Multi-Objective Optimization of Oxyfuel Gas Engine Using Stochastic Engine Model and Detailed Chemistry

The energy transition initiatives in Germany’s renown coal mining region Lusatia have driven research into Power-to-X-to-Power (P2X2P) technologies, where synthetic fuel is produced from renewably sourced hydrogen and captured CO2, and converted to electricity and heat through oxyfuel combustion. This work investigates the multi-objective optimization of oxyfuel gas engine using stochastic engine model (SEM) and detailed chemistry. EGR rate, initial cylinder temperature and pressure, spark timing, piston bowl radius and depth are selected as design parameters to minimize the exhaust temperature at exhaust valve opening (EVO) and indicated specific fuel consumption (ISFC) corresponding to oxyfuel operation with different dry and wet exhaust gas recirculation (EGR) rates. The optimization problem is solved for a dry EGR and four wet EGR cases with various CO2 / H2O fractions, aiming to achieve comparable performance as in conventional natural gas / air operation, along with an energy-efficient carbon capture unit. The case with the lowest humidity (T10deg) had the lowest Texh of 1537 K, while the one with the highest vapor fraction (T70deg) attained the minimum 260 g/kWh ISFC. The superiority of the T10deg case is offset by much higher cooling demand (3.06 kW) for CO2 separation than that for T70deg case (0.81 kW). The optimized designs from all the cases could outperform the reference case in terms of IMEP, nevertheless they fell below 31% indicated efficiency, which is linked to stoichiometric combustion.