The efficient stabilization of high-moisture forestry biomass is a critical prerequisite for its conversion into high-value functional materials. However, the uncontrolled phase transition (crystallization) of free water within anisotropic porous media often leads to irreversible skeletal collapse and channel occlusion. This study investigates the static magnetic field (SMF)-assisted phase transition process of bamboo shoots (Chimonobambusa utilis) to mitigate these structural defects. To address the highly non-linear coupling effects of magnetic field intensity ( B ), coolant temperature ( T ), and air velocity ( v ), a comparative "Statistical-Intelligence" process modeling strategy was implemented. Response Surface Methodology (RSM) was primarily utilized to elucidate the statistical significance and complex interactive mechanisms, while a Genetic Algorithm-optimized Artificial Neural Network (GA-ANN) was developed as a comparative local surrogate model. Statistical analysis strictly validated the robustness of the RSM framework, demonstrating strong generalization capability (Predicted R 2 = 0.7592). Complementarily, the topology-optimized GA-ANN (3−4−1) achieved superior localized non-linear fitting precision ( R 2 = 0.9905, MAPE = 1.94%) against the RSM polynomial ( R 2 = 0.9764, MAPE = 5.51%), confirming the complex magnetic response of the biomass. Through RSM-driven global optimization, the ideal processing parameters were identified as v = 1.95 m/s, T = -25.73 °C, and B = 3.46 mT. Under these conditions, the predicted phase transition time of 645.12 s was highly consistent with the experimental validation value of 670 s (relative error = 3.86%), confirming the robustness of the predictive framework. Crucially, the phase transition dynamics exhibited a distinct non-monotonic response to the magnetic field. Quantitative topological analysis revealed that the optimized magnetic field induced "thermodynamic refinement" and "kinetic ordering" effects. This regime achieved an optimal balance between skeletal densification (deformation rate −22.8%) and permeability enhancement (Comprehensive Permeability Index 31.40), effectively preserving the native anisotropic vascular channels. This work provides a robust theoretical basis and a data-driven engineering paradigm for the low-carbon processing of biomass precursors. • RSM successfully modeled the nonlinear SMF-assisted biomass phase transition. • "Thermodynamic-Kinetic Balance" for structural protection was found at 4 mT. • SMF modulated water clusters to refine ice crystals and reduce supercooling. • Optimized protocol achieved 5-fold permeability via structural preservation.
Ruan et al. (Sat,) studied this question.