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

Abstract

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 (CA50) 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 CA50, but the AMPC demonstrates faster IMEP response (within eight cycles), lower CA50 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.