Enhancement of compression ignition engine performance using waste plastic-derived fuel blended with nanoparticles: an experimental and regression analysis - Rajak, Upendra; Panchal, Manoj; Dasore, Abhishek; Hashim, Norhashila; Mohanty, Ramesh Chandra; Giri, Nimay Chandra; Das, Soumya Ranjan (Elsevier B.V., 2026)
The study is experimental research on the performance, combustion, and emission characteristics of a compression ignition (CI) engine run with waste plastic oil (WPO)-diesel blends supplemented with aluminum oxide (Al2O3) nanoparticles. The tested fuel consisted of neat diesel (D100) and WPO-diesel blends (DWPO10, DWPO20, DWPO30, and DWPO40). Furthermore, 25, 50, and 100 ppm of Al2O3nanoparticles were suspended in DWPO20. The results indicate that of all the fuels that have been tested, DWPO20 25 ppm Al2O3had the brake thermal efficiency (BTE) (32.36%), which was higher than DWPO20 without nanoparticles (31.68%), while also reducing the brake specific fuel consumption (BSFC). When the WPO proportion was raised, the peak cylinder pressure did decrease slightly, but DWPO20 maintained the same combustion behavior as D100. The emission levels of nitrogen oxides NOx were also increased with the growing CR, but the levels of CO and HC were reduced with the inclusion of the nanoparticles because of improved oxidation kinetics. Artificial neural network (ANN) and general regression neural network (GRNN) models were developed to predict engine performance and emissions. The models reported excellent predictive performance (R2=0.85–0.96) and they were able to reproduce physically consistent trends of combustion and emission with varying operating conditions.