Data-Driven Waste Management: Machine Learning and Excess Reprocessing for Optimized Environmental Sustainability

The rapid urbanisation and industrialisation has brought about a new waste management problem, which calls for more sustainable practices. There is little appetite for innovation as the alternative of burning and landfilling is becoming a major health and environmental problem. This work presents a new Excess Reprocessing Scheme (ERS) through ML to improve the waste management protocols. It mainly concentrates on waste diversion, forecasting recycling rate in addition to reprocessing effectiveness. Waste is submitted by users and gets tailored and distributed to registered recycling companies on a digital platform which mimics ERS based on company capabilities. This approach is evaluated based on the following metrics: enhanced recycling rates, reduced cost, diminished greenhouse gas emissions, and classification performance. Results of simulation reveal that machine learning integrated with the energy recovery system (ERS) significantly enhances recycling rates, reduces environmental loads, and raises the efficiency of waste management.