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Integrated Blockchain and Machine Learning Framework for Polystyrene Waste Traceability
Summary
A blockchain-IoT-ML framework for polystyrene waste management achieved 94.8% predictive accuracy for waste forecasting and 2,847 transactions per second on a custom permissioned blockchain, enabling traceable, data-driven EPS/XPS waste energy recovery. Since expanded polystyrene is a major source of microplastic pollution, transparent digital traceability systems like this could substantially improve accountability and reduce environmental plastic leakage.
The management of expanded and extruded polystyrene (EPS/XPS) waste presents a significant environmental challenge due to limited recyclability and the generation of microplastics. Conventional waste management systems often lack transparency, data interoperability, and operational efficiency. This study introduces a smart waste management framework that integrates Internet of Things (IoT) sensing, Machine Learning (ML), and blockchain technologies to enable traceable, data-driven energy recovery. The proposed system employs PureChain, a custom permissioned blockchain designed for the immutable recording of data from IoT-enabled smart bins equipped with fill-level, temperature, and waste-type sensors. Performance evaluation demonstrated that PureChain achieved a throughput of 2,847 transactions per second, a latency of 0.3 s, and an energy consumption of 0.02 kWh per transaction. For predictive analytics, the Long Short-Term Memory (LSTM) model achieved the highest forecasting accuracy of 94.8% with a coefficient of determination R2 = 0.947. In contrast, the XGBoost model maintained balanced accuracy of 92.8% and superior computational efficiency. The integration of PureChain and ML creates a closed-loop, intelligent system that enhances data traceability, optimizes waste-collection logistics, and enables a verifiable circular economy for EPS/XPS waste management.