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ADVANCING SUSTAINABLE DEVELOPMENT GOALS WITH MACHINE LEARNING AND OPTIMIZATION FOR WET WASTE BIOMASS TO RENEWABLE ENERGY CONVERSION

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Abstract

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.

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Description

55 pages

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Date Issued

2023-08

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Keywords

affordable and clean energy; carbon sequestration; hydrothermal carbonization; machine learning; pyrolysis; waste-to-energy conversion

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Union Local

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Committee Chair

You, Fengqi

Committee Co-Chair

Committee Member

Varner, Jeffrey

Degree Discipline

Chemical Engineering

Degree Name

M.S., Chemical Engineering

Degree Level

Master of Science

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Government Document

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Attribution-NonCommercial 4.0 International

Types

dissertation or thesis

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