Smart Bin for Urban Energy Recovery Optimisation

Abstract

Rapid urbanisation in developing countries has intensified pressures on municipal waste management systems while increasing demand for sustainable energy solutions. This study presents the design and simulation-based evaluation of an integrated Autonomous Waste Management System (AWMS) that combines smart waste collection with biogas-based energy recovery. The proposed architecture consists of IoT-enabled Smart Waste Bins, Autonomous Car Bases for waste transport, and an Administrative Centre responsible for real-time monitoring, routing decisions, and system coordination. Teaching–Learning-Based Optimisation (TLBO) is applied to dynamically optimise collection routes based on live sensor data. System performance is evaluated using a Discrete Event Simulation (DES) framework implemented in Python and SimPy over a 30-day period for a hypothetical urban community. Multiple waste-generation scenarios are analysed, and statistical tests are used to compare the AWMS with a conventional fixed-schedule system. Results indicate improvements in collection efficiency, reductions in collection time and transport energy use, increased biogas yield, and lower greenhouse gas emissions. The system also supports organic fertiliser recovery from digestion by-products. These findings demonstrate the scalability, operational effectiveness, and environmental benefits of integrating real-time optimisation and energy recovery within autonomous waste management systems, particularly in rapidly growing urban contexts.