04025ntm a2200337 i 4500 MY-KuUP 20260220120612.0 t||||fr|||| 000 0 ta 260220t20252025my a|||fr|||| 000 0 eng d THE0010358 (Local) Hardback UMPSA eng UMPSA rda FTKMA .Z557 2025 r Thesis Muhammad Zikri Bin Japri, author. Optimisation of noise and vibration in diesel engines using biodiesel blends / Muhammad Zikri Bin Japri Kuantan, Pahang : UMPSA, 2025 © 2025 xvi, 219 pages : illustration 30 cm. + 1 CD-ROM. rdacontent text rdamedia unmediated rdacarrier volume rda text file PDF Faculty of Mechanical and Automotive Engineering Technology Thesis (Doctor of Philosophy) -- Universiti Malaysia Pahang - 2025 Includes bibliographical references This study examines the noise and vibration characteristics of a single-cylinder diesel engine fuelled with a blend of palm oil methyl ester (POME) and waste plastic oil (WPO) biodiesel at varying engine speeds and loads. The vibration was measured in the piston axis and analysed in the low-frequency region, while the noise emission was quantified in intensity and analysed in the localised noise source mapping and its level. The analysis demonstrates that both biodiesels effectively reduce noise and vibration levels compared to conventional diesel. POME, characterised by its higher cetane number, density, and kinematic viscosity, minimises the noise and vibration through a shortened ignition delay and reduced combustion intensity, with the highest reduction of 29.23% and 1.77% reduction noticeable in the average vibration amplitude and noise level generated using POME5 and POME10. In contrast, WPO achieves smoother combustion via improved atomisation despite its longer ignition delay. The highest reductions of 29.56% and 2.47% were obtained in the average vibration amplitude and noise radiated through WPO5. The enhanced oxygen content in both fuels facilitates more complete and prolonged combustion, thereby preventing abrupt in-cylinder pressure peaks contributing to noise and vibration. Further, the noise emission analysis identifies combustion-related components—such as the piston, crankshaft, and flywheel—as the primary noise sources, with the engine support structure also playing a significant role. Integrating the artificial neural network with five different optimisation algorithms, namely ant colony optimisation (ACO), genetic algorithm (GA), moth-flame optimisation (MFO), particle swarm optimisation (PSO), and tiki-taka algorithm (TTA), have found that POME7 and WPO11 are optimal for vibration minimisation, achieving 0.2158 and 0.2282 mm/s, which bring 11.78% and 3.57% reduction, respectively. Meanwhile, POME11 and WPO13 excel in noise reduction, recording emissions of 83.444 and 83.666 dB, respectively, contributing to 1.45% and 0.73% reduction. The optimisation results indicate that POME yields slightly lower noise and vibration levels than WPO at a smaller biodiesel proportion, enhancing efficiency and sustainability. Based on the convergence plot, the algorithm performance assessment shows that ACO and TTA are emerging as the most effective due to their rapid convergence rates and robust performance. GA, PSO, and MFO also display competitive optimisation capabilities, offering viable alternatives for engineering applications. The findings highlight the potential of POME and WPO to reduce environmental impact while maintaining high engine performance, thereby supporting the transition to cleaner and quieter diesel engine technologies. Faculty of Mechanical and Automotive Engineering Technology Dissertations Universities and colleges Dissertations Theses Dissertations lcc THESIS 0 0 lcc 0 -2 20000 20000 2026-02-20 0 FTKMA .Z557 2025 r Thesis T000004118 2026-02-20 12:06:28 2026-02-20 THESIS 0 0 lcc 0 -2 20000 20000 2026-02-20 0 CD13971 T000004119 2026-02-20 12:07:18 2026-02-20 THESIS 104211 104217