ADVANCING SUSTAINABLE DEVELOPMENT GOALS WITH MACHINE LEARNING AND OPTIMIZATION FOR WET WASTE BIOMASS TO RENEWABLE ENERGY CONVERSION
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The rise in untreated wet waste due to population growth, urbanization, and enhanced living standards poses a significant environmental challenge. Converting this waste into energy and carbon-neutral substances aligns with the United NationsSustainable Development Goal 7 - Affordable and Clean Energy, and Sustainable Development Goal 15 - Life on Land. Hydrothermal carbonization (HTC) and pyrolysis technologies, capable of converting large volumes of waste into energy-rich products, have been effective solutions. We've developed a framework that leverages advanced machine learning techniques for comparative analysis of the products from HTC and pyrolysis, focusing on the Carbon Stability Index (CSI) and Return on Energy Investment (REI). This tailored approach offers optimal conditions for high energy efficiency and stable carbon sequestration. A case study involving wet food waste showed a significant enhancement in REI and CSI. The model provides robust predictions, reinforcing its practical applications and contributing to sustainability goals.