Design and implementation of an AI, IoT, and blockchain-based system for circular economy transition in landfill management: a case study of Quilmaná, Peru

Abstract

This study presents the design and implementation of an integrated system based on artificial intelligence (AI), internet of things (IoT), and blockchain to support circular economy practices in landfill management. The system addresses the lack of integrated and validated digital solutions for environmental monitoring, resource optimization, and social inclusion in resource-constrained contexts. Developed under a design science research (DSR) approach, the system combines IoT sensors for real-time monitoring, machine learning models (LSTM for methane prediction, CNN for waste classification, and reinforcement learning (RL) for biogas optimization), and a blockchain-based platform for transparent transactions and recycler formalization. The system was implemented and evaluated over 12 months using operational data. The LSTM model achieved 95% prediction accuracy, while the CNN model demonstrated high classification performance. Results indicate a 35% reduction in landfill waste, a 40% decrease in CH₄ emissions, and a 30% increase in recycler income, with 60% of informal workers formalized. These findings demonstrate that integrating AI, IoT, and blockchain enables the transformation of landfill systems into scalable circular economy platforms for sustainable waste management.