Significant Research on Sustainable Oxygenated Fuel for Compression Ignition Engines with Controlled Emissions and Optimum Performance Prediction Using Artificial Neural Network

The present work compares the performance and emissions of a compression ignition (CI) engine using dual-mode LPG at varying flow rates and an oxygenated biodiesel mix (B20). The experimental investigation is carried out on LPG flow rates (0.1, 0.3, and 0.5 kg/h) and replacing the diesel with oxygenated B20, affecting engine performance and emissions under various load circumstances while maintaining engine speed. The study demonstrates the potential of the artificial neural network (ANN) in accurately forecasting the performance and emission characteristics of the engine across different operating conditions. The ANN model’s high accuracy in correlating experimental results with predicted outcomes underscores its potential as a dependable instrument for optimizing fuel parameters. The results show that LPG and oxygenated B20 balance engine performance and emissions, making CI engine functionality sustainable. A biodiesel blend containing diethyl ether (B20 + 2%DEE) exhibits slightly reduced brake thermal efficiency (BTE) at lower brake power (BP); however, it demonstrates advantages at higher BP, with diethyl ether contributing to improved ignition quality. The analysis indicates that the average NO x emissions for B20 + 2%DEE at flow rates of 0.1 kg/h, 0.3 kg/h, and 0.5 kg/h are 29.33%, 28.89%, 48.05%, and 37.48%, respectively. Consequently, selecting appropriate fuel and regulating the LPG flow rate is critical for enhancing thermal efficiency in a dual-fuel engine.