ANN-driven prediction of optimal machine learning models for engine performance in a dual-fuel mode powered by biogas and fish oil biodiesel

Abstract

Global climate change is increasingly driven by carbon dioxide emissions from fossil fuels, intensifying the greenhouse effect and global warming. Biodiesel, particularly fish oil biodiesel, provides a sustainable alternative that reduces greenhouse gas (GHG) emissions. In this study, the engine was tested with various blends of fish oil biodiesel and conventional diesel, using volume ratios from 20 % to 40 % in 5 % increments. Subsequently, it was operated in dual-fuel mode with two mixtures: (a) Pure Methane (CH₄) and (b) Methane (CH₄) + Carbon dioxide (CO₂). These were injected through the intake manifold at flow rates of 4, 8, and 12 LPM, alongside fresh air and the biodiesel blends. Performance analysis included emissions and combustion characteristics. These comprised nitrogen oxides (NOx), smoke, hydrocarbons (HC), carbon dioxide (CO₂), carbon monoxide (CO), brake thermal efficiency (BTE), peak pressure (PP), maximum pressure rise rate (MPRR), and vibrational characteristics under varying biogas flow rates and engine loads. Engine performance was monitored using vibrational data analysed by machine learning (ML) models based on Bayes net and random forest algorithms. B25 blend combined with M7.2C4.8 (i.e., methane 7.2 LPM and CO₂ 4.8 LPM) stands out as the top performer, achieving the highest classification accuracy at 97 %. The combustion and emission parameters for the optimal blend were predicted using a feedforward backpropagation artificial neural network (ANN) employed a 3–12-8 neuron architecture. Additionally, vibrational characteristics were analysed with another ANN configured as 2–4-4–5. The results showed that these ANN models effectively predicted engine parameters under different load conditions and achieved average R-values of 0.97 and 0.98, respectively. The B25 blend significantly reduced emissions and enhanced combustion efficiency. This highlights its potential in mitigating GHG emissions and promoting sustainable alternative fuels.