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, such as naphthenic acids (NAs) and phenol, accurately and rapidly in the produced oil sands process-affected water (OSPW) is required to ensure the protection of the surrounding aquatic environment. In this paper, fluorescence techniques are investigated to rapidly quantify NAs and phenol concentrations in natural surface waters. Machine learning approaches were applied to identify relevant spectral features to improve detection accuracy in the presence of background interference from organic matter in natural waters. NAs were relatively easy to detect by all methods, however deep convolutional neural networks (CNN) resulted in optimized performance for phenol with mean absolute errors of 1.78 - 1.81 mg/L and 4.68-5.41 µg/L, respectively. Visualization of spectral areas of importance revealed that deep CNNs utilized logical areas of the fluorescence spectra associated with NAs and phenol signals. Results suggest machine learning approaches to interpreting fluorescence data can accurately predict individual toxic components of OSPW in natural waters at environmentally relevant concentrations.
