Monitoring and modelling of membrane bioreactors for wastewater treatment incorporating 2D fluorescence spectroscopy

Dissertação para obtenção do Grau de Doutor em Engenharia Química e Bioquímica, Especialidade em Engenharia BioquímicaOs biorreactores de membranas (MBRs, ‘membrane bioreactors’) para o tratamento de águas residuais combinam o processo de lamas activadas com um passo de filtração para obtenção de um efluente limpo, livre de sólidos. Os MBRs representam uma tecnologia em expansão no tratamento de águas residuais sobretudo devido ao reduzido espaço que requerem e à elevada qualidade do efluente obtido. No entanto, a colmatação das membranas pode reduzir o desempenho do MBR. Por este motivo, no presente trabalho, pretendeu-se estudar a monitorização dos MBRs, com o objectivo de minimizar o número de parâmetros de monitorização necessários para descrever o desempenho do processo e obter uma monitorização em tempo real com recurso mínimo a técnicas laboratoriais demoradas. Para este fim, estudou-se a aplicabilidade da fluorescência bidimensional em meios biológicos complexos, tais como as lamas activadas utilizadas para o tratamento de águas residuais. A fluorescência bidimensional mostrou ser uma técnica abrangente, capaz de recolher informação relevante sobre o estado do sistema em tempo real. Devido à complexidade da informação contida nos espectros de fluorescência, usaram-se técnicas de estatística multivariada, tais como análise de componentes principais e projecção de estruturas latentes (PLS, ‘projection to latent structures’), para extrair a informação dos espectros e correlacioná-la com parâmetros de operação e de desempenho do MBR. O uso de modelos estatísticos permitiu a previsão de parâmetros chave para o desempenho do MBR usando somente dados de processo impostos ou facilmente adquiríveis em tempo real. Adicionalmente, a modelação estatística foi combinada com um modelo mecanístico, numa estrutura híbrida, de forma a melhorar a previsão mecanística. Este estudo demonstrou ser possível usar modelos PLS para incorporar dados de fluorescência obtidos em tempo real, de modo a melhorar a previsão mecanística sem requerer análises laboratoriais adicionais

Enhancing river health monitoring: Developing a reliable predictive model and mitigation plan

The escalating environmental harm inflicted upon rivers is an unavoidable outcome resulting from climatefluctuations and anthropogenic activities, leading to a catastrophic impact on water quality and thousands ofindividuals succumb to waterborne diseases. Consequently, the water quality monitoring stations have beenestablished worldwide. Regrettably, the real-time evaluation of Water Quality Index (WQI) is hindered by theintricate nature of off-site water quality parameters. Thus, there is a pressing need to create a precise and robustwater quality prediction model. The dynamic and non-linear characteristics of water quality parameters posesignificant challenges for conventional machine learning algorithms like multi-linear regression, as they struggleto capture these complexities. In this particular investigation, machine learning model called FeedforwardArtificial Neural Networks (FANNs) was employed to develop WQI prediction model of Batu Pahat River,Malaysia exclusively utilizing on-site parameters. The proposed method involves a consideration of whether toinclude or exclude parameters such as BOD and COD, which are not measured in real time and can be costly tomonitor as model inputs. Validation accuracy values of 99.53%, 97.99%, and 91.03% were achieved in threedifferent scenarios: the first scenario utilized the full input, the second scenario excluded BOD, and the thirdscenario excluded both BOD and COD. It was suggested that the model has better predictive power between inputvariables and output variables. Factor contributed to river pollution has been identified and mitigation plan forBatu Pahat river pollution has been proposed. This could provide an effective alternative to compute thepollution, better manage water resources and mitigate negative impacts of climate change of river ecosystems

Biomass Density Based Adjustment of LiDAR-derived Digital Elevation Models: A Machine Learning Approach

