Interpretable Physics-Informed Machine Learning for Pyrolysis

Accurate prediction of biomass pyrolysis products remains challenging due to the inherent complexity of thermochemical kinetics and the lack of mechanistic interpretability in modern Machine Learning (ML) models. This study addresses the black-box problem by comparing a standard Artificial Neural Network (ANN) against a novel Hybrid Physics-Informed Neural Network (PINN) and a Transparent Model (Rough Set ML, RSML) for biochar yield prediction. The standard ANN demonstrated poor generalization performance (R2 = −2.4109) and exhibited physical inconsistency, quantified by a low Physical Consistency Degree (PCD=0.6429) and non-monotonic behavior in Partial Dependence Analysis. The PINN was implemented using the Independent Parallel Reactions Scheme (IPRS) to enforce kinetic constraints via a Partial Differential Equation loss (LPDE). The results show a critical trade-off: the PINN under standard balancing failed, yielding a PCD value of 0.0714, yet an Extended Kinetic Fitting mode successfully achieved perfect physical coherence (PCD=1), demonstrating that enforcing physics acts as a powerful regularizer, leading to a significant improvement in precision (R2 = 0.82). Furthermore, this coherent PINN autonomously discovered a valid Activation Energy (Ea=150 kJ/mol), offering direct mechanistic insights by establishing a thermodynamically consistent global activation energy barrier for the primary thermal decomposition stage. This is complemented by the RSML model, which generated highly certain (cer≥95%) IF–THEN rules, translating kinetic principles into actionable operational guidelines (e.g., specific thresholds for operating temperature and feedstock Ash content). The study suggests that PIML is a promising pathway for achieving reliable, robust, and mechanistically interpretable modeling in chemical engineering.