INTEGRATING MACHINE LEARNING AND REINFORCEMENT LEARNING FOR SMART BIOGAS SYSTEMS
Keywords:
Biogas Generator, Machine Learning, Renewable Energy, Smart City, Waste Management.Abstract
Urban areas face escalating challenges in waste management and energy security amidst rapid population growth. While renewable energy technologies exist, integrated systems optimizing waste-to-energy conversion using artificial intelligence remain underexplored. This study proposes an Advanced Urban Smart Biogas Generator (AUSBG) framework integrating biogas, solar panels, and microalgae modules optimized by Machine Learning (ML) and Reinforcement Learning (RL). Using a Systematic Literature Review (SLR) approach, relevant scientific articles were analyzed to identify key parameters and optimization strategies. The results indicate that ML enhances predictive accuracy for biogas production and solar output, while RL enables dynamic operational control for multi-objective optimization. The findings suggest that the AUSBG concept significantly improves energy efficiency and reduces carbon emissions, supporting circular economy principles. However, challenges regarding data scarcity and model interpretability persist. This study provides a conceptual architecture for smart urban energy systems
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