Neuro-Fuzzy modeling for optimizing thermal modification in hybrid Eucalyptus clones

Abstract

Silviculture is strategic for the Brazilian economy and demands wood with stable physical and mechanical properties. Thermal modification enhances durability and dimensional stability without chemical additives. We applied an Adaptive Neuro‑Fuzzy Inference System (ANFIS) to optimize thermal treatment in two hybrid Eucalyptus urograndis clones (H77 and LW), predicting optimal time–temperature combinations and estimating apparent/basic density, mass loss, volumetric swelling, modulus of rupture (MOR) and modulus of elasticity (MOE). Twelve trees (six per clone) were sampled; experimental temperatures ranged from 100–220 °C. Lagrange polynomial interpolation generated 60 intermediate points to build continuous response surfaces, and ANFIS (six Gaussian membership functions per input; Sugeno linear output; 36 rules) was trained/validated using RMSE. Distinct clone‑specific behaviours were observed, supporting tailored strategies: densities achieved low RMSE (apparent: 0.101 g/cm³; basic: 0.087 g/cm³), volumetric swelling ~2.12 pp, while mass loss and especially mechanical properties showed higher variability (MOE ≈ 3.796 MPa; MOR with substantial error). The results indicate that intelligent modeling coupled with clone‑specific thermal parameters can maximize performance and inform sustainable industrial applications.