Meeting Germany’s climate targets urgently demands substantial investment in renewable energies such as hydrogen, as well as tackling industrial CO₂ emissions with a strong CO₂ transport infrastructure. This is particularly crucial for CO₂-heavy industries such as steel, cement, lime production, power plants, and chemical plants, given Germany’s ban on onshore storage. The CO₂ transport network is essential for maintaining a circular economy by capturing, transporting, and either storing or utilizing CO₂. This study fills gaps in CO₂ pipeline transport research, examining pipeline diameters, costs, and pressure drop, and providing sensitivity analysis. Key findings show that the levelized cost of CO₂ transport (LCO2T) ranges from 0.25 €/t to 55.82 €/t based on varying transport masses (1000 t/day to 25,000 t/day) and distances (25 km to 500 km), with compression costs pushing LCO2T to 33.21 €/t to 92.82 €/t. Analyzing eight pipeline diameters (150 mm to 500 mm) and the impact of CO₂ flow temperature on pressure loss highlights the importance of selecting optimal pipeline sizes. Precise booster station placement is also crucial, as it significantly affects the total LCO2T. Exploring these areas can offer a more thorough understanding of the best strategies for developing cost-effective, efficient, and sustainable transport infrastructure.

Through utilization of state-of-the-art power-to-x technology, biological methanation is a novel method to capture the intermittent electricity generated by renewable energy sources. In this process, biomass grows in a liquid solution by consuming H₂ and CO₂ and produces CH₄. This study aims to improve the accuracy and comprehensibility of an initial bio-methanation model by reviewing and comparing existing technologies and methods, correcting miswritten equations, adding complementary equations, and introducing a new initialization approach. In addition, a mean value approach was used for calculating the axial mixing coefficients. Gas–liquid mass transfer in the reactor, along with other aspects, is considered the most challenging aspect of the biological methanation process due to hydrogen’s low solubility. This highlights the need for a modeling approach to improve understanding and optimize the design of the process. The improved MATLAB code was used to test different variations of parameters in the reactor and observe their effects on the system’s performance. The model was validated using experimental cases, and the results indicate that it is more accurate than Inkeri’s for certain parameter variations. Moreover, it demonstrates better accuracy in depicting the pressure effect. The sensitivity analysis revealed that liquid recycle constant λ had little effect on methane concentration, while impeller diameter d_im and reactor diameter d_re had significant impacts. Axial mixing constants b₁ and b₂ and biological kinetics constants k_D, µ^max, and m_X had relatively small effects. Overall, the study presents a more comprehensive bio-methanation model that could be used to improve the performance of industrial reactors.