| With exhaust of crude oil, it becomes a main topic to find another energy. Because of larger reserves, nature gas would be replace oil to become main chemical raw material in the next years.Undoubtedly, by using methane in natural gas as reactant, it will become a strategic topic to synthesize ethylene, which is the most important raw material in chemical industry at the present time. But it should be known that design of catalyst is key to develop the chemical process. By reviewing a great deal of catalysts developed by the former researchers, it shows that the catalytic system contained 2-3 chemical elements could not get better catalytic performance, and the catalyst, which contains some interactional transition metal elements, is assistant-catalyzed by corresponding elements arM is modified by alkali metal ions, could show better performance in oxidative coupling of methane.By reviewing the main components in catalysts of Syngas, it could be found that transition metal elements could activate methane molecule efficiently, so. Zr and Mn were chosen as main components of the developing catalyst. At the same time, S, P and W were chosen as assistant-catalyst components and the catalyst was modified by some alkali metal ions in order to suppress deep oxidation of C2. A multi-component catalyst is designed based on the above considerations.25 multi-element catalysts for oxidative coupling of methane were designed by orthography-design method. And 25 catalysts were tested on a fixed-bed micro-reactor without inert gases as thinner. A better catalyst was found in these catalysts. Experimental results showed that, the Cl]4 conversion would be 21.3 8% and C2 selectivity would be 82.56% (C2 yield be 17.65%) when GHSV was 33313 rnFg%f', Cl]4 02 was 3 1 and reaction temperature was 1069 K, the catalytic active kept constant in 10 hr. A maximum CH4 conversion could be found when temperature increased gradually on some catalysts, which reason could be increasing of the parallel side-reactions. The influence of different element on catalytic performance was researched primarily.In order to design a best catalyst, artificial neural network and genetic algoritlun are applied to design a multi-component catalyst for oxidative coupling of methane. Especially for multi-component catalyst, the relations of different components m catalyst could be complex and high nonlinear, and could not be expressed by a certain analytic function. Artificial neural network is a high-nonlinear system and have stronger map function. If the complex internal relations of catalyst components couldbe expressed by a certain neural network, the relations between catalyst components and catalytic reaction results (such as conversion of reactant and selectivity of product) would be established by the neural network which have a certain structure, such as the number of hidden layer(s) and the number of neurons. The relations expressed by neural network can be seen as a robust catalytic reaction model. Then SWIFT (Sequential Weight Increasing Factor Technique) method which could find local maximum, and a hybrid genetic algorithm which could get global maximum, would be applied to design some better catalysts. Since the number of catalysts in the training group is not larger enough to obtain the best generalization ability for the neural network at the beginning of design, it is unavoidable to adjust the weight matrix of the trained neural network. The results of first optimization should be added to the training group, and the neural network should be trained again by using the weight matrix obtained in first training as initial weight matrix. Based on the adjusted model, some other better catalysts would be designed by SWIFT method and hybrid genetic algorithm. Rest may be deduced by analogy until the predicted results of neural network approach the experimental results of the optimal catalyst.Back-propagation algorithm, which is a sort of neural network, is widely applied in many fields, and also used in this paper. Bu...
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