Salt marshes are valued for providing protective and non-protective ecosystem services. Accurate digital elevation models (DEMs) in salt marshes are crucial for modeling storm surges and determining the initial DEM elevations for modelling marsh evolution. Due to high biomass density, lidar DEMs in coastal wetlands are seldom reliable. In an aim to reduce lidar-derived DEM error, several multilinear regression and random forest models were developed and tested to estimate biomass density in the salt marshes near Saint Marks Lighthouse in Crawfordville, Florida. Between summer of 2017 and spring of 2018, two field trips were conducted to acquire true elevation and biomass density measures. Lidar point cloud data were combined with vegetation monitoring imagery acquired from Sentinel-2 and Landsat Thematic Mapper (LTM) satellites, and 64 field biomass density samples were used as target variables for developing the models. Biomass density classes were assigned to each biomass sample using a quartile approach. Moreover, 346 in-situ elevation measures were used to calculate the lidar DEM errors. The best model was then used to estimate biomass densities at all 346 locations. Finally, an adjusted DEM was produced by deducting the quartile-based adjustment values from the original lidar DEM. A random forest regression model achieved the highest pseudo R2 value of 0.94 for predicting biomass density in g/m2. The adjusted DEM based on the estimated biomass densities reduced the root mean squared error of the original DEM from 0.38 m to 0.18 m while decreasing the raw mean error from 0.33 m to 0.14 m, improving both measures by 54% and 58%, respectively

Hydrochars as Emerging Biofuels: Recent Advances and Application of Artificial Neural Networks for the Prediction of Heating Values

In this study, the growing scientific field of alternative biofuels was examined, with respect to hydrochars produced from renewable biomasses. Hydrochars are the solid products of hydrothermal carbonization (HTC) and their properties depend on the initial biomass and the temperature and duration of treatment. The basic (Scopus) and advanced (Citespace) analysis of literature showed that this is a dynamic research area, with several sub-fields of intense activity. The focus of researchers on sewage sludge and food waste as hydrochar precursors was highlighted and reviewed. It was established that hydrochars have improved behavior as fuels compared to these feedstocks. Food waste can be particularly useful in co-hydrothermal carbonization with ash-rich materials. In the case of sewage sludge, simultaneous P recovery from the HTC wastewater may add more value to the process. For both feedstocks, results from large-scale HTC are practically non-existent. Following the review, related data from the years 2014–2020 were retrieved and fitted into four different artificial neural networks (ANNs). Based on the elemental content, HTC temperature and time (as inputs), the higher heating values (HHVs) and yields (as outputs) could be successfully predicted, regardless of original biomass used for hydrochar production. ANN3 (based on C, O, H content, and HTC temperature) showed the optimum HHV predicting performance (R2 0.917, root mean square error 1.124), however, hydrochars’ HHVs could also be satisfactorily predicted by the C content alone (ANN1, R2 0.897, root mean square error 1.289)

Evaluation of Greywater and A/C Condensate for Water Reuse: An Approach using Artificial Neural Network Modeling

Alternative resources play a vital role for water-sensitive infrastructures where consistent water supply is a challenge, and freshwater resources are limited. Greywater and A/C condensate are potentially new alternatives for increasing urban water supply. An advanced physical filtration system for greywater treatment was developed named as GAC-MI-ME. It is comprised coarse filtration (CR-F) followed by microfiltration (MF), granular activated carbon (GAC), ultrafiltration (UF), ultraviolet (UV), and reverse-osmosis (RO). GAC-MI-ME effluent-quality was analyzed for greywater from laundry, shower, and wash basin. High-grade effluent equivalent to unrestricted water reuse was observed at UF and RO units. A subsequent tool (GREY-ANN) was proposed for GAC-MI-ME effluent quality predictions. Artificial Neural Network (ANN) was applied to develop 5 unit models for selected parameters including Biochemical Oxidation Demand, pH, Total Dissolved Solids, Turbidity, and Oxidation-Reduction Potential to predict effluent quality at each stage of GAC-ME-MI treatment using water quality databases (developed from a series of experiments testing greywater of varying strength). The 15 days storage potential of GAC-MI-ME treated effluents were also analyzed and showed no significant quality depletion in UF and RO effluent quality. A hybrid modeling approach was applied to A/C condensate estimation, which included a psychrometric based “Air-Conditioner-Condensate” (ACON) model, and data-driven “Internal Load Analysis using Neural Network” (ILAN) model. The ACON model uses mass and energy balance approach for HVAC systems operating under steady state conditions. It accounts for psychometric states of different air parcels during the cooling and dehumidification process. The ILAN model was developed using ANN for the city of Doha to predict Internal Load at a daily time step for variable climatic conditions (temperature, relative humidity). The ACON- ILAN models were validated for a test building and applied for yearly condensate estimation for Doha. The virtual simulations of the hybrid model showed an annual condensate volume of 1370 and 3700 l/100 m^3 of cooling space for 20% and 100% outdoor-ventilation. The condensate quality (for limited water quality parameters) showed values within primary and secondary drinking water standards, except for copper, which had marginally higher concentrations. Overall, the GREY-ANN and ACON-ILAN may improve greywater and A/C condensate reuse potentials

