Cyclone Performance for the Separation of Solids from Supercritical Water Oxidation Effluents

Texas Advanced Research Program Grant No. 003658-212Center for Water and the Environmen

Resilience Analysis Framework for a Water–Energy–Food Nexus System Under Climate Change

Climate change impacts the water–energy–food security; given the complexities of interlinkages in the nexus system, these effects may become exacerbated when feedback loops magnify detrimental effects and create vicious cycles. Resilience is understood as the system’s adaptive ability to maintain its functionality even when the system is being affected by a disturbance or shock; in WEF nexus systems, climate change impacts are considered disturbances/shocks and may affect the system in different ways, depending on its resilience. Future global challenges will severely affect all vital resources and threaten environmental resilience. In this article, we present a resilience analysis framework for a water–energy–food nexus system under climate change, and we identify how such systems can become more resilient with the implementation of policies. We showcase results in the national case study of Greece. Parametric sensitivity analysis for socioecological systems is performed to identify which parameter the model is the most sensitive to. The case study is based on the structure of a system dynamics model that maps sector-specific data from major national and international databases while causal loop diagrams and stock-and-flow diagrams are presented. Through engineering and ecological resilience metrics, we quantify system resilience and identify which policy renders the system more resilient in terms of how much perturbation it can absorb and how fast it bounces back to its original state, if at all. Two policies are tested, and the framework is implemented to identify which policy is the most beneficial for the system in terms of resilience. Copyright © 2022 Ioannou and Laspidou

The Water-Energy Nexus at City Level: The Case Study of Skiathos

Water and energy are two inextricably linked resources of great importance, as they are the key for satisfying basic human needs. In this study a water–energy nexus analysis is conducted in order to achieve a sustainable supply and effectively manage water and energy at city level. Different electricity uses such as domestic, agricultural and commercial are compared and tested on how they correlate with water use. Moreover, time series of water and energy consumption for the island of Skiathos are analyzed using specific distance metrics. The results of the analysis show that water and energy are intimately related

Simulation of a water distribution network with key performance indicators for spatio-temporal analysis and operation of highly stressedwater infrastructure

An annual and lumped water balance assessment of a water distribution network is recommended by the InternationalWater Association as a first step and prerequisite for improving the performance of the network by minimizing real/physical water losses, burst incidents, water theft, nonrevenue water, and energy consumption, among others. The current work suggests a modeling approach for developing the water balance of a network spatio-temporarily, in hour time-scale and neighborhood granularity. It exploits already established key performance indicators and introduces some new ones to highlight the potential in improving the management of a water distribution network when having a detailed spatio-temporal supervision, especially when the spatial and temporal conditions are variable. The methodology is applied in a seasonally touristic and hilly case study. Additionally, a pressure management scheme is applied to further exploit the potential of such a toolkit. For the investigated case study, the town of Skiathos, the annual real losses are estimated equal to 50.9-52.2% of the system input volume, while apparent losses are estimated to be about 5.6-6.6%. Losses depict intense seasonal variability. Real losses range from 38.8-39.6% in summer months to 63.3-64.7% in winter months, while apparent losses range from 8.4-9.3% in summer to 1.3-2.5% in winter. Annual water theft is estimated to be at least 3.6% of system input volume. Spatial variability, which is linked to the elevation and the different urban land uses is proven to play a significant role in the neighborhoods' water balances and various key performance indicators are suggested and applied for the pressure control scheme. The annual potential savings due to the applied scheme rise up to 51,300 m3 for leakage and 53,730 m3 for pressure-driven demand. © 2020 by the authors

Erosion probability for biofilm modeling: analysis of trends

This study presents the strengths and weaknesses of a biofilm erosion probability algorithm that can be used in cellular automaton and individual-based biofilm simulation models. The erosion probability is calculated using data on localized biofilm mechanical properties, expressed through the composite biofilm Young's modulus-a measure of biofilm strength that varies in time and space-and on fluid hydrodynamic shear stress. Analysis of trends shows that biofilm detachment is the process that results from the competition between biofilm strength and hydrodynamic shear stress exerted on it by the fluid, with hydrodynamics being more important when biofilm strength is low and vice versa. From the modeling sample analyzed in this study, it is evident that for biofilms with cluster and mushroom formations, erosion probabilities are lower in the crevices formed between two clusters-where substrate is depleted-and higher at the top of the clusters where there is fresh biomass growth. When compared to other detachment methodologies extensively used by biofilm modeling researchers, such as the detachment speed that is a function of the square of the distance to the solid substratum, it is proved that the probability of erosion algorithm would give similar results

Exploring the effectiveness of clustering algorithms for capturing water consumption behavior at household level

As water scarcity becomes more prevalent, the analysis of urban water consumption patterns at the consumer level and the estimation of the corresponding water demand for water utility are expected to be among the top priorities of water companies in the near future. This study proposes a comprehensive methodology for water managers to achieve an efficient operation of urban water networks, by successfully detecting residential water consumption patterns corresponding to different household needs and behaviors. The methodology uses Self Organizing Maps as the main clustering algorithm in combination with K-means and Hierarchical Agglomerative Clustering. The objective is to create clusters in a literature dataset that includes water consumption from 21 customers located in Milford, Ohio, USA, for a 7-month period. Originally, water consumption data was recorded for every water use incident in the household, while for this analysis, the information is converted to half-hourly water consumption. Individual customers with similar consumption behavior are clustered and water-consumption curves are calculated for each cluster; these curves can be used by the water utility to obtain estimates of the spatio-temporal distribution of demand, thus giving insight into peak demands at different locations. Statistical indices of agreement are used to confirm a good agreement between the estimated and observed water use, when clustering is employed. The resulting curves show a clear improvement in capturing water consumption behavior at household level, when compared to corresponding curves obtained without clustering. This analysis offers water utilities an innovative solution that relies on real time data and uses data science principles for optimizing water supply and network operation and provides tools for the efficient use of water resources. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

