Machine Learning Applications in Maximizing Renewable Energy Efficiency during Waste Recycling

Abstract

Current waste recycling and energy recovery processes involving high energy consumption suffer from inefficiencies which entail large energy losses and environmental misuses. Current methods are real-time flexible but fail at true sorting, efficient usage of energy, and increased landfill waste. The sustainability goals of renewable energy maximization as well as those of circular economy can be frustrated by such challenges. To tackle these issues this paper presents a new Machine Learning (ML) driven Waste to Energy (WTE) Optimization System. Smart waste classification using deep learning, predictive energy modeling via regression-based algorithms, and reinforcement learning for real-time recycling process optimization are integrated into this system. Furthermore, extraction techniques are optimized by adaptive energy recovery control mechanisms, and process transparency and accountability are improved through blockchain integration. Therefore with this ML-driven innovative framework, waste sorting accuracy can be enhanced, nonrecycled waste reduced and renewable energy yield increased. The proposed solution can guarantee continuous improvement of efficiency in a self-optimized manner.