Adsorption Performance of Torrefied Wood Chips for Volatile Organic Compounds and Ethylene Gas

Abstract

Machine learning models were developed to predict volatile organic compound and ethylene gas adsorption performance of freshness-preserving agents based on torrefied oak wood chips. Oak chips were torrefied at 350 °C for 20 min and processed into three particle sizes. A dataset of 39 experimental points was collected, comprising 8 input variables (particle size, torrefied wood content, commercial content, bulk density, compressed density, porosity, total content, and final bulk density) and 2 output variables (VOC and ethylene adsorption levels). Data augmentation techniques were applied to overcome dataset limitations. Three machine learning algorithms were implemented: Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR). For ethylene adsorption, SVR achieved superior performance with R² = 0.934, RMSE = 5.06, and MAE = 1.997.). For VOC adsorption, RF demonstrated highest accuracy with R² = 0.962, RMSE = 1.11, and MAE = 0.845. Torrefied wood content was positively correlated with ethylene adsorption (r = 0.43). Porosity was negatively correlated (r = -0.76). Higher porosity gave reduced ethylene capture efficiency, consistent with a negative relationship between pore structure and adsorption. The effectiveness of machine learning was demonstrated in predicting gas adsorption performance. The work provides practical guidelines for designing torrefied wood-based freshness-preserving systems.