Enhancing biogas/biohydrogen utilization in dual-fuel engines using advanced machine learning algorithms

Abstract

Biodiesel obtained from waste plant resources suffers from low energy density and poor cold flow properties, which affect its performance in real-world applications. Blending waste biodiesel with biogas increases the overall energy content in the combustion chamber and improves the combustion efficiency, eventually reducing carbon emissions. By lowering NOx and enhancing combustion properties, the addition of bio-hydrogen further improves the fuel's environmental profile. To analyse and optimize these blends, the study employed an Analytic Hierarchy Process (AHP) weighted k-means clustering approach. Load, compression ratio (CR), Ignition pressure (IP), biohydrogen supply and biogas flow rate are selected as operating parameters since this influence the combustion characteristics, fuel-air mixing, and emission behaviour in diesel engines. According to Pearson's r = 0.981, there is a high beneficial correlation between brake thermal efficiency (BTE) and brake specific fuel consumption (BSFC), and there is a moderate association (r = 0.952) between CO and NOx emissions. From the priority analysis, BTE (51%) and NOx (24%) came out to be the dominant parameters as compared to CO (17%) and BSFC (8%). The k-means clustering found the optimum combination of outcomes which are BTE of 37.5%, a BSFC of 220 g/kWh, and emissions of 1.1 g/kWh for CO and 675 ppm for NOx respectively. Optimal input settings are achieved in Dataset 18 which operates at a Load of 100%, a CR of 19, and an IP of 220 bar, utilizing 12% Hydrogen and 8% Biogas Flow Rate. Henceforth, the addition of biogas-biohydrogen in biodiesel mitigates its shortcomings by improving combustion efficiency, reducing emissions, and guaranteeing sustainable engine operation. Employing green fuels will aid in reducing fossil fuel dependency and promote sustainable energy, leading to a cleaner and greener biosphere.