Design and optimization of new La_1−xCe_xNi_1−yFe_yO₃ (x, y = 0–0.4) nano catalysts in dry reforming of methane

ABSTRACT

The paper reports the production of syngas from dry reforming of methane (DRM) over La_1−xCe_xNi_1−yFe_yO₃ (x, y = 0–0.4) perovskites. A series of La_1−xCe_xNi_1−yFe_yO₃ were designed by central composite design (CCD) and synthesized by a sol–gel auto combustion method. Artificial neural network (ANN) approach was used to determine the relationship between preparation and operational parameters on the performance of the catalysts in the DRM process. Nickel mole fraction, lanthanum mole fraction, calcination temperature, and reaction temperature were considered as input variables, and conversion of methane was considered as the output variable. An ANN model with nine neurons in the hidden layer was the suitable in predicting conversion of methane. The genetic algorithm (GA) was subsequently used to determine the optimal preparation condition for enhancing the conversion of methane. La_0.6Ce_0.4Ni_0.99Fe_0.01O₃ catalyst, calcined at 756°C was obtained to be the most active catalyst owing to the optimal composition of nickel and lanthanum in the catalyst formulation.

KEYWORDS:

• Perovskite catalyst
• dry reforming of methane
• neural network
• genetic algorithm
• optimization

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