Abstract
Preparation conditions of Co-MgO catalyst for methane dry reforming were optimized to maximize the CO yield. Response surface method, composed of design of experiment and regression method, was applied. For the best regression, genetic programing, radial basis function network, and polynomial equation were compared. As a result, genetic programing succeeded to express explicitly the non-linear relationship between catalyst preparation parameters and catalyst performance, and the prediction error was the least. Genetic programing modifies and optimizes many functions by genetic algorithm to fit the experimental results. Radial basis function network could express the non-linear relationship, but the complexity of the function hindered the understanding of the importance of catalytic parameters. While polynomial equation proposed the explicit equation, the accuracy was not sufficient to show the non-linear relationship. Genetic programing is the most suitable regression method of response surface for the catalyst development.
