Predictive adaptive reactivity-controlled compression ignition for a dual-fuel marine engine: A model-in-the-loop study

Low-temperature, reactivity-controlled compression ignition (RCCI) combustion has proven instrumental in resolving the long-standing trade-off between engine emissions and efficiency, particularly for heavy-duty applications. However, RCCI has an inherent sensitivity to variations in-cylinder charge composition, such as fuel stratification, temperature gradients, and air-fuel mixing. This makes combustion behavior unpredictable and difficult to regulate using conventional control methods. This study presents an advanced multivariable model-based control design (MBCD) toolchain tailored for marine RCCI engines. Specifically, it introduces a real-time adaptive model predictive control (AMPC) strategy to regulate the indicated mean effective pressure ( IMEP ) and the crank angle at 50% mass fraction burned ( CA 50) by manipulating the total fuel energy and the blend ratio ( BR ) between the two fuels. The control framework is evaluated through model-in-the-loop (MiL) simulations with an experimentally validated high-fidelity UVATZ (University of Vaasa Advanced Thermo-Kinetic Multi-zone) model of a Wärtsilä 31DF engine combustor as the plant, and a physics-based linear real-time model (RTM) as an observer. The controller’s performance is benchmarked against a decentralized PI controller under various transient scenarios. Both controllers achieve comparable tracking of IMEP and CA 50, but the AMPC demonstrates faster IMEP response (within eight cycles), lower CA 50 steady-state error (maximum 0.45 crank-angle degree (CAD)), and reduced fuel consumption (2.7%). Additionally, AMPC’s receding-horizon framework and self-tuning features enhance robustness against unstructured uncertainties and parameter variations, marking a significant advancement over previously proposed predictive control strategies.