An Investigation into the treatment and modelling of Municipal Solid Waste Incineration (MSWI) Air Pollution Control (APC) residues

PhD Thesis


Khalid, M. 2019. An Investigation into the treatment and modelling of Municipal Solid Waste Incineration (MSWI) Air Pollution Control (APC) residues. PhD Thesis University of East London Architecture, Computing and Engineering https://doi.org/10.15123/uel.86wz7
AuthorsKhalid, M.
TypePhD Thesis
Abstract

Air Pollution Control (APC) residues from Municipal Solid Waste Incineration (MSWI) is considered a problematic hazardous waste, with no current viable reuse, within the UK. Therefore, it is often treated before being deposited into a landfill. This research explores a number of novel techniques to mitigate the hazardous properties of this waste by investigating thermal treatment and cold bonding. Thermal treatment was investigated to manufacture inert Light Weight Aggregate (LWA) by sintering APC residues with clay. The addition of 20% APC residue produced the highest fracture strength of 5.78MPa. Treatment through cold bonding was achieved using the geopolymerisation process. The developed material achieved a compressive strength of approximately 2.35 MPa. The data from the APC residues based geopolymer experimentation was used to develop a machine learning model to predict the compressive strength of the geopolymer.
In addition, it was observed through a comprehensive literature review, that complexities arising due to significant variations in the composition of the residue, makes it very difficult to produce a commercially stable product. Therefore, the research tackles this problem by developing an Artificial Neural Network (ANN) model to identify and classify different types of residues/ashes based on their chemical composition as determined by X-ray Fluorescence (XRF) spectroscopy. Overall this research showed that machine learning could be very beneficial to this field to determine the capabilities for various reuse applications for ash waste.

Year2019
PublisherUniversity of East London
Digital Object Identifier (DOI)https://doi.org/10.15123/uel.86wz7
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Anyone
Publication dates
OnlineJan 2019
Publication process dates
Deposited12 Jul 2019
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