article · Energy Conversion and Management
Methanol and diesel blends offer a promising alternative for reducing fossil fuel reliance in transportation, but phase stability remains a primary challenge. Twelve novel fuel blends were formulated using six distinct surfactants to assess their phase stability through ternary diagrams and to evaluate engine performance. Testing on a 3.5 kW single-cylinder diesel engine across loads from 2.5% to 100% revealed notable performance improvements. Certain blends increased brake power by up to 9.3% and boosted brake thermal efficiency by 31.5%, while achieving a low brake specific fuel consumption of 0.27 kg/kWh. Alongside laboratory trials, a machine learning model combining a Partial Reinforcement Optimiser with a Random Vector Functional Link network was introduced. This framework accurately predicted key engine performance indicators, including brake power and fuel consumption, outperforming several traditional optimisation models.
Transport sectors require practical options to reduce diesel use without demanding completely new engine systems. By confirming that stabilised methanol blends improve combustion efficiency and lower fuel consumption, this research offers a pathway to cleaner operating standards. Furthermore, the accompanying predictive algorithms help engineers forecast engine responses under variable loads without running extensive and costly physical trials.
This research applies directly to fuel developers, fleet operators, and engine calibration specialists seeking alternative liquid fuels for transportation. Evaluated using a single-cylinder 3.5 kW engine, the technology is at an applied experimental stage. Moving towards commercial deployment will require testing on full-scale, multi-cylinder automotive or industrial engines, alongside long-term durability trials for surfactant stability.
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• 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.
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DOI: 10.1016/j.enconman.2024.118943
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