Effects of Alcohol-Blended Waste Plastic Oil on Engine Performance Characteristics and Emissions of a Diesel Engine

Chalita Kaewbuddee, Somkiat Maithomklang, Prasert Aengchuan, Attasit Wiangkham, Niti Klinkaew, Atthaphon Ariyarit and Ekarong Sukjit ()
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Chalita Kaewbuddee: Faculty of Industrial Technology, Surindra Rajabhat University, Surin 32000, Thailand
Somkiat Maithomklang: School of Engineer and Innovation, Rajamangala University of Technology Tawan-ok, Chonburi 20110, Thailand
Prasert Aengchuan: School of Manufacturing Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand
Attasit Wiangkham: School of Manufacturing Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand
Niti Klinkaew: Institute of Research and Development, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand
Atthaphon Ariyarit: School of Mechanical Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand
Ekarong Sukjit: School of Mechanical Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand

Energies, 2023, vol. 16, issue 3, 1-25

Abstract: The current study aims to investigate and compare the effects of waste plastic oil blended with n-butanol on the characteristics of diesel engines and exhaust gas emissions. Waste plastic oil produced by the pyrolysis process was blended with n-butanol at 5%, 10%, and 15% by volume. Experiments were conducted on a four-stroke, four-cylinder, water-cooled, direct injection diesel engine with a variation of five engine loads, while the engine’s speed was fixed at 2500 rpm. The experimental results showed that the main hydrocarbons present in WPO were within the range of diesel fuel (C13–C18, approximately 74.39%), while its specific gravity and flash point were out of the limit prescribed by the diesel fuel specification. The addition of n-butanol to WPO was found to reduce the engine’s thermal efficiency and increase HC and CO emissions, especially when the engine operated at low-load conditions. In order to find the suitable ratio of n-butanol blends when the engine operated at the tested engine load, the optimization process was carried out by considering the engine’s load and ratio of the n-butanol blend as input factors and the engine’s performance and emissions as output factors. It was found that the multi-objective function produced by the general regression neural network (GRNN) can be modeled as the multi-objective function with high predictive performances. The coefficient of determination ( R 2 ), mean absolute percentage error ( MAPE ), and root mean square error ( RSME ) of the optimization model proposed in the study were 0.999, 2.606%, and 0.663, respectively, when brake thermal efficiency was considered, while nitrogen oxide values were 0.998, 6.915%, and 0.600, respectively. As for the results of the optimization using NSGA-II, a single optimum value may not be attained as with the other methods, but the optimization’s boundary was obtained, which was established by making a trade-off between brake thermal efficiency and nitrogen oxide emissions. According to the Pareto frontier, the engine load and ratio of the n-butanol blend that caused the trade-off between maximum brake thermal efficiency and minimum nitrogen oxides are within the approximate range of 37 N.m to 104 N.m and 9% to 14%, respectively.

Keywords: waste plastic oil; n-butanol; diesel engine; artificial intelligence; GRNNs (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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