Modeling and optimization of synthesis parameters in nanostructure La1−xBaxNi1−yCuyO3 catalysts used in the reforming of methane with CO2

Highlight
- Some promoters, including Ba and Cu were added to LaNiO3 perovskite catalyst for evaluating the effects in dry reforming of methane.
- The catalyst series were designed by central composite design (CCD).
- In order to design the modified catalysts and to optimize the methane conversion, an artificial neural network (ANN) model was linked with genetic algorithm (GA).
- Genetic algorithm was used to find the optimal catalyst with minimum experiments.
- The optimum catalysts, La0.996Ba0.004Ni0.6Cu0.4O3 exhibited the highest methane conversion.

To achieve an efficient catalyst for the production of synthesis gas by dry reforming of methane a series of LaxBa1 − xNiyCu1 − yO3 perovskite-type oxides were synthesized with the sol−gel auto-combustion method. The reaction was carried out under continuous flow of feed stream which included CO2, CH4 and Argon as an internal standard, under atmospheric pressure. In order to design the modified catalysts and to optimize the methane conversion, an artificial neural network (ANN) model linked with genetic algorithm (GA) was applied. A high R2 value was obtained for training, validation and test sets of data: 0.99, 0.97 and 0.96 respectively. The model predicted that the maximum methane conversion was achieved via La0.996Ba0.004Ni0.6Cu0.4O3 (Tca = 700 °C). The catalysts were characterized by XRD and FE-SEM.

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