Prognostic Metamodel Development for Waste-Derived Biogas-Powered Dual-Fuel Engines Using Modern Machine Learning with K-Cross Fold Validation

Abstract

Attention over greenhouse gas emissions has driven interest in cleaner energy sources including alternative fuels. Waste-derived biogas, which is produced by the anaerobic digestion of organic waste such as municipal solid waste, agricultural residues, and wastewater sludge, is an intriguing biofuel source due to its abundant availability and promise of lowering emissions. We investigate the potential of waste-derived biogas as an alternative fuel for a dual-fuel engine that also uses diesel as a secondary fuel in this study. We suggest using a modern machine learning XGBoost model to forecast engine performance. Data acquired with thorough lab-based text will be used to create prognostic models for each output in this effort. Control factors impacting engine performance, including pilot fuel injection pressure, engine load, and pilot fuel injection time, will be employed. The effects of these control elements on engine reaction variables such as brake thermal efficiency (BTE), peak pressure (Pmax), nitrogen oxides (NOx), carbon monoxide (CO), and unburned hydrocarbons (UHC) were simulated. The created models were tested using a variety of statistical approaches, including the coefficient of determination (0.9628–0.9892), Pearson’s coefficient (0.9812–0.9945), mean absolute error (0.4412–5.89), and mean squared error (0.2845–101.7), all of which indicated a robust prognostic model. The use of the increased compression ratio helped in the improvement of BTE with a peak BTE of 26.12%, which could be achieved at an 18.5 compression ratio 220 bar fuel injection pressure peak engine load. Furthermore, our findings give light regarding how to improve the performance of dual-fuel engines that run on waste-derived biogas, with potential implications for cutting emissions in the transportation sector.