Work

Machine learning based prognostics and statistical optimization of the performance of biogas-biodiesel blends powered engine

Year: 2024

Type: article

Abstract: In this study, waste biomass-derived biogas was employed as the main fuel while the biodiesel-diesel blend was used as pilot fuel. This paper describes the development of a Decision Tree and Response Surface methodology-based statistical framework for prediction modeling and optimization. The compression ratio, fuel injection time , fuel injection pressure , and biogas flow rate were employed as controllable inputs , and brake thermal efficiency , peak combustion pressure, and exhaust emission were selected as responses. The experimental data for model development was gathered for the development of prediction models and optimization. The decision tree-based models were robust with almost negligible mean squared errors and R 2 values of more than 0.9487 for all models. Response surface methodology-based optimized engine parameters were validated with the following results compression ratio was 17.9, fuel injection pressure was 225 bar, fuel injection timing was 26.3-degree crank angle after top dead center, and the biogas flow rate was 0.85 kg/h. Validation results were within 5 % of the model-optimized results. The prognostic models for all control factors were developed with decision tree-based machine learning with high predictive efficiency and low errors.

Source: Case Studies in Thermal Engineering

DOI: 10.1016/j.csite.2024.105116

Authors: Prabhu Paramasivam, Mansoor Alruqi, Seshathiri Dhanasekaran, Fahad Albalawi, H.A. Hanafi, Waleed Saad