,for the discovery and optimization of dimethyl ether synthesis on new catalytic materials.In the SVR-GA approach,a support vector regression model is constructed for correlating process data comprising values of input variables of catalyst compositional,operating conditions and output variables of performance of catalyst.Next, model inputs variables are optimized using genetic algorithms(GAs) with a view to maximize the performance of catalyst. Moreover,the SVR model is employed as an approximate model for fitness function in SVR-GA architecture.The SVR GA is a novel strategy for heterogeneous catalysis modeling and optimization. The major advantage of the hybrid strategy is that modeling and optimization can be conducted exclusively from the historic small sample space data wherein the detailed knowledge of process phenomenoiogy(reaction mechanism, rate constants,etc.) is not required and difficult to get, and simultaneously constructed for the Cu-Zn-AI-Zr slurry catalysts compositional model and kinetic model in the synthesis of DME.Finally, new catalysts,the optimum compositions and optimum preparation conditions leading to maximized CO conversion and DME selectivity were obtained. The optimized solution was verified experimentally to be feasible.
support vector regression genetic algorithm heterogeneous catalysis modeling multi-objective optimization
HAN Xiaoxia XIE Jun REN Jun XIE Keming
College of Information Engineering Taiyuan University of Technology Taivuan,030024,China College of Information Engineering Taiyuan University of Technology Taivuan,030024, China Key Laboratory of Coal Science and Technology,Ministry of Education Taiyuan University of Technology
国际会议
长沙
英文
1046-1050
2009-10-10（万方平台首次上网日期，不代表论文的发表时间）