Application of data mining techniques to predict the performance of matured Vertical Flow Constructed Wetlands Systems treating urban wastewater

The rapid urbanisation and industrialisation, due to technological advancement, led to severe environmental pollution. The environmental pollution in the last few decades resulted in an adverse impact on the environment causing massive accumulation of wastewater. Wastewater is one of the closest sources of environmental problems, at the same time water scarcity is becoming alarming due to its high demand as the global population is increasing. Hence, the application for managing available water resources becomes crucial. The ever-increasing demand for water brings the need for wastewater treatment as an alternative source of water. Constructed Wetlands (CW) have gained broader research attention due to their environmental and safety benefits for wastewater treatment. In this study, over three years of monitoring performance data from 03rd December 2014 to 28th March 2018 (thirty-nine months) of the vertical flow vertical wetlands system, receiving and treating domestic wastewater, were collected and utilised to assess and investigate the treatment performance efficiency of the Vertical Flow Constructed Wetland Systems (VFCWs) for removing pollutants from wastewater. Different laboratory-scale vertical-flow constructed wetlands filters filled with gravel and planted with common reed were built to remove removal from wastewater. The overall evaluation of the system treatment performance was calculated using percentage removal efficiency. The results were recorded it was observed that all vertical flow constructed wetland filters had recorded high removal performance for the water quality parameters, irrespective of filter set-up and operation. The system was discovered to be very useful in pollutants removal (water quality parameters) with significant efficiency. However, the high cost of analysis laboratory tests, time-consuming parameters couple with uncertainties associated with an analysis of water quality variables, lead to the development of two data mining technique models Multiple Linear Regressions (MLR) and Multilayer Perceptron (MLP). To predict the wastewater treatment performance of CW by predicting selected output water quality parameters these include Chemical Oxygen Demand (COD), Biological Oxygen Demand (BOD), orthophosphate phosphorous (PO4-P), ammonium nitrogen (NH4-N) and suspended solids (SS) with respect to other known input parameters that will provide comfortable, reliable and cost-effective methods. Correlation analysis was conducted to select the most highly correlated input parameters to be used for the model development (prediction of output parameter). The monitoring dataset of all the parameters used was divided into training dataset to build prediction models (MLR and MLP) and testing dataset to validate the models constructed. In this current work, 70% of the whole data was used as a training dataset while the remaining 30% of the data set was used as a testing dataset. The prediction models built were evaluated and compared using two model evaluation criteria: graphical model evaluation (scatter plot and hydrograph) and numerical model error evaluation criteria using five model evaluation criteria, these include: Root Mean Square Error (RMSE), regression coefficient (r), Relative Absolute Error (RAE), mean absolute error (MAE) and root relative squared error (RRSE). The results obtained indicated that the predicted values of output parameters were in good agreement and relationship with their respective measured parameters. Thus, this showed that the two models built yielded satisfactory predictions and both models had performed reasonably well in predicting output variables concentrations accurately given the value of input dependent variable. Furthermore, the comparison between the model's outcomes showed that MLP model prediction performance was discovered to be better than the MLR model in a majority of water quality parameters. Both models built could be effectively used as a tool for predicting removal of water quality parameters efficiency of vertical flow constructed wetlands treating domestic wastewater and in predicting constructed wetland performance in wastewater treatment process in term of pollutants removal. The results demonstrated the potentiality of vertical flow constructed wetlands to treat domestic wastewater and remove pollutants for future reuse

IMPACTS OF URBAN DEVELOPMENT PATTERN ON RUNOFF PEAK FLOWS AND STREAMFLOW FLASHINESS OF PERI-URBAN CATCHMENTS: ASSESSING THE PERFORMANCE OF PHYSICAL AND DATA-DRIVEN MODELS FOR REAL-TIME ENSEMBLE FLOOD FORECASTING

