Abstract

The growing accumulation of solid and electronic waste has created a pressing need for intelligent, energy-efficient, and sustainable waste management systems. Traditional waste processing frameworks often fail to optimize sorting, recycling, and treatment operations, leading to increased environmental pollution and inefficient energy consumption. Recent advances in artificial intelligence (AI) and power electronics provide opportunities to design adaptive control mechanisms that can optimize energy flow, automate waste classification, and minimize ecological impact. Existing automated waste management systems lack real-time adaptability and energy optimization, especially under variable operational loads. Moreover, conventional control systems are unable to integrate heterogeneous waste data or predict system behavior dynamically. Therefore, a hybrid intelligent model is essential to enable sustainable waste handling through optimized decision-making and efficient energy utilization. This work proposes a MOSFET-operated Fuzzy Logic and Deep Reinforcement Learning (DRL) framework for sustainable waste management and environmental remediation. The system employs a fuzzy logic controller to regulate the MOSFET-based power flow in sorting and recycling units, ensuring stable operation under fluctuating waste loads. Meanwhile, a DRL agent learns optimal waste sorting and treatment strategies from sensor data, improving efficiency over time. The hybrid model is simulated using MATLAB and TensorFlow environments to evaluate its energy efficiency, decision accuracy, and operational stability. Simulation outcomes demonstrated that the proposed hybrid system achieved energy efficiency up to 88%, improved waste classification accuracy to 91%, and exhibited a DRL convergence rate of 0.056 per episode. System stability was enhanced with actuator variance reduced to 0.021, and computational efficiency per decision cycle remained around 0.027 s, outperforming conventional fuzzy logic, deep learning, and RL-based methods by a substantial margin.

Authors

K. Anbumani, K. Kayalvizhi
Sri Sairam Engineering College, India

Keywords

Fuzzy Logic, Deep Reinforcement Learning, MOSFET Control, Sustainable Waste Management, Environmental Remediation