Citation

Santo, Istiyak Amin and Lim, Siow Chun and Lee, It Ee and Tan, Wooi Haw and Ho, Chai Yee and Razali, Nur Mazlini and Yusoff, Lukeman (2026) Tri-objective co-optimization of waste-cooking-oil biodiesel using machine learning, NSGA-II, and life-cycle assessment. Energy Reports, 15. p. 108907. ISSN 23524847

Text Tri-objective co-optimization of waste-cooking-oil biodiesel using machine learning, NSGA-II, and life-cycle assessment.pdf - Published Version Restricted to Repository staff only Download (3MB)

Abstract

Turning waste cooking oil (WCO) into biodiesel reduces waste and fossil fuel dependence, yet performance and environmental impacts are typically assessed in isolation, obscuring critical trade-offs. We identify operating conditions that simultaneously maximize biodiesel yield, increase higher heating value (HHV), and minimize process energy per kilogram of fuel. Using 500 designed experiments varying seven factors ( methanol-to-oil ratio, catalyst loading, temperature, reaction time, mixing speed, free fatty acid content, and water content). We train multiple machine learning models and apply multi-objective optimization. XGBoost delivers the best predictive accuracy across all targets; SVR performs worst for HHV. A gate-to-gate life cycle assessment (per 1 kg biodiesel) quantifies environmental impacts. The optimizer yields 58 Pareto-optimal solutions; a balanced point achieves 85.9 wt% yield, 38.2 MJ kg−1 HHV, and 5.1 MJ kg−1 process energy. Compared to a conventional baseline, this solution reduces global warming potential, acidification, and eutrophication by 11.3% on average. Unlike sequential approaches that optimize performance first and assess sustainability afterward, our integrated ML–optimization–LCA framework unifies prediction, search, and environmental evaluation in a single workflow, explicitly revealing trade-offs among yield, fuel quality, and energy intensity. This enables producers to select operating points aligned with priorities – higher output, better fuel, or lower energy – while quantifying environmental co-benefits. Policymakers can leverage these insights to align biodiesel production with carbon reduction and efficiency targets, supporting cleaner, scalable deployment at plant scale

Item Type:	Article
Uncontrolled Keywords:	Biodiesel
Subjects:	T Technology > TP Chemical technology > TP315-360 Fuel
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	02 Mar 2026 00:17
Last Modified:	02 Mar 2026 00:17
URII:	http://shdl.mmu.edu.my/id/eprint/15376

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