Machine learning applied for contaminant detection in natural surface water
department or school: Applied Science, Faculty of
department or school: Engineering, School of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Rincón Remolina, María Claudia
issued: 2023-02-15T22:51:03Z
issued: 2023
related degree: Master of Applied Science - MASc
degree grantor: University of British Columbia
description: Approximately 1.4 billion m³ of fluid tailings produced from oil sands mining operations are currently being held in Alberta, Canada and pose a significant risk to the environment if not properly treated and managed. The ability to quantify levels of toxic compounds present in oil sands tailings, such as naphthenic acids (NAs), phenol, and polycyclic aromatic hydrocarbons (PAHs), is required to ensure the protection of the surrounding aquatic environment. In this thesis, machine learning techniques are applied to rapidly detect toxic contaminants from the production of petroleum products and crude oil extraction in natural surface waters. The first study implemented modern neural network structures for improving supervised regression of NA and phenol concentrations in water. Multilayer perceptron (MLP) and convolutional neural networks (CNN) were both capable of identifying relevant spectral features of the pollutants and were accurate in estimating concentrations in the presence of background interference from organic matter in natural waters. NAs were relatively easy to detect by MLP and CNN, however, deep CNNs resulted in optimized performance for phenol with mean absolute errors (MAE) of 1.78 – 1.81 mg/L and 4.68 - 5.41 µg/L for NAs and phenol, respectively. Visualization of spectral areas of importance revealed that both MLPs and deep CNNs utilized logical regions of the fluorescence spectra associated with NAs and phenol signals. The second study aimed to improve available methods for in situ quantification of NA, phenol, pyrene, and fluoranthene concentrations in natural waters. An augmentation network that fuses data from field and lab-based optical measures was used to improve the quality of field measurements. NAs, fluoranthene and pyrene were easy to quantify by the augmentation network, however, performance for phenol was more challenging due to the low intensity of the signal and overlap with other more dominant signals from NAs. Detection and quantific…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/83827
note: MACHINE LEARNING APPLIED FOR CONTAMINANT DETECTION IN NATURAL SURFACE WATER by María Claudia Rincón Remolina B.Sc., Universidad Industrial de Santander, 2020 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Civil Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) February 2023 © María Rincón, 2023 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis entitled: Machine learning applied for contaminant detection in natural surface water submitted by Maria Claudia Rincon Remolina in partial fulfillment of the requirements of the degree of Master of Applied Science. Dr. Nicolas Peleato, School of Engineering Supervisor Dr. Anas Chaaban, School of Engineering Supervisory Committee Member Dr. Alyse Kiesser, School of Engineering Supervisory Committee Member Dr. Liwei Wang, School of Engineering University Examiner iii Abstract Approximately 1.4 billion m3 of fluid tailings produced from oil sands mining operations are currently being held in Alberta, Canada and pose a significant risk to the environment if not properly treated and managed. The ability to quantify levels of toxic compounds present in oil sands tailings, such as naphthenic acids (NAs), phenol, and polycyclic aromatic hydrocarbons (PAHs), is required to ensure the protection of the surrounding aquatic environment. In this thesis, machine learning techniques are applied to rapidly detect toxic contaminants from the production of petroleum products and crude oil extraction in natural surface waters. The first study implemented modern neural network structures for improving supervised regression of NA and phenol concentrations in water. Multilayer perceptron (MLP) and convolutional neural networks (CNN) were both capable of identifying relevant spectral features of the pollutants and were accurate in estimating concentrations in the pr…
has type: Thesis/Dissertation
date issued: 2021-05
is shown at: 10.14288/1.0425266
language: eng
degree discipline: Civil Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Peleato, Nicolas M.
type: Text
identifier URI: http://hdl.handle.net/2429/83827