Enhancing long-term water quality modeling by addressing base demand, demand patterns, and temperature uncertainty using unsupervised machine learning techniques.

Water quality modelling in Water Distribution systems (WDS) is frequently affected by uncertainties in input variables such as base demand and decay constants. When utilizing simulation tools like EPANET, which necessitate exact numerical inputs, these uncertainties can result in inaccurate simulations. This study proposes a novel framework that leverages unsupervised machine learning, specifically a Gaussian Mixture Model (GMMs), to represent and integrate these uncertainties in the simulation.

A comparison of reactor configuration using a fruit waste fed two-stage anaerobic up-flow leachate reactor microbial fuel cell and a single-stage microbial fuel cell.

Food waste generation and its consequent environmental impacts are increasing due to rapid urbanization, the global population, and associated food demand. Microbial fuel cells (MFCs) are a sustainable technology through which this food waste can be treated and used to produce bioelectricity. This study used two MFC configurations, a two-stage anaerobic up-flow leachate reactor MFC and a single-stage MFC, comparing the potential to treat solid fruit waste and fruit waste leachate.

Augmentation of field fluorescence measures for improved in situ contaminant detection.

This research proposes a new method that fuses data from the field and lab-based optical measures coupled with machine learning algorithms to quantify the concentrations of toxic contaminants found in fuels and oil sands process-affected water. Selected pairs of excitation/emission intensities at key wavelengths are inputs to an augmentation neural network (NN), trained using lab-based measurements, that generates synthetic high-resolution spectra. Then, an image processing NN is used to estimate the contaminant concentrations from the spectra generated from a few key wavelengths.

Application of machine learning methods for rapid fluorescence-based detection of naphthenic acids and phenol in natural surface waters.

Approximately 1.4 billion m 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.

Meta-analysis of operational performance and response metrics of microbial fuel cells (MFCs) fed with complex food waste.

This study reports on a meta-analysis covering the impact of design and operating factors on published MFC performance data to inform MFC research and implementations. Factors of substrate composition, operating phase, electrode material, configuration, and pre-treatments employed were considered. The meta-analysis results indicate that dual-chamber MFCs overall achieve 18% higher COD removal and 73% higher coulombic efficiencies over that of single-chamber MFCs.

Application of convolutional neural networks for prediction of disinfection by-products.

Fluorescence spectroscopy can provide high-level chemical characterization and quantification that is suitable for use in online process monitoring and control. However, the high-dimensionality of excitation-emission matrices and superposition of underlying signals is a major challenge to implementation. Herein the use of Convolutional Neural Networks (CNNs) is investigated to interpret fluorescence spectra and predict the formation of disinfection by-products during drinking water treatment.

Comparison of dimensionality reduction techniques for cross-source transfer of fluorescence contaminant detection models.

Fluorescence spectroscopy shows promise as a tool for monitoring water quality due to its real-time capabilities and sensitive detection of several compounds of interest. Previous work has shown the possible use of fluorescence to detect and quantify low levels of polycyclic aromatic hydrocarbons and fluorescing pesticides. However, the fluorescence-based contaminant detection models are highly source-specific and require significant effort and resources to build and calibrate them for each source water of interest.

Integration of weather conditions for predicting microbial water quality using Bayesian Belief Networks.

Levels of fecal indicator bacteria (FIB) provide a surrogate measure of the microbial quality of water used for a wide range of applications. Despite the common use of these measures, a significant limitation is a delay in results due to the time required for cultivation and enumeration of FIB. Testing requires at least 18-24 h, and therefore, FIB cannot be used to identify current or real-time microbial water quality.

Towards real-time detection of wastewater in surface waters using fluorescence spectroscopy.

The presence of municipal wastewater at the intake of a major drinking water treatment facility located on Lake Ontario was examined using fluorescence data collected during a period of continuous monitoring. In addition, controlled mixing of lake water and wastewater sampled from a local treatment facility were conducted using a bench-scale fluorescence system to quantify observed changes in natural organic matter. Multivariate linear regression was applied to components derived from parallel factors analysis.

Neural networks for dimensionality reduction of fluorescence spectra and prediction of drinking water disinfection by-products.

The use of fluorescence data coupled with neural networks for improved predictability of drinking water disinfection by-products (DBPs) was investigated. Novel application of autoencoders to process high-dimensional fluorescence data was related to common dimensionality reduction techniques of parallel factors analysis (PARAFAC) and principal component analysis (PCA). The proposed method was assessed based on component interpretability as well as for prediction of organic matter reactivity to formation of DBPs.

Characterization of UF foulants and fouling mechanisms when applying low in-line coagulant pre-treatment.

Fluorescence spectroscopy was used as a characterization method to examine organic fouling of single ultrafiltration (UF) fibres at bench-scale. Low doses of coagulant were applied to modify organic properties, without significant formation of precipitates. This approach compliments previous studies investigating coagulation as a pre-treatment method for UF fouling control, which have principally focused on reduction of foulant concentrations.

Investigation of ozone and peroxone impacts on natural organic matter character and biofiltration performance using fluorescence spectroscopy.

Impacts of ozonation alone as well as an advanced oxidation process of ozone plus hydrogen peroxide (HO + O) on organic matter prior to and following biofiltration were studied at pilot-scale. Three biofilters were operated in parallel to assess the effects of varying pre-treatment types and dosages. Conventionally treated water (coagulation/flocculation/sedimentation) was fed to one control biofilter, while the remaining two received water with varying applied doses of O or HO + O.

Investigation of fluorescence methods for rapid detection of municipal wastewater impact on drinking water sources.

Fluorescence spectroscopy as a means to detect low levels of treated wastewater impact on two source waters was investigated using effluents from five wastewater facilities. To identify how best to interpret the fluorescence excitation-emission matrices (EEMs) for detecting the presence of wastewater, several feature selection and classification methods were compared. An expert supervised regional integration approach was used based on previously identified features which distinguish biologically processed organic matter including protein-like fluorescence and the ratio of protein to humic-like fluorescence.

Fluorescence spectroscopy for monitoring reduction of natural organic matter and halogenated furanone precursors by biofiltration.

The application of fluorescence spectroscopy to monitor natural organic matter (NOM) reduction as a function of biofiltration performance was investigated. This study was conducted at pilot-scale where a conventional media filter was compared to six biofilters employing varying enhancement strategies. Overall reductions of NOM were identified by measuring dissolved organic carbon (DOC), and UV absorbance at 254 nm, as well as characterization of organic sub-fractions by liquid chromatography-organic carbon detection (LC-OCD) and parallel factors analysis (PARAFAC) of fluorescence excitation-emission matrices (FEEM).

Comparison of three-dimensional fluorescence analysis methods for predicting formation of trihalomethanes and haloacetic acids.

This work investigated the application of several fluorescence excitation-emission matrix analysis methods as natural organic matter (NOM) indicators for use in predicting the formation of trihalomethanes (THMs) and haloacetic acids (HAAs). Waters from four different sources (two rivers and two lakes) were subjected to jar testing followed by 24hr disinfection by-product formation tests using chlorine. NOM was quantified using three common measures: dissolved organic carbon, ultraviolet absorbance at 254 nm, and specific ultraviolet absorbance as well as by principal component analysis, peak picking, and parallel factor analysis of fluorescence spectra.