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Integrated Blockchain and Machine Learning Framework for Polystyrene Waste Traceability
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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.
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India is drowning in trash faster than it can manage it, and this study looks at whether smart tech—like AI, sensors, and blockchain—can help track and recycle waste more effectively. The findings suggest these tools can improve efficiency, but they only work if the country also fixes gaps in infrastructure, policy, and public habits. This matters because uncollected plastic and e-waste don't just pile up in landfills—they break down into microplastics and toxic chemicals that can contaminate water, food, and ultimately end up in our bodies.
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Scientists built an AI system that can look at a photo of trash and identify what type of plastic it is with over 96% accuracy, then use chemical scanning to pinpoint the exact plastic type with 99.5% accuracy. This matters because better sorting is the first step toward actually recycling or breaking down plastic waste using bacteria—rather than it sitting in landfills or oceans for centuries, breaking into the microplastics now found in our food, water, and even our bodies. Note that the "bacteria eating plastic" part of this study is based on prior research, not something the authors tested themselves—the real innovation here is the sorting techn
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