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 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 a stochastic engine model and detailed chemistry. Exhaust gas recirculation (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 and indicated specific fuel consumption (ISFC) corresponding to oxyfuel operation with different dry and wet 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, and energy-efficient carbon capture. The case with the lowest humidity (T10deg) had the lowest temperature 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 constraint for combustion efficiency (>65%) limited the solution space towards high ISFC values, while the constraint for low indicated mean effective pressure (IMEP) (>7 bar) and the constraint for high IMEP (<8 bar) limited the solution space in between the two distinct clusters of feasible designs, and towards high exhaust temperature, respectively. 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 associated with stoichiometric combustion.