Wolfram Heineken

Hydrogen storage plays a crucial role in enabling its large‐scale adoption as an energy carrier. This study examines the technical and economic aspects of storing hydrogen in 200‐bar pressure vessels. It focuses on the impact of different transportation methods, including 350‐bar trailers, 540‐bar trailers, and pipelines, on storage performance and costs. Key factors analyzed include pressure and temperature variations during the filling process, the levelized cost of hydrogen storage, and the combined levelized cost of hydrogen transportation and storage. The results indicate that higher mass flow rates lead to increased temperature fluctuations, requiring careful control to maintain safe operating conditions. Additionally, while the levelized storage costs decrease with increasing hydrogen demand, the choice of transportation method significantly influences the total cost. The analysis shows that 350‐bar trailers offer the most cost‐effective transport and storage solution for short to medium distances, whereas, 540‐bar trailers become more viable at longer distances. These insights provide a foundation for optimizing hydrogen infrastructure, balancing cost and efficiency in storage and distribution.

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.

The global energy landscape faces significant challenges, necessitating a transition toward alternative energy sources to mitigate energy consumption and greenhouse gas emissions. Hydrogen emerged as a versatile energy carrier with immense potential, particularly in the realm of transportation. However, the lack of a comprehensive hydrogen infrastructure poses a critical barrier to its widespread adoption. By examining various important factors including the pressure level, transported capacity, trailer capacity, pipeline diameter, pressure drop, and transport distance, this study aims to optimize the hydrogen supply chain and improve the cost‐effectiveness of compressed hydrogen gas transportation. Five major delivery modes, encompassing compressed gas trucks and pipelines of varying diameters, are evaluated. This research offers valuable insights into the costs associated with hydrogen delivery within the European context. It reveals that the total levelized cost of hydrogen transportation (LCOHT) ranges from 0.3 to 3.44 € kg⁻¹. These cost fluctuations are observed over transport distances that extend from 25 to 500 km and are dependent upon varying hydrogen demand levels, encompassing quantities of up to 100 000 kg per day. Based on the results, optimal choices for transport infrastructure are identified considering factors such as distance and hydrogen demand.

Efficient train driving plays a vital role in reducing the overall energy consumption in the railway sector. An energy minimising control strategy can be computed using the framework given by optimal control theory; in particular, the Pontryagin maximum principle can be used. Our optimisation approach is based on an algorithm presented by Khmelnitsky that considers electric trains equipped with regenerative braking. A derivation of Khmelnitsky’s theory from a more general formulation of the maximum principle is given in this article, and a complete list of switching cases between different driving regimes is included that is essential for practical application. A number of numerical examples are added to visualise the various switching cases. Energy consumption data from real-life operation of passenger trains are compared to the calculated energy minimum. In the presented study, the optimised strategy was able to save 37 percent of the average energy demand of the train in operation. The sensitivity of the energy consumption to deviations of the train speed from the optimum speed profile is studied in an example. Another example illustrates that the efficiency of regenerative braking has an effect on the optimum speed profile.

Efficient train driving plays a vital role in reducing the overall energy consumption in the railway sector. An energy minimising control strategy can be computed using the framework given by optimal control theory, in particular the Pontryagin maximum principle. Our optimisation approach is based on an algorithm presented by Khmelnitsky that considers electric trains equipped with regenerative braking. A derivation of Khmelnitsky’s theory from a more general formulation of the maximum principle is given in this article, and a complete list of switching cases between different driving regimes has been included that is essential for practical application. A number of numerical examples are added to visualise the various switching cases. Energy consumption data from real-life operation of passenger trains are compared to the calculated energy minimum. In the presented study, the optimised strategy was able to save 37 percent of the average energy demand of the train in operation.

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.

Renewable energy technologies can help us combat climate change and hydrokinetic energy conversion systems could play a major role. The simplicity of hydrokinetic devices helps us to exploit renewable sources, especially in remote locations, which is not possible with conventional methods. A new type of hydrokinetic device called the Energy Conveyor Belt was designed, which works on the concept of conveyor belt technology. Numerical simulations are performed on the design of the Energy Conveyor Belt with Ansys FLUENT to optimize its performance. Some of the optimized models produced a maximum power slightly above 1 kW. The numerical results are then compared to the experimental results of other hydrokinetic turbines. The compactness and flexibility of the design give the Energy Conveyor Belt an advantage over other hydrokinetic devices in regions with fluctuating water levels. Further research has to be undertaken into cascading systems to increase the overall power generated by the system.