Publication: Experimental and optimization study on the effects of diethyl ether addition to waste plastic oil on diesel engine characteristics
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Issued Date
2023
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File Type
application/pdf
Rights Holder(s)
Scopus
Bibliographic Citation
RSC Advances. Vol 13, No.36 (2023), p.25464-25482
Suggested Citation
Wiangkham A., Klinkaew N., Aengchuan P., Liplap P., Ariyarit A., Sukjit E. Experimental and optimization study on the effects of diethyl ether addition to waste plastic oil on diesel engine characteristics. RSC Advances. Vol 13, No.36 (2023), p.25464-25482. doi:10.1039/d3ra04489k Retrieved from: https://hdl.handle.net/20.500.14740/12462
Other Contributor(s)
Abstract
This study investigates the impact of adding diethyl ether (DEE) to pyrolysis oil derived from mixed plastic waste on engine performance, combustion characteristics, and emissions. The blending of different DEE concentrations (5%, 10%, and 15% by volume) with waste plastic oil (WPO) was analyzed. Experiments were conducted on a four-cylinder diesel engine, varying engine loads while maintaining engine speed. The results indicate that WPO mainly comprises middle-distillate hydrocarbons (52.58% C13-C18 and 26.15% C19-C23). While WPO had lower specific gravity, density, and flash point, it met diesel fuel specifications for kinematic viscosity and cetane index. The addition of DEE led to decreased properties in all blended fuels, except for the cetane index. Engine performance declined with WPO-DEE blends at low engine loads but improved at high engine loads with minimal variation as DEE concentration increased. DEE addition resulted in a shorter ignition delay and earlier combustion, although increasing DEE concentration did not further advance combustion. NOx emissions significantly decreased with DEE addition, while HC and CO emissions remained unaffected at high engine loads. To optimize the process, the non-dominated sorting genetic algorithm II (NSGA-II) with generalized regression neural networks (GRNNs) was employed as a surrogate multi-objective function. The GRNNs model demonstrated excellent performance, achieving high R2 values of 0.952 and 0.918, low RMSE values of 0.659 and 0.310, and MdAPE values of 2.675% and 5.098% for brake thermal efficiency (BTE) and NOx, respectively. The NSGA-II algorithm with GRNNs model proved successful in predicting the multi-objective function in the optimization process, even with limited data. The Pareto frontier analysis revealed an optimal DEE percentage of approximately 10% to 14% for maximum BTE and minimum NOx, with engine loads distributed around 30, 40, and 100 N m. © 2023 The Royal Society of Chemistry.