Optimization of Yield and Conversion Rates in Methane Dry Reforming Using Artificial Neural Networks and the Multiobjective Genetic Algorithm

This research aimed to enhance the dry reforming of methane by integrating computational fluid dynamics (CFD), artificial neural network (ANN), and multiobjective genetic algorithm (MOGA) techniques. Through a comprehensive analysis of reactor setups using computational fluid dynamics, reliable data were generated. Machine learning models based on the ANN were then trained with this data to establish connections between the yield, the conversion rates, the flow rates, the carbon content, and the input parameters. The trained ANN models were subsequently employed as an objective function for the MOGA, enabling the identification of optimized input parameter values that maximize the conversion rates, yields, and flow rates and minimize the carbon content. The optimization results from the MOGA revealed that in the low flow rate regime, the optimal input parameters for the reactor performance fell within a U_g range of 0.08-1.0 m/s with corresponding h/H values of 0.37 and 0.48. In the high flow rate regime, the Pareto front analysis unveiled that the optimal U_g values ranged from 1.5 to 1.6 m/s accompanied by corresponding h/H values ranging from 0.5 to 0.8. Furthermore, the study identified temperature values between 814 and 817 m/s as the optimal range for achieving a balanced compromise between the coke content and the conversion rates/yields. Overall, this research provides valuable insights into optimizing the dry reforming process, highlighting the effectiveness of the ANN models, the significance of specialized methods for optimization, and the relationship between the input parameters and the performance indicators. These findings contribute to the development of more efficient and productive reactors for the dry reforming of methane.