Strategic combination of waste plastic/tire pyrolysis oil with biodiesel for natural gas-enriched HCCI engine: Experimental analysis and machine learning model

Anh Tuan Hoang, Parthasarathy Murugesan, Elumalai Pv, Dhinesh Balasubramanian, Satyajeet Parida, Chandra Priya Jayabal, Murugu Nachippan, M.a Kalam, Thanh Hai Truong, Dao Nam Cao and Van Vang Le

Energy, 2023, vol. 280, issue C

Abstract: In this experiment, different combinations and blends based on 50% biodiesel and 50% pyrolysis oil were prepared to create 4 fuel samples for all tests. These samples were provided to the test engine operated on the conventional mode and homogeneous charge compression-ignition (HCCI) mode aiming to evaluate the performance, emission, and combustion characteristics of these modes. In the HCCI mode, a steady flow of 3 L per minute of compressed natural gas (CNG) was injected together with the air. As a result, preheated 50% Pongamia biodiesel/50% plastic pyrolysis oil combined with enriched CNG for the HCCI mode was found to be superior to those of other fuels according to performance, combustion, and emission characteristics although brake thermal efficiency was slightly lower than the conventional diesel engine. In addition, the performance and emission parameters of the HCCI engine were also predicted by using three machine learning models such as Decision Tree, Random Forest, and Support Vector Regression. Finally, Random Forest and Support Vector Regression approaches were recommended to predict the operation parameters of the HCCI engine with an accuracy level as high as nearly 99% in most instances, except for HC and CO with 80% and 90% of accuracy levels, respectively.

Keywords: Pyrolysis oil; HCCI engine; Biodiesel; Compressed natural gas enrichment; Engine behavior; Machine learning model (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:280:y:2023:i:c:s0360544223016274

DOI: 10.1016/j.energy.2023.128233

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