CHARACTERIZATION OF MUNICIPAL SOLID WASTE USING MACHINE LEARNING BASED IMAGING AND CHEMICAL ANALYSIS
Author ORCID Identifier
https://orcid.org/0009-0009-3344-4621
Date Available
8-20-2025
Year of Publication
2025
Document Type
Master's Thesis
Degree Name
Master of Science in Biosystems and Agricultural Engineering (MSBiosyAgE)
College
Agriculture; Engineering
Department/School/Program
Biosystems and Agricultural Engineering
Faculty
Dr. Jian Shi
Faculty
Dr. Michael Sama
Abstract
According to the U.S. Department of Energy’s 2023-billion-ton report, the United States generates nearly 300 million tons of municipal solid waste every year. More than 50% of that waste is underutilized and ends up in landfills. This poses serious public health and environmental risks. But beyond the environmental and health implications, this is also a wasted opportunity. The U.S. Energy Information Administration estimates that, for every 100 lbs of waste we throw away, about 85% can be used as fuel or for electricity generation. However, its complex and heterogeneous nature poses challenges for efficient sorting and valorization in waste to energy systems. In this project, we employed hyperspectral Near-Infrared (NIR) imaging, with various preprocessing techniques for the detection and classification of various MSW fractions (paper, cardboard and various waste plastics) collected from homes in Lexington, Kentucky. Simultaneously, comprehensive chemical and structural analyses, including ultimate and elemental analysis, Fourier Transform Infrared (FTIR) spectroscopy, and Scanning Electron Microscopy (SEM), were conducted. This study successfully developed and evaluated machine learning models, including Support Vector Machines (SVM), Random Forest (RF), k-Nearest Neighbors (KNN), and a 1D Convolutional Neural Network (1D-CNN), for automated classification based on the hyperspectral data. The results showed accuracies above 90% across all models. The classification was also performed on selected wavelengths of 3, 5, 10, and 20 for both Recursive feature elimination and sequential forward selection. Overall, the results indicate that NIR hyperspectral imaging could be used to classify different waste materials rapidly.
Digital Object Identifier (DOI)
https://doi.org/10.13023/etd.2025.434
Funding Information
This work was funded by the United States Department of Energy’s Bioenergy Technologies Office (Award Number: DE-EE0010295)
Recommended Citation
Ahadzi, Enyonam, "CHARACTERIZATION OF MUNICIPAL SOLID WASTE USING MACHINE LEARNING BASED IMAGING AND CHEMICAL ANALYSIS" (2025). Theses and Dissertations--Biosystems and Agricultural Engineering. 123.
https://uknowledge.uky.edu/bae_etds/123
