Author
Listed:
- Dutta, Sarajit
- Khan, Muhammad Neamat Ullah
- Hoque, Md Emdadul
- Jin, Yingai
- Alam, Firoz
- Trinuruk, Piyatida
Abstract
This paper presents the use of pyrolytic plastic oil (PPO) blends in single-cylinder, water-cooled diesel engines, analyzing their performance across various loading conditions. PPO was produced via pyrolysis of waste plastics, with HDPE being the dominant material. Five different fuel blends were created by varying volumetric ratios, and their physical properties were analyzed, showing similarities with pure diesel. Chemical characterizations through FTIR and UV–Vis spectroscopy confirmed the presence of aromatics, conjugated bonds and oxygenated compounds in PPO blends, influencing engine performance. Results demonstrated improved brake thermal efficiency (BTE) with higher blend ratios, though knocking tendency increased at peak loads. The PPO40 blend demonstrated optimal performance across all conditions. An artificial neural network (ANN) model was developed using experimental data, with engine load and blend ratios as input features and output parameters including brake specific fuel consumption (BSFC), BTE, exhaust gas temperature (EGT), and brake mean effective pressure (BMEP). The model demonstrated excellent accuracy in predicting engine performance, with statistical consistency validated against experimental results. The model's ability to predict outcomes for an untested intermediate blend (PPO70) across load conditions highlights its strength as a predictive tool. Additionally, factors affecting performance, such as concentration of chemical functional groups, were discussed in comparison with pure diesel. This research validates the potential of PPO as an alternative fuel for CI engines, offering a sustainable solution to reduce reliance on fossil fuels.
Suggested Citation
Dutta, Sarajit & Khan, Muhammad Neamat Ullah & Hoque, Md Emdadul & Jin, Yingai & Alam, Firoz & Trinuruk, Piyatida, 2025.
"Sustainable diesel engine performance enhancement using pyrolytic plastic oil blends: experimental investigation and artificial neural network-based prediction,"
Energy, Elsevier, vol. 333(C).
Handle:
RePEc:eee:energy:v:333:y:2025:i:c:s0360544225031111
DOI: 10.1016/j.energy.2025.137469
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