This study contributes to the advancement of circular economy practices in polymer manufacturing by applying machine learning algorithms (MLA) to predict the tensile strength of recycled low-density polyethylene (LDPE) building films. As the construction and packaging industries increasingly seek eco-efficient and low-carbon materials, recycled LDPE offers a valuable route toward sustainable resource management. However, ensuring consistent mechanical performance remains a challenge when reusing polymer waste streams. To address this, tensile tests were conducted on LDPE films produced from recycled granules, measuring tensile strength, strain, mass per unit area, thickness, and surface roughness. Three established machine learning algorithms—feed-forward Neural Network (NN), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost)—were implemented, trained, and optimized using the experimental dataset using R statistical software (version 4.4.3). The models achieved high predictive accuracy, with XGBoost providing the most robust performance and the highest level of explainability. Feature importance analysis revealed that mass per unit area and surface roughness have a significant influence on film durability and performance. These insights enable more efficient production planning, reduced raw material usage, and improved quality control, key pillars of sustainable technological innovation. The integration of data-driven methods into polymer recycling workflows demonstrates the potential of artificial intelligence to accelerate circular economy objectives by enhancing process optimization, material performance, and resource efficiency in the plastics sector.
Chalecki et al. (2026) studied this question.