A Hybrid Machine Learning Approach to Shelflife Prediction of NBR for Long‐Term Storage Applications

This study examines the thermal degradation kinetics and shelf‐life prediction of Nitrile Butadiene Rubber (NBR) using thermogravimetric analysis and a hybrid machine learning framework. Both virgin and two‐year‐aged NBR samples were assessed with model‐free kinetic methods: Ozawa–Flynn–Wall (OFW), Kissinger–Akahira–Sunose, and Kissinger. The activation energy for virgin samples ranged from 67.26 to 73.33 kJ/mol; for aged samples, it was slightly lower (66.9–72.57 kJ/mol), indicating molecular degradation. Shelf life estimated at 40°C with Toop's equation decreased from 111.29 to 89.01 years under OFW kinetics. An Arrhenius‐type exponential degradation model, using a Random Forest (RF)–AdaBoost algorithm (80/20 train‐test split), achieved high accuracy ( R 2 > 0.999, RMSE < 1%, MAPE < 2%). The fitted parameters (Ψ ≈0.043°C −1 ; Υ = 0.08–0.11; Φ ≈1) and deviations of less than 1% from the experimental shelf life confirm the robustness of the nonlinear degradation prediction.