@article{RichterRachowIsraeletal.2023, author = {Richter, Jana and Rachow, Fabian and Israel, Johannes and Roth, Norbert and Charlafti, Evgenia and G{\"u}nther, Vivien and Flege, Jan Ingo and Mauss, Fabian}, title = {Reaction mechanism development for methane steam reforming on a Ni/Al2O3 catalyst}, doi = {10.3390/catal13050884}, year = {2023}, abstract = {In this work, a reliable kinetic reaction mechanism was revised to accurately reproduce the detailed reaction paths of steam reforming of methane over a Ni/Al2O3 catalyst. A steadystate fixed-bed reactor experiment and a 1D reactor catalyst model were utilized for this task. The distinctive feature of this experiment is the possibility to measure the axially resolved temperature profile of the catalyst bed, which makes the reaction kinetics inside the reactor visible. This allows for understanding the actual influence of the reaction kinetics on the system; while pure gas concentration measurements at the catalytic reactor outlet show near-equilibrium conditions, the inhere presented temperature profile shows that it is insufficient to base a reaction mechanism development on close equilibrium data. The new experimental data allow for achieving much higher quality in the modeling efforts. Additionally, by carefully controlling the available active surface via dilution in the experiment, it was possible to slow down the catalyst conversion rate, which helped during the adjustment of the reaction kinetics. To assess the accuracy of the revised mechanism, a monolith experiment from the literature was simulated. The results show that the fitted reaction mechanism was able to accurately predict the experimental outcomes for various inlet mass flows, temperatures, and steam-to-carbon ratios.}, subject = {1D modeling; Reaction rates; Methane steam reforming; Fixed-bed reactor experiments; Nickel catalyst; 1D-Modellierung; Reaktionsgeschwindigkeiten; Methan-Dampfreformierung; Festbettreaktor-Experimente; Nickel-Katalysator; Katalysator; Reaktionsmechanismus; Festbettreaktor; Steamreforming; Reaktionsgeschwindigkeit}, language = {en} } @article{FrankenNetzerMaussetal., author = {Franken, Tim and Netzer, Corinna and Mauss, Fabian and Pasternak, Michal and Seidel, Lars and Borg, Anders and Lehtiniemi, Harry and Matrisciano, Andrea and Kulzer, Andre Casal}, title = {Multi-objective optimization of water injection in spark-ignition engines using the stochastic reactor model with tabulated chemistry}, publisher = {SAGE Publications}, address = {London, England}, issn = {1468-0874}, doi = {10.1177/1468087419857602}, abstract = {Water injection is investigated for turbocharged spark-ignition engines to reduce knock probability and enable higher engine efficiency. The novel approach of this work is the development of a simulation-based optimization process combining the advantages of detailed chemistry, the stochastic reactor model and genetic optimization to assess water injection. The fast running quasi-dimensional stochastic reactor model with tabulated chemistry accounts for water effects on laminar flame speed and combustion chemistry. The stochastic reactor model is coupled with the Non-dominated Sorting Genetic Algorithm to find an optimum set of operating conditions for high engine efficiency. Subsequently, the feasibility of the simulation-based optimization process is tested for a three-dimensional computational fluid dynamic numerical test case. The newly proposed optimization method predicts a trade-off between fuel efficiency and low knock probability, which highlights the present target conflict for spark-ignition engine development. Overall, the optimization shows that water injection is beneficial to decrease fuel consumption and knock probability at the same time. The application of the fast running quasi-dimensional stochastic reactor model allows to run large optimization problems with low computational costs. The incorporation with the Non-dominated Sorting Genetic Algorithm shows a well-performing multi-objective optimization and an optimized set of engine operating parameters with water injection and high compression ratio is found.}, subject = {Ottomotor; Water injection; Genetic optimization; Spark-ignition engine; Stochastic reactor model; Detailed chemistry; Wassereinspritzung; Prozessoptimierung; Stochastisches Modell}, language = {en} }