Performance Analysis of Methane–Hydrogen Mixture Transportation in Pipelines Using Aspen Plus and Artificial Neural Networks

Abstract

A suitable method for hydrogen transmission is to blend it with methane gas. This study explored the pressure drop and energy required of methane–hydrogen pipelines under various conditions using Aspen Plus and artificial neural network (ANN) models. The integration of Aspen Plus and ANN was highly effective in analyzing the performance of methane–hydrogen pipelines. The results of this study showed that pipeline diameter and hydrogen mole fraction had the most significant impact on pressure drop and energy required compared to other factors. The effect of adding hydrogen on pressure drop and energy required decreased as the pipeline diameter increased. The impact of hydrogen addition remained relatively constant across varying pipeline lengths, surface roughness, and mass flow rates. Additionally, the effect of adding hydrogen was significantly less in vertical pipelines compared with horizontal pipelines. At lower inlet pressures, the impact of hydrogen addition on pressure drop and energy required diminished. Inlet temperature had minimal effects on pressure drop and energy required across varying hydrogen mole fractions. Furthermore, the heat transfer coefficient and ambient temperature had negligible effects on pressure drop and energy required. These findings demonstrated the feasibility of incorporating hydrogen into natural gas pipelines and highlighted the adaptability of pipeline systems to various operational and environmental conditions.