Urban growth is a global phenomenon, and the associated impacts on hydrology from land development are expected to increase, especially in peri-urban catchments, which are newly developing catchments in proximity of growing cities. In northern climates, hydrologic response of peri-urban catchments change with the water budget and climatic conditions. As a result, runoff response of northern peri-urban catchments can vary immensely across seasons. During warm seasons, the evapotranspiration (ET) and infiltration rates are high, so urban floods are expected to occur during high intensity, low duration storm events. During cold seasons and below freezing temperatures, surficial soils are typically frozen and nearly impervious. In addition, the ET rate is low throughout winter. Therefore, the difference in runoff response between peri-urban and natural catchments is least in winter. Furthermore, winter snow redistribution by plowing and endogenous urban heat affect the snowmelt timing and frequency. Due to the limited availability of data on snow removal and redistribution activities in northern peri-urban catchments, cold-season hydrologic modeling for peri-urban catchments remains a challenging task in urban hydrology. Research on the cold season hydrologic response of peri-urban catchments are mostly limited to Finland, Sweden, and Canada. The resulting research gap on seasonal change in hydrologic response of peri-urban catchments is common to many northern settings. In the first phase of this study, I use intensive discharge monitoring records at several peri-urban catchments near Syracuse, NY to calculate and compare seasonal runoff peak flows among several peri-urban catchments. These are selected to provide a range of drainage area and imperviousness to clarify the impact of urban development and catchment size on seasonal hydrologic behavior of peri-urban catchments. It is well understood that greater peak flows and higher stream flashiness are associated with increased surface imperviousness and storm location. However, the effect of the distribution of impervious areas on runoff peak flow response and stream flashiness of peri-urban catchments has not been well studied. In the second phase of this dissertation, I define a new geometric index, Relative Nearness of Imperviousness to the Catchment Outlet (RNICO), to correlate imperviousness distribution of peri-urban catchments with runoff peak flows and stream flashiness. The study sites for this phase of the study include ninety peri-urban catchments in proximity of 9 large US cities: New York, NY (NYC), Syracuse, NY, Baltimore, MD, Portland, OR, Chicago, IL, Austin, TX, Houston, TX, San Francisco, CA, and Los Angeles, CA. Based on RNICO, all development patterns are divided into 3 classes: upstream, centralized, and downstream. Analysis results showed an obvious increase in runoff peak flows and decrease in time to peak as the centroid of imperviousness moves downstream. This indicates that RNICO is an effective tool for classifying urban development patterns and for macroscale understanding of the hydrologic behavior of small peri-urban catchments, despite the complexity of urban drainage systems. Results for nine cities show strong positive correlations between RNICO and runoff peak flows and stream flashiness index for small peri-urban catchments. However, the area threshold used to distinguish small and large catchments differs slightly by location. For example, for Chicago, IL, NYC, NY, Baltimore, MD, Houston, TX, and Austin, TX area threshold values of 55, 40, 50, 42, and 32 km2 emerged, runoff peak flows in catchments with drainage area below these values were positively correlated to RNCIO. This first phase of this study suggests that RNICO is a stronger predictor of runoff peak flow and stream-flow regime in humid northern and southern US study sites, compared to more arid western US study sites. This difference is likely due to the greater precipitation rates and greater antecedent soil moisture contents for humid climates. The extent of urban infrastructure is less likely to control the effectiveness of RNICO for predicting runoff peak flows and R-B flashiness index for the selected study sites, due to the relatively similar urban development level within the peri-urban study catchments. Consistent forecast of peak flows across scales in flood hydrographs remains a challenge for most hydrologic models. Urbanization increases the magnitude and frequency of peak flows, often challenging the forecast ability for real-time flood prediction. Following advances in satellite and ground-based meteorological observations, global and continental real-time ensemble flood forecasting systems use a variety of physical hydrology models to predict urban peak flows. Artificial intelligence (AI) models provide an alternative approach to physical hydrology models for real-time flood forecasting. Despite recent advances in AI techniques for hydrologic prediction, ensemble stream-flow prediction by these methods has been limited. In addition, application of AI models for flood forecasting has been limited to large river basins, with very limited research on use of AI models for small peri-urban catchments. Flood forecasting in small urban catchments can be a critical task to urban safety due to the short time of concentration and quick precipitation runoff response. AI flood forecasting models typically apply upstream streamflow measurements to forecast downstream flood discharge. Therefore, the storm direction may change the flood travel time and time to peak, which challenges accurate flood forecasting. For example, if the storm direction is upstream through an AI model trained on the upstream gage data may fail to accurately predict peak flow magnitude and timing, at the outlet, this is due to the quicker runoff response of the downstream gage compared to the upstream station. There has been very limited focus on the impact of storm direction on peak flow response of urban catchments and available literature are limited to lab-scale prototypes and rainfall simulators. These may not fully represent real-world flooding scenarios. Therefore, the impact of storm direction on flood forecasting performance of peri-urban catchments is another important research gap in real-time urban flood forecasting. In the third phase of my dissertation project, I initially assess the impact of storm direction on the flood forecasting performance of an Adaptive Neuro Fuzzy Inference System (ANFIS) at a peri-urban catchment in proximity of Syracuse, NY. Next, I compare the relative utility of physical hydrology and AI approaches to predict flood hydrograph in peri-urban catchments. For this comparison, I selected ANFIS, and Sacramento Soil Moisture Accounting Model (SAC-SMA) for real-time ensemble re-forecasting of streamflow in several small to medium size suburban catchments near NYC for Hurricane Irene and a smaller storm event. The SAC-SMA model is a physical hydrology model that was initially developed by Burnash et al. (1973). The National Oceanic and Atmospheric Administration (NOAA) selected the SAC-SMA lumped model as a comparison baseline for participating distributed hydrologic models in the Distributed Model Intercomparison Project (DMIP), which aimed to identify the most suitable model for National Weather Service (NWS) streamflow prediction across the US (http://www.nws.noaa.gov/ohd/hrl/dmip/). More importantly, the NWS is currently using the lumped form of SAC-SMA for ensemble flood forecasting across the US (Emerton et al., 2016). For these reasons, I chose to employ a lumped version of SAC-SMA in my dissertation project. SAC-SMA performed well for both large and small events and for lead times of three to 24 hours, but ANFIS predicted the Hurricane Irene flood discharge well only for short lead times in small study catchments. ANFIS had reasonable percent bias (PBIAS) for predicting the small storm event for all lead times, indicating the utility of ANFIS for small events. In addition, the accuracy of both SAC-SMA and ANFIS models for ensemble flood prediction did not change significantly with catchment size and imperviousness. Overall, results of the third phase of this study suggest that the lumped SAC-SMA model may be a reliable option for local urban flood forecasting for evacuation plan lead time up to 24 hours. Due to the uncertainties in future climatic conditions, my study emphasizes the importance of using physical hydrology models for real-time flood forecasting of large events in small urban catchments. This recommendation is based on the finding that the performance of data-driven models may greatly decrease with the storm scale if the training period includes storms of magnitude less than storms in the validation period

