Combined Two-Model CFD Simulation of Trickle Bed Reactors with Head-Sump Extension: Case Study on Hydrodynamics and Biological Methanation - Markthaler, Simon; Plankenbühler, Thomas; Miederer, Jonas; Kolb, Sebastian; Herkendell, Katharina; Karl, Jürgen (Industrial & Engineering Chemistry Research, 2022)
Trickle bed reactors are one of the world’s most employed technologies for multiphase processing, and they have been scrutinized for decades. However, accurate prediction and scale-up of trickle bed reactors are still challenging owing to complex interactions of multiphase flow, interfacial mass transfer, and reaction kinetics. The present computational model provides an insight into process phenomena on the basis of Eulerian–Eulerian methodology. It captures all parts of a trickle bed reactor─packing, head, and sump─which enables simulation of co- and countercurrent flow, gas or liquid recycle, and arbitrary retention time. Applying this model, hydrodynamic simulations reveal an improved prediction of liquid holdup, liquid dispersion, and pressure drop compared to CFD models of only the trickle bed. Furthermore, the interfacial surface area is analyzed and validated on account of a literature correlation. Exemplifying the entire approach with chemical absorption and reaction, the simulation of biological methanation presents methane concentration and productivity which are of the same order of magnitude as experimental data for an operating pressure of pop = 0.1–1 MPaabs.<br

Efficient Scheduling of Methanation on Wastewater Treatment Plants - Miederer, Jonas; Elhaus, Nora; Karl, Jürgen (10th International Conference on Renewable Energy Gas Technology (REGATEC 2024), 2024)

Enhancement of catalytic direct methanation for application in wastewater treatment plants - Miederer, Jonas; Markthaler, Simon; Kolb, Sebastian; Karl, Jürgen (4th Doctoral Colloquium BIOENERGY, 2021)

Short-Term Natural Gas and Carbon Price Forecasting Using Artificial Neural Networks - Böhm, Laura; Kolb, Sebastian; Plankenbühler, Thomas; Miederer, Jonas; Markthaler, Simon; Karl, Jürgen (Energies, 2023)
Methods of computational intelligence show a high potential for short-term price forecasting of the energy market as they offer the possibility to cope with the complexity, multi-parameter dependency, and non-linearity of pricing mechanisms. While there is a large number of publications applying neural networks to the prediction of electricity prices, the analysis of natural gas and carbon prices remains scarce. Identifying a best practice from the literature, this study presents an iterative approach to optimize both the input values and network configuration of neural networks. We apply the approach to the natural gas and carbon market, sequentially testing autoregressive and exogenous explanatory variables as well as different neural network architectures. We subsequently discuss the influence of architectural properties, input parameters, data preparation, and the models’ resilience to singular events. Results show that the selection of appropriate lags of gas and carbon prices to account for autoregressive properties of the respective time series leads to a high degree of forecasting accuracy. Additionally, including ambient temperature data can slightly reduce errors of natural gas price forecasting whereas carbon price predictions benefit from electricity prices as a further explanatory input. The best configurations presented in this contribution achieve a root mean square error (RMSE) of 0.64 EUR/MWh (natural gas prices) corresponding to a normalized RMSE of 0.037 and 0.33 EUR/tCO2 (carbon prices) corresponding to a normalized RMSE of 0.023.
CRIS-Team Scopus Importer:2023-10-06
Keywords: carbon price; multi-layer perceptron; natural gas price; neural network; price prediction