Optimising novel methanol/diesel blends as sustainable fuel alternatives: Performance evaluation and predictive modelling

• Developed 12 novel methanol/diesel blends, achieving up to 9.3% increase in BP. • Lowest BSFC of 0.27 kg/kWh in methanol/diesel blends, outperforming pure diesel. • Machine learning model has a prediction accuracy of R 2 ≈ 93% and RMSE ≈ 1.13. • BTE increased by 31.5% with C2 blend, showing enhanced combustion efficiency. • Methanol/diesel blends showed stable VE between 71.96% and 76.65% across loads. The pursuit of reducing diesel consumption while progressing towards a sustainable energy future necessitates critical decisions regarding fuel modifications or engine adaptations to ensure smooth transitions in transportation. This study explores the potential of methanol/diesel blends as a sustainable fuel solution for the transport sector. We address a significant gap by examining the impact of six different surfactants on blend stability and engine performance. Ternary phase diagrams were constructed to analyse blend stability, and engine testing on a 3.5 kW single-cylinder diesel engine evaluated the effects on brake power (BP), brake specific fuel consumption (BSFC), brake thermal efficiency (BTE), brake mean effective pressure (BMEP), and volumetric efficiency (VE) across various load conditions (2.5 %, 25 %, 50 %, 75 %, and 100 % load). Additionally, a novel predictive model was developed using the Partial Reinforcement Optimiser (PRO) algorithm integrated with Random Vector Functional Link (RVFL) to enhance engine performance estimation. Comparative analysis with established optimisation algorithms (GWO, WOA, AOA, HHO, and traditional RVFL) demonstrated the superior accuracy of the PRO-RVFL model. The model consistently achieved the highest R 2 and lowest RMSE scores for all evaluated parameters (BP: R 2 ≈ 93 %, RMSE ≈ 1.13; BSFC: R 2 ≈ 91 %, RMSE ≈ 1.45; BTE: R 2 ≈ 89 %; BMEP: R 2 ≈ 81 %, RMSE ≈ 2.80; VE: R 2 ≈ 71 %, RMSE ≈ 3.13). The findings support the viability of methanol/diesel blends in enhancing engine performance while promoting sustainability in transportation. This study, with its precise experimentation and advanced modelling techniques, paves the way for the development of cleaner and more efficient transportation systems.