Environmental, health, and safety assessment of chemical alternatives during early process design: The role of predictive modeling and streamlined techniques

Industrial chemicals are important for many aspects of modern life, though they can be harmful to the environment and human health. Environmental or safety concerns identified during the early design and selection of chemicals could motivate choices as to safer alternatives and process setups. There is a growing interest in developing more rapid, and streamlined assessment methods to obtain a first indication of the potential impacts linked to the nature and use of industrial chemicals. This work applies predictive modeling and streamlined techniques to estimate the potential environmental, health, and safety hazards associated with specific chemical structures. The assessment is performed during the design and selection of promising candidates for a particular process as part of the computer-aided molecular design (CAMD) and process setup. The case of phase-change solvents used for post-combustion carbon capture is examined. Furthermore, the refinement of predictive models through the incorporation of knowledge already existing in the field (prior knowledge) is investigated. A procedure for knowledge extraction from scientific articles that applies text mining is proposed. The results show that incorporating impact assessment criteria into the CAMD facilitates the molecular design by enriching the Pareto front of candidates. The use of predictive models that estimate molecular properties, such as acute aquatic toxicity, bioconcentration, and persistency are found to support the identification of the optimal solvents for CO2 capture. Given the role of sustainability-related properties in tasks such as CAMD, the improved performance and the interpretability of the aquatic toxicity predictive models developed here and using prior knowledge are important. The process level assessment of the phase-change solvent systems indicated that phase-change solvent alternatives could provide benefits, not only in terms of reduced energy consumption but also lower impacts on human health and the environment. \ua0However, the degradation behaviors of these compounds should be properly assessed and controlled to ensure beneficial performances compared to conventional carbon capture solvents. Overall, predictive modeling and streamlined life-cycle assessments (LCAs), as well as environmental, health, and safety evaluation methods were revealed to be valuable for defining the critical aspects that influence the potential impacts of chemicals and in supporting decisions concerning the molecular and process designs