Elephant dung (ED) is an abundant yet severely underutilized biomass resource due to its exceptionally high ash content and weak structural integrity when densified. This study establishes an integrated multi-domain decision-oriented framework for ED-based biomass pellet optimization. The framework quantitatively links pellet mechanics, fuel chemistry, heating value, and combustion kinetics within a unified strategy using thermochemical by-product additives, including bio-oil (BO), carbonization tar (CBT), and pyroligneous acid (PRA), with palm oil (PO) as a reference binder (5–15 wt%). All formulations exhibited statistically dominant additive effects across physical, proximate, ultimate, thermal, and combustion properties (ANOVA, P < 0.001; η 2 and ω 2 ≥ 0.99). Additive incorporation transformed ED from a low-grade residue into a technically viable upgraded solid fuel, increasing fixed carbon from 11.6% to 21.7%, reducing ash from 26.7% to 10.3%, and achieving pellet densities up to 1112 kg/m 3 with dimensional stability approaching 98.5%. Higher heating value peaked at 18.60 MJ/kg for 5% CBT, while 15% BO delivered superior operational performance, combining exceptional moisture durability (water resistance at 78.8%), rapid ignition (29 s), and high burning rate (0.9 g/min). A hybrid multi-criteria decision-making framework integrating the criteria importance through an intercriteria correlation weighted sum model with random forest regression produced a robust composite suitability index (R 2 = 0 . 913 , MAE = 0.068, RMSE = 0.076). This work advances solid biofuel development from empirical trial-and-error toward predictive, data-driven fuel engineering. Overall optimization identified 15% BO-modified pellets as the highest-performing formulation, with 5% CBT as a cost-efficient energy-dense alternative for circular bioenergy systems. • BO additive shows highest stability, fastest ignition, and top C i ranking. • CBT achieves balanced performance with strong energy yield and low cost. • Thermochemical by-products restore wood-like composition and fuel quality. • Machine learning validates Ci-based selection of high-efficiency additives. • Optimization analysis suggests BO–CBT synergy for superior pellet performance.
Jansri et al. (Sun,) studied this question.