Sparse identification with multi-objective optimisation and multi-criteria decision-making for predicting hydrogen–diesel dual-fuel engine performance

Hydrogen enrichment in compression-ignition engines introduces strong nonlinear combustion effects that require advanced modelling for accurate prediction. This study develops a data-driven Sparse Identification of Nonlinear Dynamical Systems (SINDy) framework for predicting hydrogen–diesel dual-fuel (HDDF) combustion across a wide operating range. Systematic evaluation of sparsity levels and 127 function-library combinations showed that hybrid nonlinear libraries provide the most consistent predictive performance. Using all terms achieved R2 values of 0.9829 (Brake Power), 0.9794 (BSFC), 0.8139 (NOx), 0.8605 (Torque/BMEP), 0.8641 (BTE), and 0.9938 (UHC), while CO prediction remained challenging due to intrinsic combustion variability. Multi-objective optimisation using Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) identified an optimal configuration at λ = 0.05 and θ = 122, balancing BSFC, NOx, and BTE trade-offs. Comparison with artificial neural networks demonstrated improved robustness and generalisation. The framework provides an interpretable and computationally efficient surrogate model for HDDF prediction and optimisation.