NEURAL NETWORK MODELING OF TRANSITION METAL - ZEOLITE EXHAUST CATALYSTS

Recent research in automobile exhaust catalysts has addressed the substitution of platinum-group metals Pt, Pd and Rh by metals such as Cu, Co, Ag, Zn, Mn and Sr exchanged or impregnated on zeolites, TiO₂ or Zro₂ carriers. These catalysts have the potential of becoming good alternatives to the commercial three-way catalysts to convert pollutant hydrocarbons (HC), carbon monoxide (CO) and nitrogen oxides (NO_x). This paper describes a technique based on neural networks, to correlate the catalyst synthesis variables and resulting exhaust conversion. The optimum catalyst composition and operating conditions for a specified exhaust conversion are determined

A back-propagation algorithm was used to train the feed-forward neural network consisting of two hidden layers with 45 and 60 neurons in the first and second hidden layers respectively, with optimum values of the learning factor and momentum gain coefficient. The effects of the operating and compositional parameters on NO_x conversion by Cu-ZSM-5 were found. The optimum conversion was predicted for Si/Al atom ratio in the range 30-35, Cu-loading (in Cu-ZSM-5) of 1·1 - 1.2% of the zeolite weight, and an operating temperature of 650-675  K. The rare-earth metals (Ce, Cs and La) that act as promoters for three-way catalysts did not have a considerable effect on the exhaust conversion. The conversion increased by at least 10% when Co is used as a co-cation in Cu-ZSM-5.