Exploring the Effectiveness of Clustering Algorithms for Capturing Water Consumption Behavior at Household Level

As water scarcity becomes more prevalent, the analysis of urban water consumption patterns at the consumer level and the estimation of the corresponding water demand for water utility are expected to be among the top priorities of water companies in the near future. This study proposes a comprehensive methodology for water managers to achieve an efficient operation of urban water networks, by successfully detecting residential water consumption patterns corresponding to different household needs and behaviors. The methodology uses Self Organizing Maps as the main clustering algorithm in combination with K-means and Hierarchical Agglomerative Clustering. The objective is to create clusters in a literature dataset that includes water consumption from 21 customers located in Milford, Ohio, USA, for a 7-month period. Originally, water consumption data was recorded for every water use incident in the household, while for this analysis, the information is converted to half-hourly water consumption. Individual customers with similar consumption behavior are clustered and water-consumption curves are calculated for each cluster; these curves can be used by the water utility to obtain estimates of the spatio-temporal distribution of demand, thus giving insight into peak demands at different locations. Statistical indices of agreement are used to confirm a good agreement between the estimated and observed water use, when clustering is employed. The resulting curves show a clear improvement in capturing water consumption behavior at household level, when compared to corresponding curves obtained without clustering. This analysis offers water utilities an innovative solution that relies on real time data and uses data science principles for optimizing water supply and network operation and provides tools for the efficient use of water resources

A methodology for synthetic household water consumption data generation

In the smart cities context, real-time knowledge of residential water consumption has become increasingly important, especially given the fast evolution of sensors, ICT and the production of big, high-resolution data coming from the urban environment. A variety of reasons often leads to the creation of continuity gaps in these data series, thus making the need for a methodology that produces reliable and realistic synthetic data urgent. In this article, we present a methodology that generates synthetic household water consumption data; we showcase it in two case studies, Skiathos, Greece and Sosnowiec, Poland, which exhibit significant differences in water consumption patterns. The methodology captures the stochasticity of daily residential water use. Algorithm validation is implemented through the comparison of various metrics for actual and generated data; this way, we show that the suggested approach is capable of adequately simulating water consumption in both micro- and macro-time scale. © 2017 Elsevier Lt

Towards ranking the water-energy-food-land use-climate nexus interlinkages for building a nexus conceptual model with a Heuristic Algorithm

The concept of the Water-Energy-Food nexus (WEF), as documented by the United Nations Food and Agriculture Organization (FAO), suggests that the three resources are thoroughly interrelated, shaping a complicated web of interlinkages. Perceiving the three commodities as an interdependent variable system, rather than isolated subsystems is a step towards a more holistic approach, and thus a prerequisite to introducing a sustainable scheme for better managing resources. In this work, the well-documented WEF nexus is broadened to a five-dimensional nexus, also involving land use and climate. A methodology for drawing the interrelations among the five dimensions and unreeling the complicated system of direct and indirect interlinkages is given. The intensity of interlinkages among nexus components is initially assessed through a three-point typology with interlinkage scoring corresponding to resource use in Greece. The typology is used and is further expanded to quantify successfully all interlinkages among nexus components with a proposed heuristic algorithm. Results are used to create the cross-interlinkage matrix that identifies food as the most influencing resource and water as the resource mostly influenced by other nexus elements. Results show that indirect interlinkages of multiple resources can be very significant and should not be ignored when planning nexus-coherent policy initiatives and investments in different sectors, in order to promote resource efficiency. © 2019 by the authors

Geochemical modeling of mercury in coastal groundwater

The systematic analysis of groundwater in the Greek island of Skiathos revealed a seasonal increase of total mercury concentrations after the extensive groundwater abstraction during the busy and heavily touristic summer months. This contamination was accompanied by a corresponding increase of the chloride content of groundwater, attributed to seawater intrusion into the freshwater-depleted aquifer within mercury-rich bedrock. The effects of elevated concentrations of chloride anions in the mobilization of mercury and its speciation were addressed by geochemical equilibrium modeling, considering cinnabar (HgS) as the mineral source of mercury. Adsorption onto hydrous ferric oxide (Fe2O3·H2O) was a necessary ingredient of the geochemical model for bringing the calculated concentrations in agreement with field measurements, after optimization of the cinnabar/adsorbent mass ratio to a value of 4.9 × 10−8. The speciation of mercury was found to depend on the acidity and redox status as well as on the chloride content of groundwater. Mercury concentrations in the groundwater of Skiathos rise above the World Health Organization limit of 1 μg L−1 for a seawater intrusion higher than 3 %, with HgCl2 being the dominant species followed by HgClOH, HgCl3− and HgCl42−. The assumed concentration of dissolved organic matter in groundwater had a negligible impact on the mercury speciation and its mobilization by chloride. © 2021 The Author